A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distance transformations in digital images
Computer Vision, Graphics, and Image Processing
International Journal of Computer Vision
Statistical model-based change detection in moving video
Signal Processing
Automatic moving object and background separation
Signal Processing - Video segmentation for content-based processing manipulation
An integrated scheme for object-based video abstraction
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
A Noise Robust Method for Segmentation of Moving Objects in Video Sequences
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97) -Volume 4 - Volume 4
The MPEG-4 video standard verification model
IEEE Transactions on Circuits and Systems for Video Technology
Video segmentation based on multiple features for interactive multimedia applications
IEEE Transactions on Circuits and Systems for Video Technology
Semiautomatic segmentation and tracking of semantic video objects
IEEE Transactions on Circuits and Systems for Video Technology
IEEE Transactions on Circuits and Systems for Video Technology
Computer Vision and Image Understanding - Special isssue on video retrieval and summarization
Exemplar-based video inpainting without ghost shadow artifacts by maintaining temporal continuity
IEEE Transactions on Circuits and Systems for Video Technology
Content-based attention ranking using visual and contextual attention model for baseball videos
IEEE Transactions on Multimedia - Special issue on integration of context and content
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In this paper, we propose two novel video object (VO) extraction schemes, specifically designed for two different scenarios of content-based video analysis applications. One is a change detection-based VO extraction algorithm appropriate to surveillance type video sequences, where automatic detection of new appearance of objects are important in envisaging on-line object-oriented applications as well as object-based coding. The other is an object tracking-based method, which is especially robust to video sequences with moving background, although human intervention is needed in the process. In both cases, the semantically meaningful video objects are obtained by a final regularization stage realized by means of a cascade of morphological filters. Experimental results obtained on the MPEG-4 test sequences are presented respectively.