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Unsupervised video segmentation based on watersheds and temporal tracking
IEEE Transactions on Circuits and Systems for Video Technology
Video segmentation for content-based coding
IEEE Transactions on Circuits and Systems for Video Technology
IEEE Transactions on Circuits and Systems for Video Technology
Fast and automatic video object segmentation and tracking for content-based applications
IEEE Transactions on Circuits and Systems for Video Technology
Efficient moving object segmentation algorithm using background registration technique
IEEE Transactions on Circuits and Systems for Video Technology
Automatic segmentation of moving objects in video sequences: a region labeling approach
IEEE Transactions on Circuits and Systems for Video Technology
Object-based video coding by global-to-local motion segmentation
IEEE Transactions on Circuits and Systems for Video Technology
Predictive watershed: a fast watershed algorithm for video segmentation
IEEE Transactions on Circuits and Systems for Video Technology
Automatic moving object extraction for content-based applications
IEEE Transactions on Circuits and Systems for Video Technology
A Bayesian approach to video object segmentation via merging 3-D watershed volumes
IEEE Transactions on Circuits and Systems for Video Technology
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
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Video object segmentation is an important pre-processing task for many video analysis systems. To achieve the requirement of real-time video analysis, hardware acceleration is required. In this paper, after analyzing existing video object segmentation algorithms, it is found that most of the core operations can be implemented with simple morphology operations. Therefore, with the concepts of morphological image processing element array and stream processing, a reconfigurable morphological image processing accelerator is proposed, where by the proposed instruction set, the operation of each processing element can be controlled, and the interconnection between processing elements can also be reconfigured. Simulation results show that most of the core operations of video object segmentation can be supported by the accelerator by only changing the instructions. A prototype chip is designed to support real-time change-detection-and-background-registration based video object segmentation algorithm. This chip incorporates eight macro processing elements and can support a processing capacity of 6,200 9-bit morphological operations per second on a SIF image. Furthermore, with the proposed tiling and pipelined-parallel techniques, a real-time watershed transform can be achieved using 32 macro processing elements.