Segmentation from motion of non-rigid objects by neuronal lateral interaction
Pattern Recognition Letters
Motion and Structure from Image Sequences
Motion and Structure from Image Sequences
Extracting Gestural Motion Trajectories
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
An Appearance-Based Representation of Action
ICPR '96 Proceedings of the 1996 International Conference on Pattern Recognition (ICPR '96) Volume I - Volume 7270
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To be able to understand the motion of non-rigid objects, techniques in image processing and computer vision are essential for motion analysis. Lateral interaction in accumulative computation for extracting non-rigid blobs and shapes from an image sequence has recently been presented, as well as its application to segmentation from motion. In this paper we show an architecture consisting of five layers based on spatial and temporal coherence in visual motion analysis with application to visual surveillance. The LIAC method used in general task ”spatio-temporal coherent shape building” consists in (a) spatial coherence for brightness-based image segmentation, (b) temporal coherence for motion-based pixel charge computation, (c) spatial coherence for charge-based pixel charge computation, (d) spatial coherence for charge-based blob fusion, and, (e) spatial coherence for charge-based shape fusion. In our case, temporal coherence (in accumulative computation) is understood as a measure of frame to frame motion persistency on a pixel, whilst spatial coherence (in lateral interaction) is a measure of pixel to neighbouring pixels accumulative charge comparison.