Dynamic texture segmentation based on deterministic partially self-avoiding walks

Autor(es)

Goncalves, Wesley Nunes ^[1] ; Bruno, Odemir Martinez ^[1]

Afiliação do(s) autor(es)

^[1] Univ Sao Paulo, Sao Carlos Inst Phys IFSC, BR-13560970 Sao Carlos, SP - Brazil

• Tipo de documento: Artigo Científico
• Fonte: COMPUTER VISION AND IMAGE UNDERSTANDING; v. 117, n. 9, p. 1163-1174, SEP 2013.
• Citações Web of Science: 3

Resumo

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)

• Processo FAPESP: 11/01523-1 - Métodos de visão computacional aplicados à identificação e análise de plantas
• Beneficiário: Odemir Martinez Bruno
• Modalidade de apoio: Auxílio à Pesquisa - Regular
• Processo FAPESP: 10/08614-0 - Análise de Texturas Estáticas e Dinâmicas e suas Aplicações em Biologia e Nanotecnologia
• Beneficiário: Wesley Nunes Gonçalves
• Modalidade de apoio: Bolsas no Brasil - Doutorado