@article {12454,
	title = {A Fast Bilinear Structure from Motion Algorithm Using a Video Sequence and Inertial Sensors},
	journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
	volume = {33},
	year = {2011},
	month = {2011/01//},
	pages = {186 - 193},
	abstract = {In this paper, we study the benefits of the availability of a specific form of additional information-the vertical direction (gravity) and the height of the camera, both of which can be conveniently measured using inertial sensors and a monocular video sequence for 3D urban modeling. We show that in the presence of this information, the SfM equations can be rewritten in a bilinear form. This allows us to derive a fast, robust, and scalable SfM algorithm for large scale applications. The SfM algorithm developed in this paper is experimentally demonstrated to have favorable properties compared to the sparse bundle adjustment algorithm. We provide experimental evidence indicating that the proposed algorithm converges in many cases to solutions with lower error than state-of-art implementations of bundle adjustment. We also demonstrate that for the case of large reconstruction problems, the proposed algorithm takes lesser time to reach its solution compared to bundle adjustment. We also present SfM results using our algorithm on the Google StreetView research data set.},
	keywords = {3D urban modeling, algorithms, Artificial intelligence, CAMERAS, computer vision., Convergence, fast bilinear structure, Google StreetView research data set, Image Interpretation, Computer-Assisted, Image reconstruction, Image sensors, Image sequences, Imaging, Three-Dimensional, inertial sensors, Information Storage and Retrieval, Linear systems, minimization, MOTION, motion algorithm, Motion estimation, multiple view geometry, Pattern Recognition, Automated, Sensors, SfM equations, sparse bundle adjustment algorithm, structure from motion, Three dimensional displays, vertical direction, Video Recording, video sequence, video signal processing},
	isbn = {0162-8828},
	doi = {10.1109/TPAMI.2010.163},
	author = {Ramachandran, M. and Veeraraghavan,A. and Chellapa, Rama}
}
@conference {12445,
	title = {Variable remapping of images from very different sources},
	booktitle = {2011 18th IEEE International Conference on Image Processing (ICIP)},
	year = {2011},
	month = {2011/09/11/14},
	pages = {1501 - 1504},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {We present a system which registers image sequences acquired by very different sources, so that multiple views could be transformed to the same coordinates system. This enables the functionality of automatic object identification and confirmation across views and platforms. The capability of the system comes from three ingredients: 1) image context enlargement through temporal integration; 2) robust motion estimation using the G-RANSAC framework with a relaxed correspondence criteria; 3) constrained motion estimation within the G-RANSAC framework. The proposed system has worked successfully on thousands of frames from multiple collections with significant variations in scale and resolution.},
	keywords = {automatic object identification, Buildings, CAMERAS, Conferences, constrained motion estimation, coordinates system, Estimation, G-RANSAC framework, image context enlargement, Image Enhancement, image registration, image sequence registration, Image sequences, Motion estimation, Robustness, temporal integration, variable image remapping},
	isbn = {978-1-4577-1304-0},
	doi = {10.1109/ICIP.2011.6115729},
	author = {Wei Zhang and Yanlin Guo and Meth, R. and Sokoloff, H. and Pope, A. and Strat, T. and Chellapa, Rama}
}
@conference {12480,
	title = {Tracking via object reflectance using a hyperspectral video camera},
	booktitle = {2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
	year = {2010},
	month = {2010/06/13/18},
	pages = {44 - 51},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Recent advances in electronics and sensor design have enabled the development of a hyperspectral video camera that can capture hyperspectral datacubes at near video rates. The sensor offers the potential for novel and robust methods for surveillance by combining methods from computer vision and hyperspectral image analysis. Here, we focus on the problem of tracking objects through challenging conditions, such as rapid illumination and pose changes, occlusions, and in the presence of confusers. A new framework that incorporates radiative transfer theory to estimate object reflectance and the mean shift algorithm to simultaneously track the object based on its reflectance spectra is proposed. The combination of spectral detection and motion prediction enables the tracker to be robust against abrupt motions, and facilitate fast convergence of the mean shift tracker. In addition, the system achieves good computational efficiency by using random projection to reduce spectral dimension. The tracker has been evaluated on real hyperspectral video data.},
	keywords = {Computer vision, electronic design, hyperspectral datacubes, hyperspectral image analysis, Hyperspectral imaging, Hyperspectral sensors, hyperspectral video camera, Image motion analysis, Image sensors, lighting, Motion estimation, motion prediction, Object detection, object reflectance tracking, random projection, Reflectivity, robust methods, Robustness, sensor design, spectral detection, Surveillance, tracking, Video surveillance},
	isbn = {978-1-4244-7029-7},
	doi = {10.1109/CVPRW.2010.5543780},
	author = {Nguyen,Hien Van and Banerjee, A. and Chellapa, Rama}
}
@conference {14207,
	title = {Combining motion from texture and lines for visual navigation},
	booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems, 2007. IROS 2007},
	year = {2007},
	month = {2007/11/29/Oct. },
	pages = {232 - 239},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Two novel methods for computing 3D structure information from video for a piecewise planar scene are presented. The first method is based on a new line constraint, which clearly separates the estimation of distance from the estimation of slant. The second method exploits the concepts of phase correlation to compute from the change of image frequencies of a textured plane, distance and slant information. The two different estimates together with structure estimates from classical image motion are combined and integrated over time using an extended Kalman filter. The estimation of the scene structure is demonstrated experimentally in a motion control algorithm that allows the robot to move along a corridor. We demonstrate the efficacy of each individual method and their combination and show that the method allows for visual navigation in textured as well as un-textured environments.},
	keywords = {3D structure information, CAMERAS, Computer vision, extended Kalman filter, Frequency, image frequencies, Image motion analysis, Image texture, Kalman filters, Layout, motion control, Motion estimation, Navigation, Optical computing, phase correlation, piecewise planar scene, Robustness, Simultaneous localization and mapping, Speech processing, textured plane, video signal processing, visual navigation},
	isbn = {978-1-4244-0912-9},
	doi = {10.1109/IROS.2007.4399568},
	author = {Bitsakos,K. and Li Yi and Ferm{\"u}ller, Cornelia}
}
@article {14182,
	title = {A 3D shape constraint on video},
	journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
	volume = {28},
	year = {2006},
	month = {2006/06//},
	pages = {1018 - 1023},
	abstract = {We propose to combine the information from multiple motion fields by enforcing a constraint on the surface normals (3D shape) of the scene in view. The fact that the shape vectors in the different views are related only by rotation can be formulated as a rank = 3 constraint. This constraint is implemented in an algorithm which solves 3D motion and structure estimation as a practical constrained minimization. Experiments demonstrate its usefulness as a tool in structure from motion providing very accurate estimates of 3D motion.},
	keywords = {3D motion estimation, algorithms, Artificial intelligence, CAMERAS, decoupling translation from rotation, Estimation error, Fluid flow measurement, Image Enhancement, Image Interpretation, Computer-Assisted, Image reconstruction, Imaging, Three-Dimensional, Information Storage and Retrieval, integration of motion fields, Layout, minimisation, Minimization methods, Motion estimation, multiple motion fields, parameter estimation, Pattern Recognition, Automated, Photography, practical constrained minimization, SHAPE, shape and rotation., shape vectors, stability, structure estimation, surface normals, Three-dimensional motion estimation, video 3D shape constraint, Video Recording, video signal processing},
	isbn = {0162-8828},
	doi = {10.1109/TPAMI.2006.109},
	author = {Hui Ji and Ferm{\"u}ller, Cornelia}
}
@conference {12014,
	title = {Integration of visual and inertial information for egomotion: a stochastic approach},
	booktitle = {Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006},
	year = {2006},
	month = {2006/05/15/19},
	pages = {2053 - 2059},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {We present a probabilistic framework for visual correspondence, inertial measurements and egomotion. First, we describe a simple method based on Gabor filters to produce correspondence probability distributions. Next, we generate a noise model for inertial measurements. Probability distributions over the motions are then computed directly from the correspondence distributions and the inertial measurements. We investigate combining the inertial and visual information for a single distribution over the motions. We find that with smaller amounts of correspondence information, fusion of the visual data with the inertial sensor results in much better egomotion estimation. This is essentially because inertial measurements decrease the "translation-rotation" ambiguity. However, when more correspondence information is used, this ambiguity is reduced to such a degree that the inertial measurements provide negligible improvement in accuracy. This suggests that inertial and visual information are more closely integrated in a compositional sense},
