Feature detection from local energy
Pattern Recognition Letters
Algorithms for clustering data
Algorithms for clustering data
On the classification of image features
Pattern Recognition Letters
Performance of optical flow techniques
International Journal of Computer Vision
What is the goal of sensory coding?
Neural Computation
International Journal of Computer Vision
A hierarchical Markov modeling approach for the segmentation and tracking of deformable shapes
Graphical Models and Image Processing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geodesic Active Contours and Level Sets for the Detection and Tracking of Moving Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Measurement of Image Velocity
Moving Target Classification and Tracking from Real-time Video
WACV '98 Proceedings of the 4th IEEE Workshop on Applications of Computer Vision (WACV'98)
Temporal spatio-velocity transform and its application to tracking and interaction
Computer Vision and Image Understanding - Special issue on event detection in video
An iterative region-growing algorithm for motion segmentation and estimation: Research Articles
International Journal of Intelligent Systems - Robotics and Computer Vision
International Journal of Intelligent Systems Technologies and Applications
3D-orientation space: filters and sampling
SCIA'03 Proceedings of the 13th Scandinavian conference on Image analysis
Dissimilarity measures for visual pattern partitioning
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part II
A survey on visual surveillance of object motion and behaviors
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Fast occluded object tracking by a robust appearance filter
IEEE Transactions on Pattern Analysis and Machine Intelligence
Simultaneous motion estimation and segmentation
IEEE Transactions on Image Processing
Multiple motion segmentation with level sets
IEEE Transactions on Image Processing
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This work tackles the segmentation of apparent-motion from a bottom-up perspective. When no information is available to build prior high-level models, the only alternative are bottom-up techniques. Hence, the whole segmentation process relies on the suitability of the low-level features selected to describe motion. A wide variety of low-level spatio-temporal features have been proposed so far. However, all of them suffer from diverse drawbacks. Here, we propose the use of composite energy features in bottom-up motion segmentation to solve several of these problems. Composite energy features are clusters of energy filters-pairs of band-pass filters in quadrature-each one sensitive to a different set of scale, orientation, direction of motion and speed. They are grouped in order to reconstruct independent motion patterns in a video sequence. A composite energy feature, this is, the response of one of these clusters of filters, can be built as a combination of the responses of the individual filters. Therefore, it inherits the desirable properties of energy filters but providing a more complete representation of motion patterns. In this paper, we will present our approach for integration of composite features based on the concept of Phase Congruence. We will show some results that illustrate the capabilities of this low-level motion representation and its usefulness in bottom-up motion segmentation and tracking.