Optimal multi-level thresholding using a two-stage Otsu optimization approach
Pattern Recognition Letters
Proceedings of the Conference on High Performance Graphics 2009
Image segmentation method using thresholds automatically determined from picture contents
Journal on Image and Video Processing
Local and Global Collaboration for Object Detection Enhancement with Information Redundancy
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
Efficient content analysis engine for visual surveillance network
IEEE Transactions on Circuits and Systems for Video Technology
Improving flexible macroblock ordering of H.264/AVC
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
K-means based segmentation for real-time zenithal people counting
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Adaptive shadow estimator for removing shadow of moving object
Computer Vision and Image Understanding
Video segmentation using Metropolis Hastings Algorithm for the VCR operations
International Journal of Advanced Media and Communication
On implementing motion-based Region of Interest detection on multi-core CELL
Computer Vision and Image Understanding
System Level Design and Implementation for Region-of-Interest Segmentation
Journal of Signal Processing Systems
Reconfigurable Morphological Image Processing Accelerator for Video Object Segmentation
Journal of Signal Processing Systems
Stereo-based object segmentation combining spatio-temporal information
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part III
Modified bacterial foraging algorithm based multilevel thresholding for image segmentation
Engineering Applications of Artificial Intelligence
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Automatic video segmentation plays an important role in real-time MPEG-4 encoding systems. Several video segmentation algorithms have been proposed; however, most of them are not suitable for real-time applications because of high computation load and many parameters needed to be set in advance. This paper presents a fast video segmentation algorithm for MPEG-4 camera systems. With change detection and background registration techniques, this algorithm can give satisfying segmentation results with low computation load. The processing speed of 40 QCIF frames per second can be achieved on a personal computer with an 800 MHz Pentium-III processor. Besides, it has shadow cancellation mode, which can deal with light changing effect and shadow effect. A fast global motion compensation algorithm is also included in this algorithm to make it applicable in slight moving camera situations. Furthermore, the required parameters can be decided automatically, which can enhance the proposed algorithm to have adaptive threshold ability. It can be integrated into MPEG-4 videophone systems and digital cameras.