@conference {14227,
	title = {Learning shift-invariant sparse representation of actions},
	booktitle = {2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
	year = {2010},
	month = {2010/06/13/18},
	pages = {2630 - 2637},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {A central problem in the analysis of motion capture (MoCap) data is how to decompose motion sequences into primitives. Ideally, a description in terms of primitives should facilitate the recognition, synthesis, and characterization of actions. We propose an unsupervised learning algorithm for automatically decomposing joint movements in human motion capture (MoCap) sequences into shift-invariant basis functions. Our formulation models the time series data of joint movements in actions as a sparse linear combination of short basis functions (snippets), which are executed (or {\textquotedblleft}activated{\textquotedblright}) at different positions in time. Given a set of MoCap sequences of different actions, our algorithm finds the decomposition of MoCap sequences in terms of basis functions and their activations in time. Using the tools of L1 minimization, the procedure alternately solves two large convex minimizations: Given the basis functions, a variant of Orthogonal Matching Pursuit solves for the activations, and given the activations, the Split Bregman Algorithm solves for the basis functions. Experiments demonstrate the power of the decomposition in a number of applications, including action recognition, retrieval, MoCap data compression, and as a tool for classification in the diagnosis of Parkinson (a motion disorder disease).},
	keywords = {action characterization, Action recognition, action retrieval, action synthesis, Character recognition, data compression, human motion capture, HUMANS, Image matching, Image motion analysis, image representation, Image sequences, Information retrieval, joint movements, large convex minimizations, learning (artificial intelligence), learning shift-invariant sparse representation, Matching pursuit algorithms, minimisation, Minimization methods, MoCap data compression, Motion analysis, motion capture analysis, motion disorder disease, motion sequences, orthogonal matching pursuit, Parkinson diagnosis, Parkinson{\textquoteright}s disease, Pursuit algorithms, shift-invariant basis functions, short basis functions, snippets, sparse linear combination, split Bregman algorithm, time series, time series data, Unsupervised learning, unsupervised learning algorithm},
	isbn = {978-1-4244-6984-0},
	doi = {10.1109/CVPR.2010.5539977},
	author = {Li,Yi and Ferm{\"u}ller, Cornelia and Aloimonos, J. and Hui Ji}
}
@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 {12041,
	title = {Motion Segmentation: A Synergistic Approach},
	booktitle = {Computer Vision and Pattern Recognition, IEEE Computer Society Conference on},
	volume = {2},
	year = {1999},
	month = {1999///},
	pages = {2226 - 2226},
	publisher = {IEEE Computer Society},
	organization = {IEEE Computer Society},
	address = {Los Alamitos, CA, USA},
	abstract = {Since estimation of camera motion requires knowledge of independent motion, and moving object detection and localization requires knowledge about the camera motion, the two problems of motion estimation and segmentation need to be solved together in a synergistic manner. This paper provides an approach to treating both these problems simultaneously. The technique introduced here is based on a novel concept, {\textquoteleft}{\textquoteleft}scene ruggedness,{\textquoteright}{\textquoteright} which parameterizes the variation in estimated scene depth with the error in the underlying three-dimensional (3D) motion. The idea is that incorrect 3D motion estimates cause distortions in the estimated depth map, and as a result smooth scene patches are computed as rugged surfaces. The correct 3D motion can be distinguished, as it does not cause any distortion and thus gives rise to the background patches with the least depth variation between depth discontinuities, with the locations corresponding to independent motion being rugged. The algorithm presented employs a binocular observer whose nature is exploited in the extraction of depth discontinuities, a step that facilitates the overall procedure, but the technique can be extended to a monocular observer in a variety of ways.},
	keywords = {epipolar minimization, independent motion detection, Motion analysis},
	doi = {http://doi.ieeecomputersociety.org/10.1109/CVPR.1999.784633},
	author = {Ferm{\"u}ller, Cornelia and Brodsky, Tomas and Aloimonos, J.}
}
@conference {12054,
	title = {Shape from Video},
	booktitle = {Computer Vision and Pattern Recognition, IEEE Computer Society Conference on},
	volume = {2},
	year = {1999},
	month = {1999///},
	pages = {2146 - 2146},
	publisher = {IEEE Computer Society},
	organization = {IEEE Computer Society},
	address = {Los Alamitos, CA, USA},
	abstract = {This paper presents a novel technique for recovering the shape of a static scene from a video sequence due to a rigidly moving camera. The solution procedure consists of two stages. In the first stage, the rigid motion of the camera at each instant in time is recovered. This provides the transformation between successive viewing positions. The solution is achieved through new constraints which relate 3D motion and shape directly to the image derivatives. These constraints allow the combination of the processes of 3D motion estimation and segmentation by exploiting the geometry and statistics inherent in the data. In the second stage the scene surfaces are reconstructed through an optimization procedure which utilizes data from all the frames of a short video sequence. A number of experimental results demonstrate the potential of the approach.},
	keywords = {epipolar minimization, model extraction, Motion analysis, vision and graphics},
	doi = {http://doi.ieeecomputersociety.org/10.1109/CVPR.1999.784622},
	author = {Brodsky, Tomas 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.}
}
@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 {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 {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.}
}
@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 {11960,
	title = {Purposive and qualitative active vision},
	booktitle = {Proceedings of 10th International Conference on Pattern Recognition, 1990},
	volume = {i},
	year = {1990},
	month = {1990/06/16/21},
	pages = {346-360 vol.1 - 346-360 vol.1},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {The traditional view of the problem of computer vision as a recovery problem is questioned, and the paradigm of purposive-qualitative vision is offered as an alternative. This paradigm considers vision as a general recognition problem (recognition of objects, patterns or situations). To demonstrate the usefulness of the framework, the design of the Medusa of CVL is described. It is noted that this machine can perform complex visual tasks without reconstructing the world. If it is provided with intentions, knowledge of the environment, and planning capabilities, it can perform highly sophisticated navigational tasks. It is explained why the traditional structure from motion problem cannot be solved in some cases and why there is reason to be pessimistic about the optimal performance of a structure from motion module. New directions for future research on this problem in the recovery paradigm, e.g., research on stability or robustness, are suggested},
	keywords = {active vision, Automation, brain models, complex visual tasks, Computer vision, environmental knowledge, highly sophisticated navigational tasks, HUMANS, Image reconstruction, intentions, Kinetic theory, Laboratories, Medusa, Motion analysis, Navigation, planning, planning (artificial intelligence), purposive-qualitative vision, recovery problem, Robust stability, Robustness, SHAPE, stability},
	isbn = {0-8186-2062-5},
	doi = {10.1109/ICPR.1990.118128},
	author = {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.}
}
