Tolerance spaces

Shreider, Yu. A.

doi:10.1007/bf01070514

“…non‐empty intersections between TCs. The use of tolerance spaces in this work is directly related to recent work on tolerance spaces (Hassanien et al , 2009; Peters, 2009a, b, 2010; Peters and Ramanna, 2009; Gerasin et al , 2008; Zheng et al , 2005; Bartol et al , 2004; Skowron and Stepaniuk, 1996; Schroeder and Wright, 1992; Shreider, 1970; Pal and Peters, 2010).…”

Section: Definitionmentioning

confidence: 99%

Perception‐based image classification

Henry

¹

,

Peters

²

2010

International Journal of Intelligent Computing and Cybernetics

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Pattern classification methodologies are present in many systems that we depend on daily. In these systems, classes are created based on human perception of the objects being classified. Thus, it is important to have systems that accurately model human perception. Near set theory provides a framework for measuring the similarity of objects based on features that describe them in much the same way that humans perceive objects. In this paper, we show that the near set approach can be used to classify images. Further, the results presented here suggest that the near set approach can be used in any image classification system. The contribution of this article is a perception based classification of images using near sets.

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“…Hence, the basic idea in a tolerance space view of images, for example, is to replace the indiscerniblity relation in rough sets [31] with a tolerance relation in partitioning images into homologous regions where there is a high likelihood of overlaps, i.e., non-empty intersections between image tolerance classes. The use of image tolerance spaces in this work is directly linked to recent work on tolerance spaces [9,11,13,38,39,43,[52][53][54]59]. The contribution of this article is the introduction of two new tolerance space-based image resemblance measures and a comparison of the new measures with the original Henry-Peters nearness measure.…”

Section: Tolerance Space For Image Recognitionmentioning

confidence: 99%

Anisotropic Wavelet-Based Image Nearness Measure

Peters¹,

Puzio²

2009

IJCIS

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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 correspond to each other relative to minuscule similarities in the contour, position, and spatial orientation of bounded regions in the images, especially in videos containing image sequences showing varied object movements. The contribution of this article is the introduction of an anisotropic wavelet-based measure of image resemblance using a near set approach.

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“…Hence, the basic idea in a tolerance space view of images, for example, is to replace the indiscerniblity relation in rough sets [31] with a tolerance relation in partitioning images into homologous regions where there is a high likelihood of overlaps, i.e., non-empty intersections between image tolerance classes. The use of image tolerance spaces in this work is directly linked to recent work on tolerance spaces [9,11,13,38,39,43,[52][53][54]59]. The contribution of this article is the introduction of two new tolerance space-based image resemblance measures and a comparison of the new measures with the original Henry-Peters nearness measure.…”

Section: Tolerance Space For Image Recognitionmentioning

confidence: 99%

Anisotropic Wavelet-Based Image Nearness Measure

Peters¹,

Puzio²

2009

IJCIS

View full text Add to dashboard Cite

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 correspond to each other relative to minuscule similarities in the contour, position, and spatial orientation of bounded regions in the images, especially in videos containing image sequences showing varied object movements. The contribution of this article is the introduction of an anisotropic wavelet-based measure of image resemblance using a near set approach.

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Tolerance spaces

Cited by 7 publications

References 0 publications

Perception‐based image classification

Perception‐based image classification

Anisotropic Wavelet-Based Image Nearness Measure

Anisotropic Wavelet-Based Image Nearness Measure

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