	keywords = {Computer vision, data mining, Distributed computing, egomotion estimation, Gabor filters, Gravity, inertial information, inertial sensor, Laboratories, Motion estimation, Noise measurement, Probability distribution, probability distributions, Rotation measurement, stochastic approach, Stochastic processes, visual information},
	isbn = {0-7803-9505-0},
	doi = {10.1109/ROBOT.2006.1642007},
	author = {Domke, J. and Aloimonos, J.}
}
@article {12618,
	title = {Robust ego-motion estimation and 3-D model refinement using surface parallax},
	journal = {Image Processing, IEEE Transactions on},
	volume = {15},
	year = {2006},
	month = {2006/05//},
	pages = {1215 - 1225},
	abstract = {We present an iterative algorithm for robustly estimating the ego-motion and refining and updating a coarse depth map using parametric surface parallax models and brightness derivatives extracted from an image pair. Given a coarse depth map acquired by a range-finder or extracted from a digital elevation map (DEM), ego-motion is estimated by combining a global ego-motion constraint and a local brightness constancy constraint. Using the estimated camera motion and the available depth estimate, motion of the three-dimensional (3-D) points is compensated. We utilize the fact that the resulting surface parallax field is an epipolar field, and knowing its direction from the previous motion estimates, estimate its magnitude and use it to refine the depth map estimate. The parallax magnitude is estimated using a constant parallax model (CPM) which assumes a smooth parallax field and a depth based parallax model (DBPM), which models the parallax magnitude using the given depth map. We obtain confidence measures for determining the accuracy of the estimated depth values which are used to remove regions with potentially incorrect depth estimates for robustly estimating ego-motion in subsequent iterations. Experimental results using both synthetic and real data (both indoor and outdoor sequences) illustrate the effectiveness of the proposed algorithm.},
	keywords = {3D model refinement, algorithms, Artificial intelligence, Automated;Subtraction Technique;, Computer-Assisted;Imaging, constant parallax model, depth based parallax model, digital elevation map, epipolar field, Image Enhancement, Image Interpretation, iterative algorithm, iterative methods, Motion estimation, robust ego-motion estimation, smooth parallax field, surface parallax, Three-Dimensional;Information Storage and Retrieval;Pattern Recognition},
	isbn = {1057-7149},
	doi = {10.1109/TIP.2005.864167},
	author = {Agrawal,A. and Chellapa, Rama}
}
@conference {15061,
	title = {A Time Saving Method for Human Detection in Wide Angle Camera Images},
	booktitle = {Machine Learning and Cybernetics, 2006 International Conference on},
	year = {2006},
	month = {2006/08//},
	pages = {4029 - 4034},
	abstract = {A time-saving method for human detection in an indoor environment in wide angle camera images is presented in this paper, without using time-consuming template operations such as edge detection and morphological operations. It excludes other objects in motion to detect humans including cases of the partial occlusion of humans, rotation of the head and variation of skin colors. This algorithm is applicable where the memory space is limited and the time requirements are strict, such as in an embedded system. The experimental results show that it took about 0.35 seconds to process the algorithm after the scene study time},
	keywords = {CAMERAS, Embedded system, human detection, image recognition, Image segmentation, indoor environment, Motion estimation, time-saving method, wide angle camera images},
	doi = {10.1109/ICMLC.2006.258804},
	author = {Zhuolin Jiang and Li,Shao-Fa and Gao,Dong-Fa}
}
@conference {11897,
	title = {Identifying and segmenting human-motion for mobile robot navigation using alignment errors},
	booktitle = {12th International Conference on Advanced Robotics, 2005. ICAR {\textquoteright}05. Proceedings},
	year = {2005},
	month = {2005/07//},
	pages = {398 - 403},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {This paper presents a new human-motion identification and segmentation algorithm, for mobile robot platforms. The algorithm is based on computing the alignment error between pairs of object images acquired from a moving platform. Pairs of images generating relatively small alignment errors are used to estimate the fundamental frequency of the object{\textquoteright}s motion. A decision criterion is then used to test the significance of the estimated frequency and to classify the object{\textquoteright}s motion. To verify the validity of the proposed approach, experimental results are shown on different classes of objects},
	keywords = {Computer errors, Educational institutions, Frequency estimation, human-motion identification, human-motion segmentation, HUMANS, Image motion analysis, Image segmentation, mobile robot navigation, Mobile robots, Motion estimation, Navigation, Object detection, robot vision, SHAPE},
	isbn = {0-7803-9178-0},
	doi = {10.1109/ICAR.2005.1507441},
	author = {Abd-Almageed, Wael and Burns,B. J and Davis, Larry S.}
}
@conference {14224,
	title = {Integration of motion fields through shape},
	booktitle = {IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005. CVPR 2005},
	volume = {2},
	year = {2005},
	month = {2005/06/20/25},
	pages = {663- 669 vol. 2 - 663- 669 vol. 2},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Structure from motion from single flow fields has been studied intensively, but the integration of information from multiple flow fields has not received much attention. Here we address this problem by enforcing constraints on the shape (surface normals) of the scene in view, as opposed to constraints on the structure (depth). The advantage of integrating shape is two-fold. First, we do not need to estimate feature correspondences over multiple frames, but we only need to match patches. Second, the shape vectors in the different views are related only by rotation. This constraint on shape can be combined easily with motion estimation, thus formulating motion and structure estimation from multiple views as a practical constrained minimization problem using a rank-3 constraint. Based on this constraint, we develop a 3D motion technique, which locates through color and motion segmentation, planar patches in the scene, matches patches over multiple frames, and estimates the motion between multiple frames and the shape of the selected scene patches using the image gradients. Experiments evaluate the accuracy of the 3D motion estimation and demonstrate the motion and shape estimation of the technique by super-resolving an image sequence.},
	keywords = {3D motion estimation, Automation, CAMERAS, computational geometry, Computer vision, constrained minimization problem, decoupling translation from rotation, Educational institutions, image colour analysis, image gradients, image resolution, Image segmentation, image sequence, Image sequences, integration of motion fields, Layout, minimisation, Motion estimation, motion field integration, motion segmentation, parameter estimation, planar patches, rank-3 constraint, scene patches, SHAPE, shape and rotation, shape estimation, structure estimation},
	isbn = {0-7695-2372-2},
	doi = {10.1109/CVPR.2005.190},
	author = {Ji,H. and Ferm{\"u}ller, Cornelia}
}
@article {14230,
	title = {Motion segmentation using occlusions},
	journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
	volume = {27},
	year = {2005},
	month = {2005/06//},
	pages = {988 - 992},
	abstract = {We examine the key role of occlusions in finding independently moving objects instantaneously in a video obtained by a moving camera with a restricted field of view. In this problem, the image motion is caused by the combined effect of camera motion (egomotion), structure (depth), and the independent motion of scene entities. For a camera with a restricted field of view undergoing a small motion between frames, there exists, in general, a set of 3D camera motions compatible with the observed flow field even if only a small amount of noise is present, leading to ambiguous 3D motion estimates. If separable sets of solutions exist, motion-based clustering can detect one category of moving objects. Even if a single inseparable set of solutions is found, we show that occlusion information can be used to find ordinal depth, which is critical in identifying a new class of moving objects. In order to find ordinal depth, occlusions must not only be known, but they must also be filled (grouped) with optical flow from neighboring regions. We present a novel algorithm for filling occlusions and deducing ordinal depth under general circumstances. Finally, we describe another category of moving objects which is detected using cardinal comparisons between structure from motion and structure estimates from another source (e.g., stereo).},
	keywords = {3D motion estimation, algorithms, Artificial intelligence, CAMERAS, Computer vision, Filling, hidden feature removal, Image Enhancement, Image Interpretation, Computer-Assisted, image motion, Image motion analysis, Image segmentation, Layout, MOTION, Motion detection, Motion estimation, motion segmentation, Movement, Object detection, occlusion, occlusions, optical flow, ordinal depth, Pattern Recognition, Automated, Photography, Reproducibility of results, segmentation, Semiconductor device modeling, Sensitivity and Specificity, video analysis., Video Recording},
	isbn = {0162-8828},
	doi = {10.1109/TPAMI.2005.123},
