Quantitative analysis of properties and spatial relations of fuzzy image regions

Krishnapuram, Raghu; Keller, James M.; Ma, Yue

doi:10.1109/91.236554

Cited by 161 publications

(92 citation statements)

References 17 publications

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“…In order to get more flexibility, we introduce the notion of approximate (or fuzzy) visibility. It extends both the crisp definition of visibility and the definition proposed in [12] in the sense that the information is not reduced to an average angle. This is achieved by relaxing the admissibility to semi-admissibility through the introduction of an intermediary point on the segments.…”

Section: B Fuzzy Visibilitymentioning

confidence: 90%

“…5. Example where the definition of [12] hardly corresponds to intuition (2 corresponds to the average angle and is significantly smaller that , while B would be intuitively considered completely between A and A ). and in the 2-D space is computed based on a relation between points.…”

Section: B Fuzzy Set Literaturementioning

confidence: 99%

“…In the definition of [12], the degree to which an object is between two objects () proposed in [12] ( is a parameter expressing the tolerance in the idea of between). Fig.…”

Section: B Fuzzy Set Literaturementioning

confidence: 99%

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On the ternary spatial relation "Between"

Bloch¹,

Colliot

César

2006

IEEE Trans. Syst., Man, Cybern. B

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Abstract-The spatial relation "between" is a notion which is intrinsically both fuzzy and contextual, and depends, in particular, on the shape of the objects. The literature is quite poor on this and the few existing definitions do not take into account these aspects. In particular, an object that is in a concavity of an object 1 not visible from an object 2 is considered between 1 and 2 for most definitions, which is counter intuitive. Also, none of the definitions deal with cases where one object is much more elongated than the other. Here, we propose definitions which are based on convexity, morphological operators, and separation tools, and a fuzzy notion of visibility. They correspond to the main intuitive exceptions of the relation. We distinguish between cases where objects have similar spatial extensions and cases where one object is much more extended than the other. Extensions to cases where objects, themselves, are fuzzy and to three-dimensional space are proposed as well. The original work proposed in this paper covers the main classes of situations and overcomes the limits of existing approaches, particularly concerning nonvisible concavities and extended objects. Moreover, the definitions capture the intrinsic imprecision attached to this relation. The main proposed definitions are illustrated on real data from medical images.Index Terms-Convex hull, fuzzy and three-dimensional (3-D) objects, mathematical morphology, relationship "between", spatial reasoning, structural pattern recognition, visibility.

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Section: B Fuzzy Visibilitymentioning

confidence: 90%

Section: B Fuzzy Set Literaturementioning

confidence: 99%

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On the ternary spatial relation "Between"

Bloch¹,

Colliot

César

2006

IEEE Trans. Syst., Man, Cybern. B

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“…The properties of individual regions can be coupled with spatial relationships between them, to provide more realistic matching between two images. Methodologies to compute spatial relationships of the objects have been proposed by [9], [10], [11]. However the methods mostly deal with the geometric attributes of a region like (area, shape adjacency, surroundedness etc.).…”

Section: Introductionmentioning

confidence: 99%

Content-Based Image Retrieval Using Wavelet Packets and Fuzzy Spatial Relations

Banerjee

Kundu

2006

Computer Vision, Graphics and Image Processing

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Abstract. This paper proposes a region based approach for image retrieval. We develop an algorithm to segment an image into fuzzy regions based on coefficients of multiscale wavelet packet transform. The wavelet based features are clustered using fuzzy C-means algorithm. The final cluster centroids which are the representative points, signify the color and texture properties of the preassigned number of classes. Fuzzy Topological relationships are computed from the final fuzzy partition matrix. The color and texture properties as indicated by centroids and spatial relations between the segmented regions are used together to provide overall characterization of an image. The closeness between two images are estimated from these properties. The performance of the system is demonstrated using different set of examples from general purpose image database to prove that, our algorithm can be used to generate meaningful descriptions about the contents of the images.

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“…The second one is based on projection model such as minimum bounding rectangles [2], and directional relation matrix [3], etc., but these models assimilate an object to the minimum bounding rectangle, so shape, size and distance information also can't be taken into account. The third one is based on angle-histogram model [4]. Given an object of target A and a reference object B, the angle-histogram is computed from the angles between any two points in both objects and normalized by the maximum frequency.…”

Section: Introductionmentioning

confidence: 99%