Thresholding of noisy shoeprint images based on pixel context

Su, Hongjiang, Crookes, Danny and Bouridane, Ahmed (2007) Thresholding of noisy shoeprint images based on pixel context. Pattern Recognition Letters, 28 (2). pp. 301-307. ISSN 0167-8655

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In a typical shoeprint classification and retrieval system, the first step is to segment meaningful basic shapes and patterns in a noisy shoeprint image. This step has significant influence on shape descriptors and shoeprint indexing in the later stages. In this paper, we extend a recently developed denoising technique proposed by Buades, called non-local mean filtering, to give a more general model. In this model, the expected result of an operation on a pixel can be estimated by performing the same operation on all of its reference pixels in the same image. A working pixel’s reference pixels are those pixels whose neighbourhoods are similar to the working pixel’s neighbourhood. Similarity is based on the correlation between the local neighbourhoods of the working pixel and the reference pixel. We incorporate a special instance of this general case into thresholding a very noisy shoeprint image. Visual and quantitative comparisons with two benchmarking techniques, by Otsu and Kittler, are conducted in the last section, giving evidence of the effectiveness of our method for thresholding noisy shoeprint images.

Item Type: Article
Uncontrolled Keywords: reference pixels, shoeprint image
Subjects: G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Sarah Howells
Date Deposited: 19 Nov 2012 15:08
Last Modified: 13 Oct 2019 00:24

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