	author = {Ogale, A. S and Ferm{\"u}ller, Cornelia and Aloimonos, J.}
}
@article {12654,
	title = {Statistical bias in 3-D reconstruction from a monocular video},
	journal = {Image Processing, IEEE Transactions on},
	volume = {14},
	year = {2005},
	month = {2005/08//},
	pages = {1057 - 1062},
	abstract = {The present state-of-the-art in computing the error statistics in three-dimensional (3-D) reconstruction from video concentrates on estimating the error covariance. A different source of error which has not received much attention is the fact that the reconstruction estimates are often significantly statistically biased. In this paper, we derive a precise expression for the bias in the depth estimate, based on the continuous (differentiable) version of structure from motion (SfM). Many SfM algorithms, or certain portions of them, can be posed in a linear least-squares (LS) framework Ax=b. Examples include initialization procedures for bundle adjustment or algorithms that alternately estimate depth and camera motion. It is a well-known fact that the LS estimate is biased if the system matrix A is noisy. In SfM, the matrix A contains point correspondences, which are always difficult to obtain precisely; thus, it is expected that the structure and motion estimates in such a formulation of the problem would be biased. Existing results on the minimum achievable variance of the SfM estimator are extended by deriving a generalized Cramer-Rao lower bound. A detailed analysis of the effect of various camera motion parameters on the bias is presented. We conclude by presenting the effect of bias compensation on reconstructing 3-D face models from rendered images.},
	keywords = {3D face models, 3D video reconstruction, algorithms, artifacts, Artificial intelligence, Automated;Signal Processing, bias compensation, bundle adjustment, camera motion estimation, Computer simulation, Computer-Assisted;Imaging, Computer-Assisted;Subtraction Technique;Video Recording;, depth estimate, error covariance estimation, error statistics, generalized Cramer-Rao lower bound, Image Enhancement, Image Interpretation, Image reconstruction, initialization procedures, least squares approximations, linear least-squares framework, monocular video, motion compensation, Motion estimation, statistical bias, Statistical;Pattern Recognition, structure from motion algorithms, Three-Dimensional;Information Storage and Retrieval;Models, video signal processing},
	isbn = {1057-7149},
	doi = {10.1109/TIP.2005.849775},
	author = {Roy-Chowdhury, A.K. and Chellapa, Rama}
}
@article {14263,
	title = {The Argus eye: a new imaging system designed to facilitate robotic tasks of motion},
	journal = {IEEE Robotics \& Automation Magazine},
	volume = {11},
	year = {2004},
	month = {2004/12//},
	pages = {31 - 38},
	abstract = {This article describes an imaging system that has been designed to facilitate robotic tasks of motion. The system consists of a number of cameras in a network, arranged so that they sample different parts of the visual sphere. This geometric configuration has provable advantages compared to small field of view cameras for the estimation of the system{\textquoteright}s own motion and, consequently, the estimation of shape models from the individual cameras. The reason is, inherent ambiguities of confusion between translation and rotation disappear. Pairs of cameras may also be arranged in multiple stereo configurations, which provide additional advantages for segmentation. Algorithms for the calibration of the system and the three-dimensional (3-D) motion estimation are provided.},
	keywords = {Argus eye, Calibration, CAMERAS, computational geometry, Design automation, Eyes, image formation, imaging system, Information geometry, Layout, Motion estimation, multiple stereo configurations, panoramic robots, robot vision, Robot vision systems, robotic motion tasks, Robotics and automation, SHAPE, shape model estimation, system calibration},
	isbn = {1070-9932},
	doi = {10.1109/MRA.2004.1371606},
	author = {Baker, P. and Ogale, A. S and Ferm{\"u}ller, Cornelia}
}
@conference {14209,
	title = {Compound eye sensor for 3D ego motion estimation},
	booktitle = {2004 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2004. (IROS 2004). Proceedings},
	volume = {4},
	year = {2004},
	month = {2004/10/28/Sept.},
	pages = {3712- 3717 vol.4 - 3712- 3717 vol.4},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {We describe a compound eye vision sensor for 3D ego motion computation. Inspired by eyes of insects, we show that the compound eye sampling geometry is optimal for 3D camera motion estimation. This optimality allows us to estimate the 3D camera motion in a scene-independent and robust manner by utilizing linear equations. The mathematical model of the new sensor can be implemented in analog networks resulting in a compact computational sensor for instantaneous 3D ego motion measurements in full six degrees of freedom.},
	keywords = {3D camera motion estimation, CAMERAS, compound eye vision sensor, Computer vision, Equations, Eyes, Geometry, Image sensors, Insects, linear equations, Motion estimation, robot vision, Robustness, sampling geometry, Sampling methods, Sensor phenomena and characterization},
	isbn = {0-7803-8463-6},
	doi = {10.1109/IROS.2004.1389992},
	author = {Neumann, J. and Ferm{\"u}ller, Cornelia and Aloimonos, J. and Brajovic,V.}
}
@conference {12000,
	title = {Eye design in the plenoptic space of light rays},
	booktitle = {Ninth IEEE International Conference on Computer Vision, 2003. Proceedings},
	year = {2003},
	month = {2003/10/13/16},
	pages = {1160-1167 vol.2 - 1160-1167 vol.2},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Natural eye designs are optimized with regard to the tasks the eye-carrying organism has to perform for survival. This optimization has been performed by the process of natural evolution over many millions of years. Every eye captures a subset of the space of light rays. The information contained in this subset and the accuracy to which the eye can extract the necessary information determines an upper limit on how well an organism can perform a given task. In this work we propose a new methodology for camera design. By interpreting eyes as sample patterns in light ray space we can phrase the problem of eye design in a signal processing framework. This allows us to develop mathematical criteria for optimal eye design, which in turn enables us to build the best eye for a given task without the trial and error phase of natural evolution. The principle is evaluated on the task of 3D ego-motion estimation.},
	keywords = {3D ego-motion estimation, Assembly, B-splines, Camera design, CAMERAS, captured image, compound eyes, Computer vision, data mining, eye, eye-carrying organism, Eyes, filter optimization, image representation, image resolution, Information geometry, Laboratories, light field reconstruction, light gathering power, light rays, mixed spherical-Cartesian coordinate system, Motion estimation, natural evolution process, natural eye designs, natural image statistics, optical nanotechnology, Optical signal processing, optimal eye design mathematical criteria, Organisms, plenoptic image formation, plenoptic space, plenoptic video geometry, sampling operators, sensory ecology, Signal design, Signal processing, signal processing framework, signal processing tool, square-summable sequences, visual acuity},
	isbn = {0-7695-1950-4},
	doi = {10.1109/ICCV.2003.1238623},
	author = {Neumann, J. and Ferm{\"u}ller, Cornelia and Aloimonos, J.}
}
@conference {14235,
	title = {New eyes for robotics},
	booktitle = {2003 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2003. (IROS 2003). Proceedings},
	volume = {1},
	year = {2003},
	month = {2003/10/27/31},
	pages = {1018- 1023 vol.1 - 1018- 1023 vol.1},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {This paper describes an imaging system that has been designed to facilitate robotic tasks of motion. The system consists of a number of cameras in a network arranged so that they sample different parts of the visual sphere. This geometric configuration has provable advantages compared to small field of view cameras for the estimation of the system{\textquoteright}s own motion and consequently the estimation of shape models from the individual cameras. The reason is that inherent ambiguities of confusion between translation and rotation disappear. Pairs of cameras may also be arranged in multiple stereo configurations which provide additional advantages for segmentation. Algorithms for the calibration of the system and the 3D motion estimation are provided.},
	keywords = {3D motion estimation, Argus eye, array signal processing, Birds, Calibration, CAMERAS, Control systems, Eyes, geometric configuration, imaging, imaging system, Layout, Motion estimation, multiple stereo configurations, Robot kinematics, robot vision, Robot vision systems, ROBOTICS, Robotics and automation, SHAPE, shape models},
	isbn = {0-7803-7860-1},
	doi = {10.1109/IROS.2003.1250761},
	author = {Baker, P. and Ogale, A. S and Ferm{\"u}ller, Cornelia and Aloimonos, J.}
}
@conference {12055,
	title = {Polydioptric camera design and 3D motion estimation},
	booktitle = {2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings},
	volume = {2},
	year = {2003},
	month = {2003/06/18/20},
	pages = {II- 294-301 vol.2 - II- 294-301 vol.2},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Most cameras used in computer vision applications are still based on the pinhole principle inspired by our own eyes. It has been found though that this is not necessarily the optimal image formation principle for processing visual information using a machine. We describe how to find the optimal camera for 3D motion estimation by analyzing the structure of the space formed by the light rays passing through a volume of space. Every camera corresponds to a sampling pattern in light ray space, thus the question of camera design can be rephrased as finding the optimal sampling pattern with regard to a given task. This framework suggests that large field-of-view multi-perspective (polydioptric) cameras are the optimal image sensors for 3D motion estimation. We conclude by proposing design principles for polydioptric cameras and describe an algorithm for such a camera that estimates its 3D motion in a scene independent and robust manner.},
