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Artificial Intelligence
Early orientation selection: tangent fields and the dimensionality of their support
Papers from the second workshop Vol. 13 on Human and Machine Vision II
Probabilistic reasoning in intelligent systems: networks of plausible inference
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Using Perceptual Organization to Extract 3D Structures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision, Graphics, and Image Processing
Human and machine vision: computing perceptual organisation
Human and machine vision: computing perceptual organisation
Shape and motion from image streams under orthography: a factorization method
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Computational geometry in C
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IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Perceptual completion of occluded surfaces
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Perceptual grouping for generic recognition
International Journal of Computer Vision - Special issue on computer vision research at the University of Southern California
Inferring global perceptual contours from local features
International Journal of Computer Vision - Special issue on computer vision research at the University of Southern California
A probabilistic approach to perceptual grouping
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Asynchronous perceptual grouping: from contours to relevant 2-D structures
Computer Vision and Image Understanding
Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data- and model-driven selection using parallel line groups
Computer Vision and Image Understanding
A Generic Grouping Algorithm and Its Quantitative Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Segmentation and Factorization-Based Motion and Structure Estimation for Long Image Sequences
IEEE Transactions on Pattern Analysis and Machine Intelligence
Direct incremental model-based image motion segmentation for video analysis
Signal Processing - Video segmentation for content-based processing manipulation
Hierarchical Image Segmentation—Part I: Detection of Regular Curves in a Vector Graph
International Journal of Computer Vision
Parameterized modeling and recognition of activities
Computer Vision and Image Understanding
Detection of independent motion using directional motion estimation
Computer Vision and Image Understanding
Fast linear expected-time alogorithms for computing maxima and convex hulls
SODA '90 Proceedings of the first annual ACM-SIAM symposium on Discrete algorithms
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IEEE Transactions on Pattern Analysis and Machine Intelligence
A Bayesian Computer Vision System for Modeling Human Interactions
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Figure-Ground Discrimination: A Combinatorial Optimization Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geometric Primitive Extraction Using a Genetic Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Normalized Cuts and Image Segmentation
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Visual Organization for Figure/Ground Separation
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Segmentation by Grouping Junctions
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Learning to Form Large Groups of Salient Image Features
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Using Adaptive Tracking to Classify and Monitor Activities in a Site
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Stochastic completion fields: a neural model of illusory contour shape and salience
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Spatio-temporal segmentation based on motion and static segmentation
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol. 1)-Volume 1 - Volume 1
Tracking of articulated structures exploiting spatio-temporal image slices
ICIP '97 Proceedings of the 1997 International Conference on Image Processing (ICIP '97) 3-Volume Set-Volume 3 - Volume 3
Fragment Grouping via the Principle of Perceptual Occlusion
ICPR '96 Proceedings of the 1996 International Conference on Pattern Recognition (ICPR '96) Volume I - Volume 7270
Multimedia Tools and Applications
Applications of a simple characterization of human gait in surveillance
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on gait analysis
Video scene interpretation using perceptual prominence and mise-en-scène features
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
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The role of perceptual organization in motion analysis has heretofore been minimal. In this work we present a simple but powerful computational model and associated algorithms based on the use of perceptual organizational principles, such as temporal coherence (or common fate) and spatial proximity, for motion segmentation. The computational model does not use the traditional frame by frame motion analysis; rather it treats an image sequence as a single 3D spatio-temporal volume. It endeavors to find organizations in this volume of data over three levels-signal, primitive, and structural. The signal level is concerned with detecting individual image pixels that are probably part of a moving object. The primitive level groups these individual pixels into planar patches, which we call the temporal envelopes. Compositions of these temporal envelopes describe the spatio-temporal surfaces that result from object motion. At the structural level, we detect these compositions of temporal envelopes by utilizing the structure and organization among them. The algorithms employed to realize the computational model include 3D edge detection. Hough transformation, and graph based methods to group the temporal envelopes based on Gestalt principles. The significance of the Gestalt relationships between any two temporal envelopes is expressed in probabilistic terms. One of the attractive features of the adopted algorithm is that it does not require the detection of special 2D features or the tracking of these features across frames. We demonstrate that even with simple grouping strategies, we can easily handle drastic illumination changes, occlusion events, and multiple moving objects, without the use of training and specific object or illumination models. We present results on a large variety of motion sequences to demonstrate this robustness.