Extending case-based reasoning by discovering and using image features in IVF
SAC '00 Proceedings of the 2000 ACM symposium on Applied computing - Volume 1
Unsupervised Segmentation of Color-Texture Regions in Images and Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
Extraction of Video Objects via Surface Optimization and Voronoi Order
Journal of VLSI Signal Processing Systems
Moving Object Segmentation Based on Human Visual Sensitivity
BMVC '00 Proceedings of the First IEEE International Workshop on Biologically Motivated Computer Vision
A method for single-stimulus quality assessment of segmented video
EURASIP Journal on Applied Signal Processing
Robust bilayer video segmentation by adaptive propagation of global shape and local appearance
Journal of Visual Communication and Image Representation
Region-Level motion-based foreground detection with shadow removal using MRFs
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part I
Efficient moving object segmentation algorithm for illumination change in surveillance system
ICIAR'05 Proceedings of the Second international conference on Image Analysis and Recognition
Video event detection for fault monitoring in assembly automation
International Journal of Intelligent Systems Technologies and Applications
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This paper presents a new morphological spatio-temporal segmentation algorithm. The algorithm incorporates luminance and motion information simultaneously and uses morphological tools such as morphological filters and watershed algorithm. The procedure toward complete segmentation consists of three steps: joint marker extraction, boundary decision, and motion-based region fusion. First, the joint marker extraction identifies the presence of homogeneous regions in both motion and luminance, where a simple joint marker extraction technique is proposed. Second, the spatio-temporal boundaries are decided by the watershed algorithm. For this purpose, a new joint similarity measure is proposed. Finally, an elimination of redundant regions is done using motion-based region fusion. By incorporating spatial and temporal information simultaneously, we can obtain visually meaningful segmentation results. Simulation results demonstrates the efficiency of the proposed method