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67
Performance of optical flow techniques
 INTERNATIONAL JOURNAL OF COMPUTER VISION
, 1994
"... While different optical flow techniques continue to appear, there has been a lack of quantitative evaluation of existing methods. For a common set of real and synthetic image sequences, we report the results of a number of regularly cited optical flow techniques, including instances of differential, ..."
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Cited by 1246 (31 self)
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While different optical flow techniques continue to appear, there has been a lack of quantitative evaluation of existing methods. For a common set of real and synthetic image sequences, we report the results of a number of regularly cited optical flow techniques, including instances of differential, matching, energybased and phasebased methods. Our comparisons are primarily empirical, and concentrate on the accuracy, reliability and density of the velocity measurements; they show that performance can differ significantly among the techniques we implemented.
Coding, Analysis, Interpretation, and Recognition of Facial Expressions
, 1997
"... We describe a computer vision system for observing facial motion by using an optimal estimation optical flow method coupled with geometric, physical and motionbased dynamic models describing the facial structure. Our method produces a reliable parametric representation of the face's independen ..."
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Cited by 319 (6 self)
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We describe a computer vision system for observing facial motion by using an optimal estimation optical flow method coupled with geometric, physical and motionbased dynamic models describing the facial structure. Our method produces a reliable parametric representation of the face's independent muscle action groups, as well as an accurate estimate of facial motion. Previous efforts at analysis of facial expression have been based on the Facial Action Coding System (FACS), a representation developed in order to allow human psychologists to code expression from static pictures. To avoid use of this heuristic coding scheme, we have used our computer vision system to probabilistically characterize facial motion and muscle activation in an experimental population, thus deriving a new, more accurate representation of human facial expressions that we call FACS+. Finally, we show how this method can be used for coding, analysis, interpretation, and recognition of facial expressions.
Automatic Facial Expression Analysis: A Survey
 PATTERN RECOGNITION
, 1999
"... Over the last decade, automatic facial expression analysis has become an active research area that finds potential applications in areas such as more engaging humancomputer interfaces, talking heads, image retrieval and human emotion analysis. Facial expressions reflect not only emotions, but ot ..."
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Cited by 258 (0 self)
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Over the last decade, automatic facial expression analysis has become an active research area that finds potential applications in areas such as more engaging humancomputer interfaces, talking heads, image retrieval and human emotion analysis. Facial expressions reflect not only emotions, but other mental activities, social interaction and physiological signals. In this survey we introduce the most prominent automatic facial expression analysis methods and systems presented in the literature. Facial motion and deformation extraction approaches as well as classification methods are discussed with respect to issues such as face normalization, facial expression dynamics and facial expression intensity, but also with regard to their robustness towards environmental changes.
A Vision System for Observing and Extracting Facial Action Parameters
 PROCEEDINGS OF COMPUTER VISION AND PATTERN RECOGNITION (CVPR 94
, 1994
"... We describe a computer vision system for observing the "action units" of a face using video sequences as input. The visual observation (sensing) is achieved by using an optimal estimation optical flow method coupled with a geometric and a physical (muscle) model describing the facial struc ..."
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Cited by 81 (12 self)
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We describe a computer vision system for observing the "action units" of a face using video sequences as input. The visual observation (sensing) is achieved by using an optimal estimation optical flow method coupled with a geometric and a physical (muscle) model describing the facial structure. This modeling results in a timevarying spatial patterning of facial shape and a parametric representation of the independent muscle action groups, responsible for the observed facial motions. These muscle action patterns may then be used for analysis, interpretation, and synthesis. Thus, by interpreting facial motions within a physicsbased optimal estimation framework, a new control model of facial movement is developed. The newly extracted action units (which we name "FACS+") are both physics and geometrybased, and extend the wellknown FACS parameters for facial expressions by adding temporal information and nonlocal spatial patterning of facial motion.
Bayesian computation in recurrent neural circuits
 Neural Computation
, 2004
"... A large number of human psychophysical results have been successfully explained in recent years using Bayesian models. However, the neural implementation of such models remains largely unclear. In this paper, we show that a network architecture commonly used to model the cerebral cortex can implem ..."
