Conference proceeding
Unsupervised Motion Segmentation Using Metric Embedding of Features
SIMILARITY-BASED PATTERN RECOGNITION, SIMBAD 2015, v 9370, pp 133-145
01 Jan 2015
Featured in Collection : UN Sustainable Development Goals @ Drexel
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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1 citations in Scopus
Details
- Title
- Unsupervised Motion Segmentation Using Metric Embedding of Features
- Creators
- Yusuf Osmanlioglu - Drexel UniversitySven Dickinson - University of TorontoAli Shokoufandeh - Drexel University
- Contributors
- A Feragen (Editor)M Pelillo (Editor)M Loog (Editor)
- Publication Details
- SIMILARITY-BASED PATTERN RECOGNITION, SIMBAD 2015, v 9370, pp 133-145
- Series
- Lecture Notes in Computer Science
- Publisher
- Springer Nature
- Number of pages
- 13
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Computer Science
- Web of Science ID
- WOS:000374478900011
- Scopus ID
- 2-s2.0-84945903482
- Other Identifier
- 991019167670004721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Information Systems
- Computer Science, Interdisciplinary Applications
- Computer Science, Theory & Methods
- Robotics