JOURNAL ARTICLE

Contour-Based Motion Segmentation Using Few Priors

Abstract

In this paper, we propose a novel method of motion segmentation through image sequence. In our work, no assumptions of boundary densities, color, or textures are made about the background or the object. Neither do we assume any special motion model to the background or the object. With the help of classic Bayesian theory, an energy function describing the piece-wise smooth motion field of the image sequences are formulated. By minimizing this energy function, a spatiotemporal contour evolves until it enfolds the volume generated by moving objects. Very promising experimental results are provided using numerous real image sequences with natural objects and camera motion

Keywords:
Artificial intelligence Computer vision Motion field Image segmentation Computer science Segmentation Active contour model Motion (physics) Prior probability Boundary (topology) Object (grammar) Motion estimation Scale-space segmentation Bayesian probability Pattern recognition (psychology) Mathematics

Metrics

1
Cited By
0.00
FWCI (Field Weighted Citation Impact)
19
Refs
0.08
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Advanced Vision and Imaging
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Video Surveillance and Tracking Methods
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Medical Image Segmentation Techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition

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