	keywords = {3D motion estimation, Algorithm design and analysis, Application software, CAMERAS, Computer vision, Eyes, field-of-view camera, Image motion analysis, image sampling, image sensor, Image sensors, Layout, light ray, Motion estimation, multiperspective camera, optimal camera, optimal image formation, optimal sampling pattern, pinhole principle, polydioptric camera design, ray space, scene independent estimation, space structure analysis, stereo image processing, visual information processing},
	isbn = {0-7695-1900-8},
	doi = {10.1109/CVPR.2003.1211483},
	author = {Neumann, J. and Ferm{\"u}ller, Cornelia and Aloimonos, J.}
}
@conference {14219,
	title = {Eyes from eyes: new cameras for structure from motion},
	booktitle = {Third Workshop on Omnidirectional Vision, 2002. Proceedings},
	year = {2002},
	month = {2002///},
	pages = {19 - 26},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {We investigate the relationship between camera design and the problem of recovering the motion and structure of a scene from video data. The visual information that could possibly be obtained is described by the plenoptic function. A camera can be viewed as a device that captures a subset of this function, that is, it measures some of the light rays in some part of the space. The information contained in the subset determines how difficult it is to solve subsequent interpretation processes. By examining the differential structure of the time varying plenoptic function we relate different known and new camera models to the spatiotemporal structure of the observed scene. This allows us to define a hierarchy of camera designs, where the order is determined by the stability and complexity of the computations necessary to estimate structure and motion. At the low end of this hierarchy is the standard planar pinhole camera for which the structure from motion problem is non-linear and ill-posed. At the high end is a new camera, which we call the full field of view polydioptric camera, for which the problem is linear and stable. In between are multiple-view cameras with large fields of view which we have built, as well as catadioptric panoramic sensors and other omni-directional cameras. We develop design suggestions for the polydioptric camera, and based upon this new design we propose a linear algorithm for ego-motion estimation, which in essence combines differential motion estimation with differential stereo.},
	keywords = {Algorithm design and analysis, Camera design, CAMERAS, Design automation, differential stereo, Educational institutions, ego-motion estimation, Eyes, Geometrical optics, IMAGE PROCESSING, Layout, Motion estimation, observed scene, Optical films, polydioptric camera, RETINA},
	isbn = {0-7695-1629-7},
	doi = {10.1109/OMNVIS.2002.1044486},
	author = {Neumann, J. and Ferm{\"u}ller, Cornelia and Aloimonos, J.}
}
@conference {11901,
	title = {A non-intrusive Kalman filter-based tracker for pursuit eye movement},
	booktitle = {American Control Conference, 2002. Proceedings of the 2002},
	volume = {2},
	year = {2002},
	month = {2002///},
	pages = {1443- 1447 vol.2 - 1443- 1447 vol.2},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {In this paper, we introduce a new non-intrusive approach to estimating the eye position during pursuit motion of the eye. We introduce a new characterization for the pursuit eye movement. Our characterization is based on the decomposition of the pursuit eye motion into a deterministic component and random component. We use a discrete Kalman filter to estimate the random component and calculate the deterministic component. We add the two components to obtain an estimate of the eye position. Simulation results are provided to illustrate the eye position estimation.},
	keywords = {Application software, characterization, Computer vision, Current measurement, deterministic component, Electric variables measurement, eye position estimation, eye tracking, gaze tracking, Human computer interaction, Kalman filter, Kalman filters, Lenses, Motion estimation, Optical reflection, pursuit eye movement, pursuit motion, random component, Skin, tracking},
	isbn = {0-7803-7298-0},
	doi = {10.1109/ACC.2002.1023224},
	url = {http://ieeexplore.ieee.org/ielx5/7965/22015/01023224.pdf?tp=\&arnumber=1023224\&isnumber=22015},
	author = {Abd-Almageed, Wael and Fadali,M. S and Bebis,G.}
}
@article {12060,
	title = {Visual space-time geometry - A tool for perception and the imagination},
	journal = {Proceedings of the IEEE},
	volume = {90},
	year = {2002},
	month = {2002/07//},
	pages = {1113 - 1135},
	abstract = {Although the fundamental ideas underlying research efforts in the field of computer vision have not radically changed in the past two decades, there has been a transformation in the way work in this field is conducted. This is primarily due to the emergence of a number of tools, of both a practical and a theoretical nature. One such tool, celebrated throughout the nineties, is the geometry of visual space-time. It is known under a variety of headings, such as multiple view geometry, structure from motion, and model building. It is a mathematical theory relating multiple views (images) of a scene taken at different viewpoints to three-dimensional models of the (possibly dynamic) scene. This mathematical theory gave rise to algorithms that take as input images (or video) and provide as output a model of the scene. Such algorithms are one of the biggest successes of the field and they have many applications in other disciplines, such as graphics (image-based rendering, motion capture) and robotics (navigation). One of the difficulties, however is that the current tools cannot yet be fully automated, and they do not provide very accurate results. More research is required for automation and high precision. During the past few years we have investigated a number of basic questions underlying the structure from motion problem. Our investigations resulted in a small number of principles that characterize the problem. These principles, which give rise to automatic procedures and point to new avenues for studying the next level of the structure from motion problem, are the subject of this paper.},
	keywords = {3-D motion estimation, Buildings, Computer vision, Geometry, Graphics, Image sequences, Layout, Mathematical model, mathematical theory, model building, Motion estimation, multiple view geometry, multiple views, Navigation, optical flow, optical illusions, patch correspondence, Rendering (computer graphics), Robotics and automation, Solid modeling, structure from motion, three-dimensional models, visual space-time},
	isbn = {0018-9219},
	doi = {10.1109/JPROC.2002.801440},
	author = {Ferm{\"u}ller, Cornelia and Baker, P. and Aloimonos, J.}
}
@conference {14188,
	title = {A spherical eye from multiple cameras (makes better models of the world)},
	booktitle = {Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2001. CVPR 2001},
	volume = {1},
	year = {2001},
	month = {2001///},
	pages = {I-576- I-583 vol.1 - I-576- I-583 vol.1},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {The paper describes an imaging system that has been designed specifically for the purpose of recovering egomotion and structure from video. The system consists of six cameras in a network arranged so that they sample different parts of the visual sphere. This geometric configuration has provable advantages compared to small field of view cameras for the estimation of the system{\textquoteright}s own motion and consequently the estimation of shape models from the individual cameras. The reason is that inherent ambiguities of confusion between translation and rotation disappear. We provide algorithms for the calibration of the system and 3D motion estimation. The calibration is based on a new geometric constraint that relates the images of lines parallel in space to the rotation between the cameras. The 3D motion estimation uses a constraint relating structure directly to image gradients.},
	keywords = {3D motion estimation, Calibration, camera network, CAMERAS, Computer vision, egomotion recovery, geometric configuration, geometric constraint, image gradients, image sampling, imaging system, Laboratories, Layout, Motion estimation, multiple cameras, Pixel, Robot vision systems, SHAPE, shape models, Space technology, spherical eye, system calibration, video, video cameras, video signal processing, visual sphere sampling},
	isbn = {0-7695-1272-0},
	doi = {10.1109/CVPR.2001.990525},
	author = {Baker, P. and Ferm{\"u}ller, Cornelia and Aloimonos, J. and Pless, R.}
}
@conference {14267,
	title = {The statistics of optical flow: implications for the process of correspondence in vision},
	booktitle = {15th International Conference on Pattern Recognition, 2000. Proceedings},
	volume = {1},
	year = {2000},
	month = {2000///},
	pages = {119-126 vol.1 - 119-126 vol.1},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {This paper studies the three major categories of flow estimation methods: gradient-based, energy-based, and correlation methods; it analyzes different ways of compounding 1D motion estimates (image gradients, spatio-temporal frequency triplets, local correlation estimates) into 2D velocity estimates, including linear and nonlinear methods. Correcting for the bias would require knowledge of the noise parameters. In many situations, however, these are difficult to estimate accurately, as they change with the dynamic imagery in unpredictable and complex ways. Thus, the bias really is a problem inherent to optical flow estimation. We argue that the bias is also integral to the human visual system. It is the cause of the illusory perception of motion in the Ouchi pattern and also explains various psychophysical studies of the perception of moving plaids. Finally, the implication of the analysis is that flow or correspondence can be estimated very accurately only when feedback is utilized},
