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Texture Boundary Detection Based on the Long Correlation Model
January 1989 (vol. 11 no. 1)
pp. 58-67

The problem of detecting texture boundaries without assuming any knowledge on the number of regions or the types of textures is considered. Texture boundaries are often regarded as better features than intensity edges, because a large class of images can be considered a composite of several different texture regions. An algorithm is developed that detects texture boundaries at reasonably high resolution without assuming any prior knowledge on the texture composition of the image. The algorithm utilizes the long correlation texture model with a small number of parameters to characterize textures. The parameters of the model are estimated by a least-squares method in the frequency domain. The existence and the location of texture boundary is estimated by the maximum-likelihood method. The algorithm is applied to several different images, and its performance is shown by examples. Experimental results show that the algorithm successfully detects texture boundaries without knowing the number of types of textures in the image.

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Index Terms:
texture boundary detection; picture processing; pattern recognition; long correlation model; least-squares method; frequency domain; maximum-likelihood method; parameter estimation; pattern recognition; picture processing
Citation:
R.L. Kashyap, K.B. Eom, "Texture Boundary Detection Based on the Long Correlation Model," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 11, no. 1, pp. 58-67, Jan. 1989, doi:10.1109/34.23113
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