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Cited by 80 (4 self)
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A large number of human psychophysical results have been successfully explained in recent years using Bayesian models. However, the neural implementation of such models remains largely unclear. In this paper, we show that a network architecture commonly used to model the cerebral cortex can implement Bayesian inference for an arbitrary hidden Markov model. We illustrate the approach using an orientation discrimination task and a visual motion detection task. In the case of orientation discrimination, we show that the model network can infer the posterior distribution over orientations and correctly estimate stimulus orientation in the presence of significant noise. In the case of motion detection, we show that the resulting model network exhibits direction selectivity and correctly computes the posterior probabilities over motion direction and position. When used to solve the wellknown random dots motion discrimination task, the model generates responses that mimic the activities of evidenceaccumulating neurons in cortical areas LIP and FEF. The framework introduced in the paper posits a new interpretation of cortical activities in terms of log posterior probabilities of stimuli occurring in the natural world. 1 1
Slow and Smooth: a Bayesian theory for the combination of of local motion signals in human vision
, 1998
"... In order to estimate the motion of an object, the visual system needs to combine multiple local measurements, each of which carries some degree of ambiguity. We present a model of motion perception whereby measurements from dierent image regions are combined according to a Bayesian estimator: the ..."
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Cited by 67 (3 self)
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In order to estimate the motion of an object, the visual system needs to combine multiple local measurements, each of which carries some degree of ambiguity. We present a model of motion perception whereby measurements from dierent image regions are combined according to a Bayesian estimator: the estimated motion maximizes the posterior probability assuming a prior favoring slow and smooth velocities. In reviewing a large number of previously published phenomena we nd that the Bayesian estimator predicts a wide range of psychophysical results. This suggests that the seemingly complex set of illusions arise from a single computational strategy that is optimal under reasonable assumptions. 1 Introduction Estimating motion in scenes containing multiple, complex motions remains a dicult problem for computer vision systems, yet is performed eortlessly by human observers. Motion analysis in such scenes imposes conicting demands on the design of a vision system (Braddick, 1993)....
On benchmarking optical flow
, 2001
"... Evaluating the performance of optical flow algorithms has been difficult because of the lack of ground truth data sets for complex scenes. We present a new method for generating motion fields from real sequences containing polyhedral objects and present a test suite for benchmarking optical flow alg ..."
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Cited by 62 (0 self)
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Evaluating the performance of optical flow algorithms has been difficult because of the lack of ground truth data sets for complex scenes. We present a new method for generating motion fields from real sequences containing polyhedral objects and present a test suite for benchmarking optical flow algorithms consisting of complex synthetic sequences and real scenes with ground truth. We provide a preliminary quantitative evaluation of seven optical flow algorithms using these synthetic and real sequences. Ultimately, we feel that researchers should benchmark their own algorithms using a standard suite. To that end, we offer our Web site as a repository for standard sequences and results.
Estimating Motion in Image Sequences  A tutorial on modeling and computation of 2D motion
 IEEE Signal Processing Magazine
, 1999
"... this paper should be helpful to researchers and practitioners working in the fields of video compression and processing, as well as in computer vision. Although the understanding of issues involved in the computation of motion has significantly increased over the last decade, we are still far from g ..."
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Cited by 44 (0 self)
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this paper should be helpful to researchers and practitioners working in the fields of video compression and processing, as well as in computer vision. Although the understanding of issues involved in the computation of motion has significantly increased over the last decade, we are still far from generic, robust, realtime motion estimation algorithms. The selection of the best motion estimator is still highly dependent on the application. Nevertheless, a broad variety of estimation models, criteria and optimization schemes can be treated in a unified framework presented here, thus allowing a direct comparison and leading to a deeper understanding of the properties of the resulting estimators.
Optical Flow Estimation
, 2005
"... This chapter provides a tutorial introduction to gradientbased optical flow estimation. We discuss leastsquares and robust estimators, iterative coarsetofine refinement, different forms of parametric motion models, different conservation assumptions, probabilistic formulations, and robust mixtur ..."
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Cited by 39 (4 self)
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This chapter provides a tutorial introduction to gradientbased optical flow estimation. We discuss leastsquares and robust estimators, iterative coarsetofine refinement, different forms of parametric motion models, different conservation assumptions, probabilistic formulations, and robust mixture models.
Velocity Likelihoods in Biological and Machine Vision
 In Probabilistic Models of the Brain: Perception and Neural Function
, 2001
"... Recent approaches to estimating twodimensional image motion and to modeling the perception of image motion have achieved success with Bayesian formulations. With a Bayesian approach, the goal is to compute the posterior probability distribution of velocity, which is proportional to a likelihood ..."
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Cited by 38 (4 self)
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Recent approaches to estimating twodimensional image motion and to modeling the perception of image motion have achieved success with Bayesian formulations. With a Bayesian approach, the goal is to compute the posterior probability distribution of velocity, which is proportional to a likelihood function and a prior. The likelihood function describes the probability of observing the image data given the image velocity; surprisingly, there is still disagreement about the right likelihood function to use. Here we derive a likelihood function by starting from a generative model. We assume that the scene translates, conserving image brightness, while the image is equal to the projected scene plus noise. We discuss the connection between the resulting likelihood function and existing models of motion analysis. We show that the likelihood can be calculated by a population of units whose response properties are similar to \motion energy" units. This suggests that a population o...