	keywords = {Bias, Computer vision, correlation, correlation methods, energy-based method, flow estimation, Frequency estimation, gradient method, gradient methods, Image analysis, Image motion analysis, Image sequences, least squares, least squares approximations, Motion estimation, Nonlinear optics, Optical feedback, optical flow, Optical harmonic generation, Optical noise, Statistics, Visual perception},
	isbn = {0-7695-0750-6},
	doi = {10.1109/ICPR.2000.905288},
	author = {Ferm{\"u}ller, Cornelia and Aloimonos, J.}
}
@conference {18311,
	title = {Estimation of composite object and camera image motion},
	booktitle = {The Proceedings of the Seventh IEEE International Conference on Computer Vision, 1999},
	volume = {1},
	year = {1999},
	month = {1999///},
	pages = {190-197 vol.1 - 190-197 vol.1},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {An approach for estimating composite independent object and camera image motions is proposed. The approach employs spatio-temporal flow models learned through observing typical movements of the object, to decompose image motion into independent object and camera motions. The spatio-temporal flow models of the object motion are represented as a set of orthogonal flow bases that are learned using principal component analysis of instantaneous flow measurements from a stationary camera. These models are then employed in scenes with a moving camera to extract motion trajectories relative to those learned. The performance of the algorithm is demonstrated on several image sequences of rigid and articulated bodies in motion},
	keywords = {articulated bodies, camera image motion, camera image motions, CAMERAS, composite independent object, composite object estimation, Computer vision, Educational institutions, Image sequences, instantaneous flow measurements, Laboratories, Layout, Motion analysis, Motion estimation, motion trajectories, orthogonal flow bases, Principal component analysis, spatio-temporal flow models, tracking, Vehicles},
	isbn = {0-7695-0164-8},
	doi = {10.1109/ICCV.1999.791217},
	author = {Yacoob,Yaser and Davis, Larry S.}
}
@conference {18568,
	title = {Field-to-frame transcoding with spatial and temporal downsampling},
	booktitle = {Image Processing, 1999. ICIP 99. Proceedings. 1999 International Conference on},
	volume = {4},
	year = {1999},
	month = {1999///},
	pages = {271 -275 vol.4 - 271 -275 vol.4},
	abstract = {We present an algorithm for transcoding high-rate compressed bitstreams containing field-coded interlaced video to lower-rate compressed bitstreams containing frame-coded progressive video. We focus on MPEG-2 to H.263 transcoding, however these results can be extended to other lower-rate video compression standards including MPEG-4 simple profile and MPEG-1. A conventional approach to the transcoding problem involves decoding the input bitstream, spatially and temporally downsampling the decoded frames, and re-encoding the result. The proposed transcoder achieves improved performance by exploiting the details of the MPEG-2 and H.263 compression standards when performing interlaced to progressive (or field to frame) conversion with spatial downsampling and frame-rate reduction. The transcoder reduces the MPEG-2 decoding requirements by temporally downsampling the data at the bitstream level and reduces the H.263 encoding requirements by largely bypassing H.263 motion estimation by reusing the motion vectors and coding modes given in the input bitstream. In software implementations, the proposed approach achieved a 5 times; speedup over the conventional approach with only a 0.3 and 0.5 dB loss in PSNR for the Carousel and Bus sequences},
	keywords = {data compression, decoding, Encoding, field-coded interlaced video, field-to-frame transcoding, frame-coded progressive video, H.263 motion estimation, H.263 transcoding, high-rate compressed bitstreams, lower-rate compressed bitstreams, Motion estimation, MPEG-1, MPEG-2, MPEG-4, spatial downsampling, standards, temporal downsampling, transcoder, video coding, video compression standards},
	doi = {10.1109/ICIP.1999.819593},
	author = {Wee,S.J. and Apostolopoulos,J.G. and Feamster, Nick}
}
@conference {11986,
	title = {Independent motion: the importance of history},
	booktitle = {Computer Vision and Pattern Recognition, 1999. IEEE Computer Society Conference on.},
	volume = {2},
	year = {1999},
	month = {1999///},
	pages = {97 Vol. 2 - 97 Vol. 2},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {We consider a problem central in aerial visual surveillance applications-detection and tracking of small, independently moving objects in long and noisy video sequences. We directly use spatiotemporal image intensity gradient measurements to compute an exact model of background motion. This allows the creation of accurate mosaics over many frames and the definition of a constraint violation function which acts as an indication of independent motion. A novel temporal integration method maintains confidence measures over long subsequences without computing the optic flow, requiring object models, or using a Kalman filler. The mosaic acts as a stable feature frame, allowing precise localization of the independently moving objects. We present a statistical analysis of the effects of image noise on the constraint violation measure and find a good match between the predicted probability distribution function and the measured sample frequencies in a test sequence},
	keywords = {aerial visual surveillance, background image, Fluid flow measurement, Frequency measurement, History, Motion detection, Motion estimation, Motion measurement, Noise measurement, Optical computing, Optical noise, spatiotemporal image intensity gradient measurements, Spatiotemporal phenomena, Surveillance, Video sequences},
	isbn = {0-7695-0149-4},
	doi = {10.1109/CVPR.1999.784614},
	author = {Pless, R. and Brodsky, T. and Aloimonos, J.}
}
@conference {11965,
	title = {Statistical biases in optic flow},
	booktitle = {Computer Vision and Pattern Recognition, 1999. IEEE Computer Society Conference on.},
	volume = {1},
	year = {1999},
	month = {1999///},
	pages = {566 Vol. 1 - 566 Vol. 1},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {The computation of optical flow from image derivatives is biased in regions of non uniform gradient distributions. A least-squares or total least squares approach to computing optic flow from image derivatives even in regions of consistent flow can lead to a systematic bias dependent upon the direction of the optic flow, the distribution of the gradient directions, and the distribution of the image noise. The bias a consistent underestimation of length and a directional error. Similar results hold for various methods of computing optical flow in the spatiotemporal frequency domain. The predicted bias in the optical flow is consistent with psychophysical evidence of human judgment of the velocity of moving plaids, and provides an explanation of the Ouchi illusion. Correction of the bias requires accurate estimates of the noise distribution; the failure of the human visual system to make these corrections illustrates both the difficulty of the task and the feasibility of using this distorted optic flow or undistorted normal flow in tasks requiring higher lever processing},
	keywords = {Distributed computing, Frequency domain analysis, HUMANS, image derivatives, Image motion analysis, Image sequences, Least squares methods, Motion estimation, Optical computing, Optical distortion, optical flow, Optical noise, Ouchi illusion, perception of motion, Psychology, Spatiotemporal phenomena, statistical analysis, systematic bias, total least squares},
	isbn = {0-7695-0149-4},
	doi = {10.1109/CVPR.1999.786994},
	author = {Ferm{\"u}ller, Cornelia and Pless, R. and Aloimonos, J.}
}
@conference {18323,
	title = {Tracking rigid motion using a compact-structure constraint},
	booktitle = {Computer Vision, 1999. The Proceedings of the Seventh IEEE International Conference on},
	volume = {1},
	year = {1999},
	month = {1999///},
	pages = {198 -205 vol.1 - 198 -205 vol.1},
	abstract = {An approach for tracking the motion of a rigid object using parameterized flow models and a compact-structure constraint is proposed. While polynomial parameterized flow models have been shown to be effective in tracking the rigid motion of planar objects, these models are inappropriate for tracking moving objects that change appearance revealing their 3D structure. We extend these models by adding a structure-compactness constraint that accounts for image motion that deviates from a planar structure. The constraint is based on the assumption that object structure variations are limited with respect to planar object projection onto the image plane and therefore can be expressed as a direct constraint on the image motion. The performance of the algorithm is demonstrated on several long image sequences of rigidly moving objects},
	keywords = {compact-structure constraint, image motion, Image sequences, Motion estimation, parameterized flow models, polynomial parameterized flow models, Polynomials, rigid motion tracking, rigid object, rigidly moving objects, structure-compactness constraint, tracking},
	doi = {10.1109/ICCV.1999.791218},
	author = {Yacoob,Yaser and Davis, Larry S.}
}
@conference {18324,
	title = {A framework for modeling appearance change in image sequences},
	booktitle = {Computer Vision, 1998. Sixth International Conference on},
	year = {1998},
	month = {1998/01//},
