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Abstract: A novel unsupervised video object segmentation algorithm is presented, aiming to segment a video sequence to objects: spatiotemporal regions representing a meaningful part of the sequence. The proposed algorithm consists of three stages: initial segmentation of the first frame using color, motion, and position information, based on a variant of the K-Means-with-connectivity -constraint algorithm; a temporal tracking algorithm, using a Bayes classifier and rule-based processing to reassign... (Update)
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Compressed-Domain Object Detection For Video Understanding - Vasileios Mezaris Ioannis (2004) (Correct)
Real-Time Compressed-Domain Spatiotemporal.. - Mezaris.. (2004) (Correct)
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2: A Framework for the Efficient Segmentation of Large-format Color Images - Mezaris, Kompatsiaris et al. - 2002
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BibTeX entry: (Update)
V. Mezaris, I. Kompatsiaris, and M. G. Strintzis, "Video object segmentation using bayes-based temporal tracking and trajectory-based region merging," IEEE Trans. Circuits Syst. Video Technol., to be published. http://citeseer.ist.psu.edu/mezaris04video.html More
@misc{ mezaris-video,
author = "V. Mezaris and I. Kompatsiaris and M. Strintzis",
title = "Video object segmentation using bayes-based temporal tracking and trajectory-based
region merging",
text = "V. Mezaris, I. Kompatsiaris, and M. G. Strintzis, Video object segmentation
using bayes-based temporal tracking and trajectory-based region merging,
IEEE Trans. Circuits Syst. Video Technol., to be published.",
url = "citeseer.ist.psu.edu/mezaris04video.html" }
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