Thresholding of noisy shoeprint images based on pixel context

Authors:
Hongjiang Su;Danny Crookes;Ahmed Bouridane
Affiliations:
Queen's University Belfast, Image and Vision Group, School of EECS, Northern Ireland Science Park, Queen's Road, Queen's Island, Belfast BT3 9DT, United Kingdom;Queen's University Belfast, Image and Vision Group, School of EECS, Northern Ireland Science Park, Queen's Road, Queen's Island, Belfast BT3 9DT, United Kingdom;Queen's University Belfast, Image and Vision Group, School of EECS, Northern Ireland Science Park, Queen's Road, Queen's Island, Belfast BT3 9DT, United Kingdom
Venue:
Pattern Recognition Letters
Year:
2007

Citing 4
Cited 2

Minimum error thresholding

Pattern Recognition
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Automated Processing of Shoeprint Images Based on the Fourier Transform for Use in Forensic Science

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Rotation and intensity invariant shoeprint matching using Gabor transform with application to forensic science

Pattern Recognition
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Abstract

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.