	pages = {660 - 667},
	abstract = {Image ldquo;appearance rdquo; may change over time due to a variety of causes such as: 1) object or camera motion; 2) generic photometric events including variations in illumination (e.g. shadows) and specular reflections; and 3) ldquo;iconic changes rdquo; which are specific to the objects being viewed and include complex occlusion events and changes in the material properties of the objects. We propose a general framework for representing and recovering these ldquo;appearance changes rdquo; in an image sequence as a ldquo;mixture rdquo; of different causes. The approach generalizes previous work on optical flow to provide a richer description of image events and more reliable estimates of image motion},
	keywords = {appearance change, camera motion, complex occlusion events, generic photometric events, iconic changes, illumination, image motion, Image sequences, Motion estimation, optical flow},
	doi = {10.1109/ICCV.1998.710788},
	author = {Black,M. J and Fleet,D. J and Yacoob,Yaser}
}
@conference {18325,
	title = {Parameterized modeling and recognition of activities},
	booktitle = {Computer Vision, 1998. Sixth International Conference on},
	year = {1998},
	month = {1998/01//},
	pages = {120 - 127},
	abstract = {A framework for modeling and recognition of temporal activities is proposed. The modeling of sets of exemplar activities is achieved by parameterizing their representation in the form of principal components. Recognition of spatio-temporal variants of modeled activities is achieved by parameterizing the search in the space of admissible transformations that the activities can undergo. Experiments on recognition of articulated and deformable object motion from image motion parameters are presented},
	keywords = {activities recognition, admissible transformations, articulated object motion, deformable object motion, exemplar activities, image motion parameters, Image sequences, Motion estimation, parameterized modeling, recognition, spatio-temporal variants},
	doi = {10.1109/ICCV.1998.710709},
	author = {Yacoob,Yaser and Black,M. J}
}
@conference {11993,
	title = {Self-calibration from image derivatives},
	booktitle = {Sixth International Conference on Computer Vision, 1998},
	year = {1998},
	month = {1998/01/04/7},
	pages = {83 - 89},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {This study investigates the problem of estimating the calibration parameters from image motion fields induced by a rigidly moving camera with unknown calibration parameters, where the image formation is modeled with a linear pinhole-camera model. The equations obtained show the flow to be clearly separated into a component due to the translation and the calibration parameters and a component due to the rotation and the calibration parameters. A set of parameters encoding the latter component are linearly related to the flow, and from these parameters the calibration can be determined. However, as for discrete motion, in the general case it is not possible, to decouple image measurements from two frames only into their translational and rotational component. Geometrically, the ambiguity takes the form of a part of the rotational component being parallel to the translational component, and thus the scene can be reconstructed only up to a projective transformation. In general, for a full calibration at least four successive image frames are necessary with the 3D-rotation changing between the measurements. The geometric analysis gives rise to a direct self-calibration method that avoids computation of optical flow or point correspondences and uses only normal flow measurements. In this technique the direction of translation is estimated employing in a novel way smoothness constraints. Then the calibration parameters are estimated from the rotational components of several flow fields using Levenberg-Marquardt parameter estimation, iterative in the calibration parameters only. The technique proposed does not require calibration objects in the scene or special camera motions and it also avoids the computation of exact correspondence. This makes it suitable for the calibration of active vision systems which have to acquire knowledge about their intrinsic parameters while they perform other tasks, or as a tool for analyzing image sequences in large video databases},
	keywords = {3D-rotation, active vision, Calibration, CAMERAS, discrete motion, Encoding, Equations, image derivatives, image formation, image measurements, Image motion analysis, image motion fields, Image reconstruction, Image sequences, large video databases, Layout, Levenberg-Marquardt parameter estimation, linear pinhole-camera model, Motion estimation, Motion measurement, Optical computing, parameter estimation, projective transformation, rigidly moving camera, self-calibration, smoothness constraints, unknown calibration parameters},
	isbn = {81-7319-221-9},
	doi = {10.1109/ICCV.1998.710704},
	author = {Brodsky, T. and Ferm{\"u}ller, Cornelia and Aloimonos, J.}
}
@conference {12047,
	title = {Which shape from motion?},
	booktitle = {Sixth International Conference on Computer Vision, 1998},
	year = {1998},
	month = {1998/01/04/7},
	pages = {689 - 695},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {In a practical situation, the rigid transformation relating different views is recovered with errors. In such a case, the recovered depth of the scene contains errors, and consequently a distorted version of visual space is computed. What then are meaningful shape representations that can be computed from the images? The result presented in this paper states that if the rigid transformation between different views is estimated in a way that gives rise to a minimum number of negative depth values, then at the center of the image affine shape can be correctly computed. This result is obtained by exploiting properties of the distortion function. The distortion model turns out to be a very powerful tool in the analysis and design of 3D motion and shape estimation algorithms, and as a byproduct of our analysis we present a computational explanation of psychophysical results demonstrating human visual space distortion from motion information},
	keywords = {3D motion estimation, affine shape, Algorithm design and analysis, Computer vision, distorted version, distortion function, human visual space distortion, HUMANS, Image motion analysis, image representation, Information analysis, Layout, Motion analysis, Motion estimation, motion information, Psychology, rigid transformation, SHAPE, shape estimation, shape representations, State estimation, visual space},
	isbn = {81-7319-221-9},
	doi = {10.1109/ICCV.1998.710792},
	author = {Ferm{\"u}ller, Cornelia and Aloimonos, J.}
}
@conference {14264,
	title = {The confounding of translation and rotation in reconstruction from multiple views},
	booktitle = {, 1997 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1997. Proceedings},
	year = {1997},
	month = {1997/06/17/19},
	pages = {250 - 256},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {If 3D rigid motion is estimated with some error a distorted version of the scene structure will in turn be computed. Of computational interest are these regions in space where the distortions are such that the depths become negative, because in order to be visible the scene has to lie in front of the image. The stability analysis for the structure-from-motion problem presented in this paper investigates the optimal relationship between the errors in the estimated translational and rotational parameters of a rigid motion, that results in the estimation of a minimum number of negative depth values. The input used is the value of the flow along some direction, which is more general than optic flow or correspondence. For a planar retina it is shown that the optimal configuration is achieved when the projections of the translational and rotational errors on the image plane are perpendicular. Furthermore, the projection of the actual and the estimated translation lie on a line passing through the image center. For a spherical retina given a rotational error, the optimal translation is the correct one, while given a translational error. The optimal rotational error is normal to the translational one at an equal distance from the real and estimated translations. The proofs, besides illuminating the confounding of translation and rotation in structure from motion, have an important application to ecological optics, explaining differences of planar and spherical eye or camera designs in motion and shape estimation},
	keywords = {3D rigid motion, CAMERAS, ecological optics, Error correction, Image motion analysis, Image reconstruction, Layout, Motion analysis, Motion estimation, multiple views, Optical distortion, optimal configuration, optimal rotational error, planar retina, RETINA, rigid motion, ROTATION, scene structure, shape estimation, spherical retina, Stability analysis, structure-from-motion problem, translation},
	isbn = {0-8186-7822-4},
	doi = {10.1109/CVPR.1997.609328},
	author = {Ferm{\"u}ller, Cornelia and Aloimonos, J.}
}
@conference {18321,
	title = {Learning parameterized models of image motion},
	booktitle = {Computer Vision and Pattern Recognition, 1997. Proceedings., 1997 IEEE Computer Society Conference on},
	year = {1997},
	month = {1997/06//},
	pages = {561 - 567},
	abstract = {A framework for learning parameterized models of optical flow from image sequences is presented. A class of motions is represented by a set of orthogonal basis flow fields that are computed from a training set using principal component analysis. Many complex image motions can be represented by a linear combination of a small number of these basis flows. The learned motion models may be used for optical flow estimation and for model-based recognition. For optical flow estimation we describe a robust, multi-resolution scheme for directly computing the parameters of the learned flow models from image derivatives. As examples we consider learning motion discontinuities, non-rigid motion of human mouths, and articulated human motion},
