@conference {12449,
	title = {P2C2: Programmable pixel compressive camera for high speed imaging},
	booktitle = {2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
	year = {2011},
	month = {2011/06/20/25},
	pages = {329 - 336},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {We describe an imaging architecture for compressive video sensing termed programmable pixel compressive camera (P2C2). P2C2 allows us to capture fast phenomena at frame rates higher than the camera sensor. In P2C2, each pixel has an independent shutter that is modulated at a rate higher than the camera frame-rate. The observed intensity at a pixel is an integration of the incoming light modulated by its specific shutter. We propose a reconstruction algorithm that uses the data from P2C2 along with additional priors about videos to perform temporal super-resolution. We model the spatial redundancy of videos using sparse representations and the temporal redundancy using brightness constancy constraints inferred via optical flow. We show that by modeling such spatio-temporal redundancies in a video volume, one can faithfully recover the underlying high-speed video frames from the observed low speed coded video. The imaging architecture and the reconstruction algorithm allows us to achieve temporal super-resolution without loss in spatial resolution. We implement a prototype of P2C2 using an LCOS modulator and recover several videos at 200 fps using a 25 fps camera.},
	keywords = {Brightness, brightness constancy constraint, camera sensor, CAMERAS, compressive video sensing, high speed imaging, high-speed video frames, Image sequences, imaging, imaging architecture, independent shutter, Liquid crystal on silicon, low speed coded video, Modulation, optical flow, P2C2, programmable pixel compressive camera, reconstruction algorithm, sparse representation, Spatial resolution, spatio-temporal redundancies, temporal redundancy, temporal super-resolution, video coding},
	isbn = {978-1-4577-0394-2},
	doi = {10.1109/CVPR.2011.5995542},
	author = {Reddy, D. and Veeraraghavan,A. and Chellapa, Rama}
}
@conference {14193,
	title = {Active segmentation for robotics},
	booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems, 2009. IROS 2009},
	year = {2009},
	month = {2009/10/10/15},
	pages = {3133 - 3139},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {The semantic robots of the immediate future are robots that will be able to find and recognize objects in any environment. They need the capability of segmenting objects in their visual field. In this paper, we propose a novel approach to segmentation based on the operation of fixation by an active observer. Our approach is different from current approaches: while existing works attempt to segment the whole scene at once into many areas, we segment only one image region, specifically the one containing the fixation point. Furthermore, our solution integrates monocular cues (color, texture) with binocular cues (stereo disparities and optical flow). Experiments with real imagery collected by our active robot and from the known databases demonstrate the promise of the approach.},
	keywords = {binocular cues, image colour analysis, Image segmentation, Image texture, Intelligent robots, Layout, Machine vision, monocular cues, Navigation, optical flow, Orbital robotics, Robot sensing systems, robot vision, Robot vision systems, robotics active segmentation, Robotics and automation, semantic robot, Simultaneous localization and mapping, stereo disparity, stereo image processing},
	isbn = {978-1-4244-3803-7},
	doi = {10.1109/IROS.2009.5354325},
	author = {Mishra,A. and Aloimonos, J. 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 {12740,
	title = {Fast two-frame multiscale dense optical flow estimation using discrete wavelet filters},
	journal = {Journal of the Optical Society of America AJ. Opt. Soc. Am. A},
	volume = {20},
	year = {2003},
	month = {2003///},
	pages = {1505 - 1515},
	abstract = {A multiscale algorithm with complexity O(N) (where N is the number of pixels in one image) using wavelet filters is proposed to estimate dense optical flow from two frames. Hierarchical image representation by wavelet decomposition is integrated with differential techniques in a new multiscale framework. It is shown that if a compactly supported wavelet basis with one vanishing moment is carefully selected, hierarchical image, first-order derivative, and corner representations can be obtained from the wavelet decomposition. On the basis of this result, three of the four components of the wavelet decomposition are employed to estimate dense optical flow with use of only two frames. This overcomes the {\textquotedblleft}flattening-out{\textquotedblright} problem in traditional pyramid methods, which produce large errors when low-texture regions become flat at coarse levels as a result of blurring. A two-dimensional affine motion model is used to formulate the optical flow problem as a linear system, with all resolutions simultaneously (i.e., coarse-and-fine) rather than the traditional coarse-to-fine approach, which unavoidably propagates errors from the coarse level. This not only helps to improve the accuracy but also makes the hardware implementation of our algorithm simple. Experiments on different types of image sequences, together with quantitative and qualitative comparisons with several other optical flow methods, are given to demonstrate the effectiveness and the robustness of our algorithm.},
	keywords = {digital image processing, Image analysis, optical flow},
	doi = {10.1364/JOSAA.20.001505},
	url = {http://josaa.osa.org/abstract.cfm?URI=josaa-20-8-1505},
	author = {Liu,Haiying and Chellapa, Rama and Rosenfeld,Azriel}
}
@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.}
}
@article {14166,
	title = {The Ouchi illusion as an artifact of biased flow estimation},
	journal = {Vision Research},
	volume = {40},
	year = {2000},
	month = {2000/01//},
	pages = {77 - 95},
	abstract = {A pattern by Ouchi has the surprising property that small motions can cause illusory relative motion between the inset and background regions. The effect can be attained with small retinal motions or a slight jiggling of the paper and is robust over large changes in the patterns, frequencies and boundary shapes. In this paper, we explain that the cause of the illusion lies in the statistical difficulty of integrating local one-dimensional motion signals into two-dimensional image velocity measurements. The estimation of image velocity generally is biased, and for the particular spatial gradient distributions of the Ouchi pattern the bias is highly pronounced, giving rise to a large difference in the velocity estimates in the two regions. The computational model introduced to describe the statistical estimation of image velocity also accounts for the findings of psychophysical studies with variations of the Ouchi pattern and for various findings on the perception of moving plaids. The insight gained from this computational study challenges the current models used to explain biological vision systems and to construct robotic vision systems. Considering the statistical difficulties in image velocity estimation in conjunction with the problem of discontinuity detection in motion fields suggests that theoretically the process of optical flow computations should not be carried out in isolation but in conjunction with the higher level processes of 3D motion estimation, segmentation and shape computation.},
	keywords = {Bias, MOTION, optical flow, Plaid, Statistics},
	isbn = {0042-6989},
	doi = {10.1016/S0042-6989(99)00162-5},
	url = {http://www.sciencedirect.com/science/article/pii/S0042698999001625},
	author = {Ferm{\"u}ller, Cornelia and Pless,Robert and Aloimonos, J.}
}
@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 {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 {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 {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 {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 {12020,
	title = {Exploratory active vision: theory},
	booktitle = {Proceedings of 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1992},
	year = {1992},
	month = {1992/06/15/18},
	pages = {10 - 15},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {An active approach to the integration of shape from x modules-here shape from shading and shape from texture-is proposed. The question of what constitutes a good motion for the active observer is addressed. Generally, the role of the visual system is to provide depth information to an autonomous robot; a trajectory module will then interpret it to determine a motion for the robot, which in turn will affect the visual information received. It is suggested that the motion can also be chosen so as to improve the performance of the visual system},
	keywords = {active observer, Active shape model, active vision, Automation, autonomous robot, CAMERAS, Computer science, Computer vision, depth information, Laboratories, Layout, Mobile robots, Motion analysis, optical flow, Robots, shape from shading, shape from texture, shape from x modules, trajectory module, Visual system},
	isbn = {0-8186-2855-3},
	doi = {10.1109/CVPR.1992.223234},
	author = {Herve,J. -Y and Aloimonos, J.}
}
