We have developed an approach to the separation of background texture and structures in images. The developed approach is based on the statistical difference between local and median co-occurrence matrices. It is our assertion that the classification of mammographic parenchymal patterns can be improved if anatomical structures can be removed from the image and the classification is based only o...

The periodicity of a texture is one of its important visual characteristics. The inertias of co-occurrence matrices of the texture have been often used to detect the visual periodicity. However, it is time-consuming to explicitly construct these matrices. In this paper, we propose the distance matching function to avoid constructing the matrices due to our new interpretation of an inertia. For ...

Spatial information is an important cue for visual object analysis. Various studies in this field have been conducted. However, they are either too rigid or too fragile to efficiently utilize such information. In this paper, we propose to model the distribution of objects' local appearance patterns by using their cooccurrence at different spatial locations. In order to represent such a distribu...

Domain experts are frequently interested to analyze multiple related spatial datasets. This capability is important for change analysis and contrast mining. In this paper, a novel clustering approach called correspondence clustering is introduced that clusters two or more spatial datasets by maximizing cluster interestingness and correspondence between clusters derived from different datasets. ...

A critical shortcoming of determining texture features derived from grey-level co-occurrence matrices (GLCM’s) is the excessive computational burden. This paper describes the implementation of a linked-list algorithm to determine co-occurrence texture features far more efficiently. Behavior of common co-occurrence texture features across difference grey-level quantizations is investigated.

The main purpose of this paper is to consider the t-best co-approximation and t-best simultaneous co-approximation in fuzzy normed spaces. We develop the theory of t-best co-approximation and t-best simultaneous co-approximation in quotient spaces. This new concept is employed us to improve various characterisations of t-co-proximinal and t-co-Chebyshev sets.

The magnetic properties of ColCu multilayers were investigated from 5 to 380 K and analyzed in terms of the interFace magnetism and mean-field model. The interface, which is about 1.2 w thick, is nonmagnetic at room temperature and becomes magnetic at 5 K with the average magnetization of 40% of the pure Co magnetization. The samples of X w Co/l0 A Cu behaved superparamagnetically as X ranged f...

1. Over the last 30 years there has been a great deal of interest in investigating patterns of species co-occurrence across a number of locations, which has led to the development of numerous methods to determine whether there is evidence that a particular pattern may not have occurred by random chance. 2. A key aspect that seems to have been largely overlooked is the possibility that species m...

Mass abnormality segmentation is a vital step for the medical diagnostic process and is attracting more and more the interest of many research groups. Currently, most of the works achieved in this area have used the Gray Level Co-occurrence Matrix (GLCM) as texture features with a region-based approach. These features come in previous phase for segmentation stage or are using as inputs to class...