	keywords = {image motion, Image sequences, learning, learning (artificial intelligence), model-based recognition, Motion estimation, multi-resolution scheme, non-rigid motion, optical flow, optical flow estimation, parameterized models, Principal component analysis, training set},
	doi = {10.1109/CVPR.1997.609381},
	author = {Black,M. J and Yacoob,Yaser and Jepson,A. D and Fleet,D. J}
}
@article {18317,
	title = {Recognizing human facial expressions from long image sequences using optical flow},
	journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
	volume = {18},
	year = {1996},
	month = {1996/06//},
	pages = {636 - 642},
	abstract = {An approach to the analysis and representation of facial dynamics for recognition of facial expressions from image sequences is presented. The algorithms utilize optical flow computation to identify the direction of rigid and nonrigid motions that are caused by human facial expressions. A mid-level symbolic representation motivated by psychological considerations is developed. Recognition of six facial expressions, as well as eye blinking, is demonstrated on a large set of image sequences},
	keywords = {Computer vision, Eyebrows, face recognition, facial dynamics, Facial features, human facial expression recognition, HUMANS, Image motion analysis, image recognition, image representation, Image sequences, Motion analysis, Motion estimation, Optical computing, optical flow, symbolic representation, tracking},
	isbn = {0162-8828},
	doi = {10.1109/34.506414},
	author = {Yacoob,Yaser and Davis, Larry S.}
}
@conference {11982,
	title = {Global rigidity constraints in image displacement fields},
	booktitle = {Proceedings of Fifth International Conference on Computer Vision, 1995},
	year = {1995},
	month = {1995/06/20/23},
	pages = {245 - 250},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Image displacement fields-optical flow fields, stereo disparity fields, normal flow fields-due to rigid motion possess a global geometric structure which is independent of the scene in view. Motion vectors of certain lengths and directions are constrained to lie on the imaging surface at particular loci whose location and form depends solely on the 3D motion parameters. If optical flow fields or stereo disparity fields are considered, then equal vectors are shown to lie on conic sections. Similarly, for normal motion fields, equal vectors lie within regions whose boundaries also constitute conics. By studying various properties of these curves and regions and their relationships, a characterization of the structure of rigid motion fields is given. The goal of this paper is to introduce a concept underlying the global structure of image displacement fields. This concept gives rise to various constraints that could form the basis of algorithms for the recovery of visual information from multiple views},
	keywords = {3D motion parameters, algorithms, Automation, Computer science, Computer vision, conic sections, curves, equal vectors, Fluid flow measurement, global geometric structure, global rigidity constraints, image displacement fields, Image motion analysis, Image segmentation, Image sequences, imaging surface, Laboratories, Layout, Motion estimation, Motion measurement, motion vectors, multiple views, normal flow fields, optical flow fields, regions, rigid motion, stereo disparity fields, Stereo vision, vectors, visual information recovery},
	isbn = {0-8186-7042-8},
	doi = {10.1109/ICCV.1995.466779},
	author = {Ferm{\"u}ller, Cornelia and Aloimonos, J.}
}
@conference {14168,
	title = {Global rigidity constraints in image displacement fields},
	booktitle = {, Fifth International Conference on Computer Vision, 1995. Proceedings},
	year = {1995},
	month = {1995/06/20/23},
	pages = {245 - 250},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Image displacement fields-optical flow fields, stereo disparity fields, normal flow fields-due to rigid motion possess a global geometric structure which is independent of the scene in view. Motion vectors of certain lengths and directions are constrained to lie on the imaging surface at particular loci whose location and form depends solely on the 3D motion parameters. If optical flow fields or stereo disparity fields are considered, then equal vectors are shown to lie on conic sections. Similarly, for normal motion fields, equal vectors lie within regions whose boundaries also constitute conics. By studying various properties of these curves and regions and their relationships, a characterization of the structure of rigid motion fields is given. The goal of this paper is to introduce a concept underlying the global structure of image displacement fields. This concept gives rise to various constraints that could form the basis of algorithms for the recovery of visual information from multiple views},
	keywords = {3D motion parameters, algorithms, Automation, Computer science, Computer vision, conic sections, curves, equal vectors, Fluid flow measurement, global geometric structure, global rigidity constraints, image displacement fields, Image motion analysis, Image segmentation, Image sequences, imaging surface, Laboratories, Layout, Motion estimation, Motion measurement, motion vectors, multiple views, normal flow fields, optical flow fields, regions, rigid motion, stereo disparity fields, Stereo vision, vectors, visual information recovery},
	isbn = {0-8186-7042-8},
	doi = {10.1109/ICCV.1995.466779},
	author = {Ferm{\"u}ller, Cornelia and Aloimonos, J.}
}
@conference {14171,
	title = {The information in the direction of image flow},
	booktitle = {, International Symposium on Computer Vision, 1995. Proceedings},
	year = {1995},
	month = {1995/11/21/23},
	pages = {461 - 466},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {If instead of the full motion field, we consider only the direction of the motion field due to a rigid motion, what can we say about the information regarding the three-dimensional motion? In this paper it is shown that considering as the imaging surface the whole sphere, independently of the scene in view, two different rigid motions cannot give rise to the same directional motion field. If we restrict the image to half of a sphere (or an infinitely large image plane) two different rigid motions with instantaneous translational and rotational velocities (t1, ω1) and (t2, ω2) cannot give rise to the same directional motion field unless the plane through t1 and t2 is perpendicular to the plane through ω1 and ω2 (i.e., (t1{\texttimes}t2){\textperiodcentered}(ω1 {\texttimes}ω2)=0). In addition, in order to give a practical significance to these uniqueness results for the case of a limited field of view we also characterize the locations on the image where the motion vectors due to the different motions must have different directions. If (ω1{\texttimes}ω2){\textperiodcentered}(t1 {\texttimes}t2)=0 and certain additional constraints are met, then the two rigid motions could produce motion fields with the same direction. For this to happen the depth of each corresponding surface has to be within a certain range, defined by a second and a third order surface},
	keywords = {Automation, CAMERAS, Computer vision, Educational institutions, image flow, Image motion analysis, Image sequences, imaging surface, Laboratories, Layout, Motion analysis, Motion estimation, motion field, motion vectors, Optical imaging, rigid motion, rigid motions, three-dimensional motion},
	isbn = {0-8186-7190-4},
	doi = {10.1109/ISCV.1995.477071},
	author = {Brodsky, T. and Ferm{\"u}ller, Cornelia and Aloimonos, J.}
}
@conference {12031,
	title = {Iso-distortion contours and egomotion estimation},
	booktitle = {Proceedings of International Symposium on Computer Vision, 1995},
	year = {1995},
	month = {1995/11/21/23},
	pages = {55 - 60},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {This paper introduces the framework of iso-distortion contour to deal with the problem of depth distortion due to erroneous motion estimates, and various related aspects such as the effectiveness of the visibility constraint. The framework can also be used to inquire the uniqueness aspect of normal flow. Future work will examine the implications of the iso-distortion contours for the problem of multiple frame integration},
	keywords = {Automation, Computer vision, Degradation, depth distortion, Educational institutions, egomotion estimation, Equations, erroneous motion estimates, Error analysis, HUMANS, Image sequences, iso-distortion contours, Laboratories, Layout, Motion estimation, Robustness, visibility constraint},
	isbn = {0-8186-7190-4},
	doi = {10.1109/ISCV.1995.476977},
	author = {LoongFah Cheong and Aloimonos, J.}
}
@conference {14174,
	title = {Global 3D motion estimation},
	booktitle = {, 1993 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1993. Proceedings CVPR {\textquoteright}93},
	year = {1993},
	month = {1993/06/15/17},
	pages = {415 - 421},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {It is shown how a monocular observer can estimate its 3D motion relative to the scene by using normal flow measurements in a global and qualitative way. It is proved that local normal flow measurements form global patterns in the image plane. The position of these patterns is related to the 3D motion parameters. By locating some of these patterns, which depend only on subsets of the motion parameters, through a simple search technique, the 3D motion parameters can be found. The proposed algorithmic procedure is very robust, since it is not affected by small perturbations in the normal flow measurements. The direction of translation and the axis of rotation can be estimated with up to 100\% error in the image measurements},
