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  • World Scientific Pub Co Pte Ltd  (3)
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  • World Scientific Pub Co Pte Ltd  (3)
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  • 1
    Online Resource
    Online Resource
    WATERSHED SEGMENTATION WITH AUTOMATIC ALTITUDE SELECTION AND REGION MERGING BASED ON THE MARKOV RANDOM FIELD MODEL
    World Scientific Pub Co Pte Ltd ; 2010
    In:  International Journal of Pattern Recognition and Artificial Intelligence Vol. 24, No. 01 ( 2010-02), p. 153-171
    Details
    In: International Journal of Pattern Recognition and Artificial Intelligence, World Scientific Pub Co Pte Ltd, Vol. 24, No. 01 ( 2010-02), p. 153-171
    Abstract: Watershed transformation has proven to be an important tool in image analysis. However, the resulting image of watershed transformation is inevitably over-segmented due to the presence of noise or local irregularities in the input image. In this paper, the use of contour altitude at the immersion stage is proposed. Block gradient information computed from the input gradient image is defined and used to obtain a critical altitude of watershed flooding. This altitude is then refined based on entropy estimated from the intermediate segmentation result. Thereafter, an optimal altitude and its corresponding segmentation result can be obtained. Although this process can favorably reduce the number of regions, the quality of segmentation still requires further improvement. Hence, a Markov Random Field (MRF) model defined on a region adjacency graph (RAG) is adopted. Because the MRF model can merge neighboring regions that are similar in local statistic properties, it thus alleviates the over-segmentation problem and improves the quality of image segmentation. In the experimental studies, the proposed method has been tested using several benchmark images. It achieves improved appearance and energy indices in comparison with the results obtained by conventional methods.
    Type of Medium: Online Resource
    ISSN: 0218-0014 , 1793-6381
    URL: Article
    DOI: 10.1142/S021800141000783X
    Language: English
    Publisher: World Scientific Pub Co Pte Ltd
    Publication Date: 2010
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    Online Resource
  • 2
    Online Resource
    Online Resource
    GENETIC ALGORITHMS FOR ERROR-BOUNDED POLYGONAL APPROXIMATION
    World Scientific Pub Co Pte Ltd ; 2000
    In:  International Journal of Pattern Recognition and Artificial Intelligence Vol. 14, No. 03 ( 2000-05), p. 297-314
    Details
    In: International Journal of Pattern Recognition and Artificial Intelligence, World Scientific Pub Co Pte Ltd, Vol. 14, No. 03 ( 2000-05), p. 297-314
    Abstract: A new polygonal approximation algorithm, employing the concept of genetic evolution, is presented. In the proposed method, a chromosome is used to represent a polygon by a binary string. Each bit, called a gene, represents a point on the given curve. Three genetic operators, including selection, crossover, and mutation, are designed to obtain the approximated polygon whose error is bounded by a given norm. Many experiments show that the convergence is guaranteed and the optimal or near-optimal solutions can be obtained. Compared with the Zhu–Seneviratne algorithm, 24 the proposed algorithm successfully reduced the number of segments under the same error condition in the polygonal approximation.
    Type of Medium: Online Resource
    ISSN: 0218-0014 , 1793-6381
    URL: Article
    DOI: 10.1142/S0218001400000209
    Language: English
    Publisher: World Scientific Pub Co Pte Ltd
    Publication Date: 2000
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  • 3
    Online Resource
    Online Resource
    REGION SIMILARITY RELATIONSHIP BETWEEN WATERSHED AND PENALIZED FUZZY HOPFIELD NEURAL NETWORK ALGORITHMS FOR BRAIN IMAGE SEGMENTATION
    World Scientific Pub Co Pte Ltd ; 2008
    In:  International Journal of Pattern Recognition and Artificial Intelligence Vol. 22, No. 07 ( 2008-11), p. 1403-1425
    Details
    In: International Journal of Pattern Recognition and Artificial Intelligence, World Scientific Pub Co Pte Ltd, Vol. 22, No. 07 ( 2008-11), p. 1403-1425
    Abstract: A robust image segmentation method that combines the watershed segmentation and penalized fuzzy Hopfield neural network (PFHNN) algorithms to minimize undesirable over-segmentation is described in this paper. This method incorporates spatial graph representation derived from the watershed segmented regions and cluster analysis information obtained from the PFHNN algorithm to achieve efficient image segmentation. The proposed scheme employs the Markov random field (MRF) model on the region adjacency graph (RAG) to improve the quality of watershed segmentation. Here, the fusion criterion is according to the correlation coefficient, which uses inter-region similarities to determine the merging of regions. Analysis of the performance of the proposed technique is presented through quantitative and qualitative validation experiments on benchmark images, and significant and promising segmentation results are presented using brain phantom simulated data.
    Type of Medium: Online Resource
    ISSN: 0218-0014 , 1793-6381
    URL: Article
    DOI: 10.1142/S0218001408006788
    Language: English
    Publisher: World Scientific Pub Co Pte Ltd
    Publication Date: 2008
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