| Full text | |
| Author(s): |
Total Authors: 2
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| Affiliation: | [1] Univ Sao Paulo, Sao Carlos Inst Phys IFSC, BR-13560970 Sao Carlos, SP - Brazil
Total Affiliations: 1
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| Document type: | Journal article |
| Source: | COMPUTER VISION AND IMAGE UNDERSTANDING; v. 117, n. 9, p. 1163-1174, SEP 2013. |
| Web of Science Citations: | 3 |
| Abstract | |
Recently there has been a considerable interest in dynamic textures due to the explosive growth of multimedia databases. In addition, dynamic texture appears in a wide range of videos, which makes it very important in applications concerning to model physical phenomena. Thus, dynamic textures have emerged as a new field of investigation that extends the static or spatial textures to the spatio-temporal domain. In this paper, we propose a novel approach for dynamic texture segmentation based on automata theory and k-means algorithm. In this approach, a feature vector is extracted for each pixel by applying deterministic partially self-avoiding walks on three orthogonal planes of the video. Then, these feature vectors are clustered by the well-known k-means algorithm. Although the k-means algorithm has shown interesting results, it only ensures its convergence to a local minimum, which affects the final result of segmentation. In order to overcome this drawback, we compare six methods of initialization of the k-means. The experimental results have demonstrated the effectiveness of our proposed approach compared to the state-of-the-art segmentation methods. (C) 2013 Published by Elsevier Inc. (AU) | |
| FAPESP's process: | 11/01523-1 - Computer vision methods applied to the identification and analysis of plants |
| Grantee: | Odemir Martinez Bruno |
| Support Opportunities: | Regular Research Grants |
| FAPESP's process: | 10/08614-0 - Static and Dynamic Texture Analysis and their Applications in Biology and Nanotechnology |
| Grantee: | Wesley Nunes Gonçalves |
| Support Opportunities: | Scholarships in Brazil - Doctorate |