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Classification of Objects and Background Using Parallel Genetic Algorithm Based Clustering
Kanungo, Priyadarshi (National Institute of Technology. Department of Electrical Engineering (Rourkela, Índia))
Kumar Nanda, Pradipta (National Institute of Technology. Department of Electrical Engineering (Rourkela, Índia))
Ghosh, Asish (Indian Statistical Institute)

Data: 2007
Resum: In this paper, two novel strategies have been proposed to obtain segmentation of an object and background in a given scene. The first one, known as Featureless(FL) approach, deals with the histogram of the original image where Parallel Genetic Algorithm (PGA) based clustering notion is used to determine the optimal threshold from the discrete nature of the histogram distribution. In this regard, we have proposed a new interconnection model for PGA. The second scheme, the Featured Based(FB) approach, is based on the proposed featured histogram distribution. A feature from the given image is extracted and the histogram corresponding to the derived feature pixels is used to determine the optimal threshold for the original image. The proposed PGA based clustering is used to determine the optimal threshold. The performance of both the schemes is compared with that of Otsu's and Kwon's method and FB method is found to be the best among the three techniques.
Drets: Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades. Creative Commons
Llengua: Anglès
Document: Article ; recerca ; Versió publicada
Matèria: Segmentació d'imatge ; Algoritme genètic paral·lel ; Agrupament ; Segmentación de imagen ; Algoritmo genético paralelo ; Agrupamiento ; Image segmentation ; Parallel Genetic Algorithm ; Clustering ; Thresholding
Publicat a: ELCVIA. Electronic letters on computer vision and image analysis, V. 6 n. 3 (2007) p. 42-53, ISSN 1577-5097

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DOI: 10.5565/rev/elcvia.141

12 p, 305.6 KB

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