Abstract
The problem considered in this article is how to solve the image correspondence problem in cases where it is important to measure changes in the contour, position, and spatial orientation of bounded regions. This article introduces a computational intelligence approach to the solution of this problem with anisotropic (direction dependent) wavelets and a tolerance near set approach to detecting similarities in pairs of images. Near sets are a recent generalization of rough sets introduced by Z. Pawlak during the early 1980s. Near sets resulted from a study of the perceptual basis for rough sets. Pairs of sets containing objects with similar descriptions are known as near sets. The proposed wavelet-based image nearness measure is compared with F. Hausdorff and P. Mahalanobis image distance measures. The results of three wavelet-based image resemblance measures for several well-known images, are given. A direct benefit of this research is an effective means of grouping together (classifying) images that corr...
Highlights
This paper introduces a wavelet-based near set approach to solving the image correspondence problem, i.e., where one uses anisotropic wavelets to establish a correspondence between pairs of images
Similarities between images can be measured by taking into account image features such as contour, spatial orientation and position of line segments along bounded regions contained in sample images
Anisotropic wavelets offer a straightforward approach to measuring changes in features of image objects, e.g., object contour lengths, spatial orientation, position and wavelet coefficient
Summary
This paper introduces a wavelet-based near set approach to solving the image correspondence problem, i.e., where one uses anisotropic (direction dependent) wavelets to establish a correspondence between pairs of images. The proposed approach to measuring image similarity stems from recent work on anisotropic wavelets [46,47,48], image correspondence [13,16,18, 37, 39] and near sets [16, 26, 34,35,36,37, 39, 44]. Similarities between images can be measured by taking into account image features such as contour, spatial orientation and position of line segments along bounded regions contained in sample images.
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