An improved watershed algorithm based on efficient computation of shortest paths
Pattern Recognition
Lightweight object tracking in compressed video streams demonstrated in region-of-interest coding
EURASIP Journal on Applied Signal Processing
Four-Color Theorem and Level Set Methods for Watershed Segmentation
International Journal of Computer Vision
Image segmentation method using thresholds automatically determined from picture contents
Journal on Image and Video Processing
IEEE Transactions on Circuits and Systems for Video Technology
Reconfigurable Morphological Image Processing Accelerator for Video Object Segmentation
Journal of Signal Processing Systems
Spatio-temporal quasi-flat zones for morphological video segmentation
ISMM'11 Proceedings of the 10th international conference on Mathematical morphology and its applications to image and signal processing
Region growing with automatic seeding for semantic video object segmentation
ICAPR'05 Proceedings of the Third international conference on Pattern Recognition and Image Analysis - Volume Part II
Efficient object segmentation using digital matting for MPEG video sequences
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
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The watershed transform is a key operator in video segmentation algorithms. However, the computation load of watershed transform is too large for real-time applications. In this paper, a new fast watershed algorithm, named P-watershed, for image sequence segmentation is proposed. By utilizing the temporal coherence property of the video signal, this algorithm updates watersheds instead of searching watersheds in every frame, which can avoid a lot of redundant computation. The watershed process can be accelerated, and the segmentation results are almost the same as those of conventional algorithms. Moreover, an intra-inter watershed scheme (IP-watershed) is also proposed to further improve the results. Experimental results show that this algorithm can save 20%-50% computation without degrading the segmentation results. This algorithm can be combined with any video segmentation algorithm to give more precise segmentation results. An example is also shown by combining a background registration and change-detection-based segmentation algorithm with P-Watershed. This new video segmentation algorithm can give accurate object masks with acceptable computation complexity.