DocumentCode :
1368366
Title :
Motion segmentation based on motion/brightness integration and oscillatory correlation
Author :
Çesmeli, Erdogan ; Wang, DeLiang
Author_Institution :
Biomed. Eng. Center, Ohio State Univ., Columbus, OH, USA
Volume :
11
Issue :
4
fYear :
2000
fDate :
7/1/2000 12:00:00 AM
Firstpage :
935
Lastpage :
947
Abstract :
A segmentation method based on the integration of motion and brightness is proposed for image sequences. The method is composed of two parallel pathways that process motion and brightness, respectively, Inspired by the visual system, the motion pathway has two stages. The first stage estimates local motion at locations with reliable information. The second stage performs segmentation based on local motion estimates. In the brightness pathway, the input scene is segmented into regions based on brightness distribution. Subsequently, segmentation results from the two pathways are integrated to refine motion estimates. The final segmentation is performed in the motion network based on refined estimates. For segmentation, locally excitatory globally inhibitory oscillator network (LEGION) architecture is employed whereby the oscillators corresponding to a region of similar motion/brightness oscillate in synchrony and different regions attain different phases. Results on synthetic and real image sequences are provided, and comparisons with other methods are made
Keywords :
correlation methods; image segmentation; image sequences; motion estimation; neural nets; synchronisation; LEGION; brightness; image segmentation; image sequences; locally excitatory globally inhibitory oscillator network; motion estimation; neural networks; oscillatory correlation; synchronisation; Apertures; Brightness; Computer vision; Image segmentation; Image sequences; Layout; Local oscillators; Motion estimation; Motion segmentation; Neural networks;
fLanguage :
English
Journal_Title :
Neural Networks, IEEE Transactions on
Publisher :
ieee
ISSN :
1045-9227
Type :
jour
DOI :
10.1109/72.857773
Filename :
857773
Link To Document :
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