	keywords = {3D motion parameters, Automation, axis of rotation, Computer vision, direction of translation, Educational institutions, Equations, Fluid flow measurement, global 3D motion estimation, Laboratories, Layout, monocular observer, Motion estimation, Motion measurement, normal flow measurements, Robustness, Rotation measurement, search problems, search technique, State estimation},
	isbn = {0-8186-3880-X},
	doi = {10.1109/CVPR.1993.341097},
	author = {Ferm{\"u}ller, Cornelia}
}
@conference {14176,
	title = {Motion constraint patterns},
	booktitle = {, Proceedings of IEEE Workshop on Qualitative Vision, 1993},
	year = {1993},
	month = {1993/06/14/},
	pages = {128 - 139},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {The problem of egomotion recovery has been treated by using as input local image motion, with the published algorithms utilizing the geometric constraint relating 2-D local image motion (optical flow, correspondence, derivatives of the image flow) to 3-D motion and structure. Since it has proved very difficult to achieve accurate input (local image motion), a lot of effort has been devoted to the development of robust techniques. A new approach to the problem of egomotion estimation is taken, based on constraints of a global nature. It is proved that local normal flow measurements form global patterns in the image plane. The position of these patterns is related to the three dimensional motion parameters. By locating some of these patterns, which depend only on subsets of the motion parameters, through a simple search technique, the 3-D motion parameters can be found. The proposed algorithmic procedure is very robust, since it is not affected by small perturbations in the normal flow measurements. As a matter of fact, since only the sign of the normal flow measurement is employed, the direction of translation and the axis of rotation can be estimated with up to 100\% error in the image measurements},
	keywords = {3D motion parameters, Automation, computational geometry, Computer vision, correspondence, Educational institutions, egomotion recovery, Fluid flow measurement, geometric constraint, Geometrical optics, Image motion analysis, image plane, Laboratories, local image motion, local normal flow measurements, Motion estimation, Motion measurement, motion parameters, optical flow, Rotation measurement},
	isbn = {0-8186-3692-0},
	doi = {10.1109/WQV.1993.262942},
	author = {Ferm{\"u}ller, Cornelia}
}
@conference {14246,
	title = {Perceptual computational advantages of tracking},
	booktitle = {, 11th IAPR International Conference on Pattern Recognition, 1992. Vol.I. Conference A: Computer Vision and Applications, Proceedings},
	year = {1992},
	month = {1992/09/30/Aug-3},
	pages = {599 - 602},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {The paradigm of active vision advocates studying visual problems in the form of modules that are directly related to a visual task for observers that are active. It is argued that in many cases when an object is moving in an unrestricted manner (translation and rotation) in the 3D world only the motion{\textquoteright}s translational components are of interest. For a monocular observer, using only the normal flow-the spatiotemporal derivatives of the image intensity function-the authors solve the problem of computing the direction of translation. Their strategy uses fixation and tracking. Fixation simplifies much of the computation by placing the object at the center of the visual field, and the main advantage of tracking is the accumulation of information over time. The authors show how tracking is accomplished using normal flow measurements and use it for two different tasks in the solution process. First, it serves as a tool to compensate for the lack of existence of an optical flow field and thus to estimate the translation parallel to the image plane; and second, it gathers information about the motion component perpendicular to the image plane},
	keywords = {active vision, Automation, Employment, fixation, image intensity function, Image motion analysis, IMAGE PROCESSING, Motion estimation, Nonlinear optics, Optical computing, Optical sensors, parameter estimation, pattern recognition, perceptual computational advantages, spatiotemporal derivatives, Spatiotemporal phenomena, tracking, unrestricted motion, visual flow measurements},
	isbn = {0-8186-2910-X},
	doi = {10.1109/ICPR.1992.201633},
	author = {Ferm{\"u}ller, Cornelia and Aloimonos, J.}
}
@conference {11990,
	title = {Relative depth from motion using normal flow: an active and purposive solution},
	booktitle = {Proceedings of the IEEE Workshop on Visual Motion, 1991},
	year = {1991},
	month = {1991/10/07/9},
	pages = {196 - 204},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {The authors show how an active observer can compute the relative depth of (stationary or moving) objects in the field of view using only the spatiotemporal derivatives of the time varying image intensity function. The solution they propose is purposive in the sense that it solves only the relative depth from motion problem and cannot be used for other problems related to motion; active in the sense that the activity of the observer is essential for the solution of the problem. Results indicate that exact computation of retinal motion does not appear to be a necessary first step for some problems related to visual motion. In addition, optic flow, whose computation is an ill-posed problem, is related to the motion of the scene only under very restrictive assumptions. As a result, the use of optic flow in some quantitative motion analysis studies is questionable},
	keywords = {3D information, Automation, Computer vision, Educational institutions, Image motion analysis, image recognition, Image sequences, Laboratories, Layout, Motion analysis, Motion estimation, Nonlinear optics, normal flow, optic flow, Optical computing, Optical sensors, relative depth, retinal motion, spatiotemporal derivatives, time varying image intensity function, visual motion},
	isbn = {0-8186-2153-2},
	doi = {10.1109/WVM.1991.212807},
	author = {Huang, Liuqing and Aloimonos, J.}
}
@conference {11974,
	title = {Optimal motion estimation},
	booktitle = {Workshop on Visual Motion, 1989.,Proceedings},
	year = {1989},
	month = {1989/03/20/22},
	pages = {229 - 237},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {The problem of using feature correspondences to recover the structure and 3D motion of a moving object from its successive images is analyzed. They formulate the problem as a quadratic minimization problem with a nonlinear constraint. Then they derive the condition for the solution to be optimal under the assumption of Gaussian noise in the input, in the maximum-likelihood-principle sense. The authors present two efficient ways to approximate it and discuss some inherent limitations of the structure-from-motion problem when two frames are used that should be taken into account in robotics applications that involve dynamic imagery. Finally, it is shown that some of the difficulties inherent in the two-frame approach disappear when redundancy in the data is introduced. This is concluded from experiments using a structure-from-motion algorithm that is based on multiple frames and uses only the rigidity assumption},
	keywords = {3D motion interpretation, Automation, Computer vision, computerised pattern recognition, computerised picture processing, constraint minimization, dynamic imagery, Educational institutions, feature correspondences, Gaussian noise, Image motion analysis, Laboratories, maximum-likelihood-principle, Minimization methods, Motion analysis, Motion estimation, motion parameters, moving object, multiple frames, nonlinear constraint, Optical computing, optimal motion estimation, parameter estimation, quadratic minimization, quadratic programming, redundancy, rigidity assumption, robotics applications, structure-from-motion, successive images, two-frame},
	isbn = {0-8186-1903-1},
	doi = {10.1109/WVM.1989.47114},
	author = {Spetsakis, M. E and Aloimonos, J.}
}
@conference {11957,
	title = {Optimal Computing Of Structure From Motion Using Point Correspondences In Two Frames},
	booktitle = {Proceedings of Second International Conference on Computer Vision},
	year = {1988},
	month = {1988/12/05/8},
	pages = {449 - 453},
	publisher = {IEEE},
	organization = {IEEE},
	keywords = {Automation, Computer vision, Educational institutions, Gaussian noise, Image motion analysis, Laboratories, Least squares approximation, Least squares methods, Motion estimation, Optical computing},
	isbn = {0-8186-0883-8},
	doi = {10.1109/CCV.1988.590022},
	author = {Spetsakis, M. E and Aloimonos, J.}
}
@conference {12024,
	title = {Determining three dimensional transformation parameters from images: Theory},
	booktitle = {1987 IEEE International Conference on Robotics and Automation. Proceedings},
	volume = {4},
	year = {1987},
	month = {1987/03//},
	pages = {57 - 61},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {We present a theory for the determination of the three dimensional transformation parameters of an object, from its images. The input to this process is the image intensity function and its temporal derivative. In particular, our results are: 1) If the structure of the transforming object in view is known, then the transformation parameters are determined from the solution of a linear system. Rigid motion is a special ease of our theory. 2)If the structure of the object in view is not known, then both the structure and transformation parameters may be computed through a hill climbing or simulated annealing algorithm.},
	keywords = {Automation, Computational modeling, Educational institutions, Image motion analysis, Indium tin oxide, Linear systems, Motion estimation, Optical computing, Robots, Simulated annealing},
	doi = {10.1109/ROBOT.1987.1088011},
	author = {Ito, E. and Aloimonos, J.}
}
