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    Gabor texture in active appearance models

    Gao, X. and Su, Y. and Li, Xuelong and Tao, D. (2009) Gabor texture in active appearance models. Neurocomputing 72 (13-15), pp. 3174-3181. ISSN 0925-2312.

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    In computer vision applications, Active Appearance Models (AAMs) is usually used to model the shape and the gray-level appearance of an object of interest using statistical methods, such as PCA. However, intensity values used in standard AAMs cannot provide enough information for image alignment. In this paper, we firstly propose to utilize Gabor filters to represent the image texture. The benefit of Gabor-based representation is that it can express local structures of an image. As a result, this representation can lead to more accurate matching when condition changes. Given the problem of the excessive storage and computational complexity of the Gabor, three different Gabor-based image representations are used in AAMs: (1) GaborD is the sum of Gabor filter responses over directions, (2) GaborS is the sum of Gabor filter responses over scales, and (3) GaborSD is the sum of Gabor filter responses over scales and directions. Through a large number of experiments, we show that the proposed Gabor representations lead to more accurate and robust matching between model and images.


    Item Type: Article
    Keyword(s) / Subject(s): computer vision, active appearance models (AAMs), gabor, texture representation
    School: Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences
    Depositing User: Sarah Hall
    Date Deposited: 11 Jul 2013 15:31
    Last Modified: 09 Aug 2023 12:33


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