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2015 | OriginalPaper | Buchkapitel

Unsupervised Motion Segmentation Using Metric Embedding of Features

verfasst von : Yusuf Osmanlıoğlu, Sven Dickinson, Ali Shokoufandeh

Erschienen in: Similarity-Based Pattern Recognition

Verlag: Springer International Publishing

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Abstract

Motion segmentation is a well studied problem in computer vision. Most approaches assume a priori knowledge of the number of moving objects in the scene. In the absence of such information, motion segmentation is generally achieved through brute force search, e.g., searching over all possible priors or iterating over a search for the most prominent motion. In this paper, we propose an efficient method that achieves motion segmentation over a sequence of frames while estimating the number of moving segments; no prior assumption is made about the structure of scene. We utilize metric embedding to map a complex graph of image features and their relations into hierarchically well-separated tree, yielding a simplified topology over which the motions are segmented. Moreover, the method provides a hierarchical decomposition of motion for objects with moving parts.

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Titel: Unsupervised Motion Segmentation Using Metric Embedding of Features
verfasst von: Yusuf Osmanlıoğlu
Sven Dickinson
Ali Shokoufandeh
Verlag: Springer International Publishing
Buch: Similarity-Based Pattern Recognition
Print ISBN: 978-3-319-24260-6

Electronic ISBN: 978-3-319-24261-3

Copyright-Jahr: 2015
DOI: https://doi.org/10.1007/978-3-319-24261-3_11

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