Results 291 to 300 of about 48,049 (318)

Unsupervised segmentation of textured images

Information Sciences, 1996
Abstract In this paper, a new unsupervised segmentation algorithm for textured images is developed. The proposed algorithm utilizes the characteristic of the Markov random fields (MRF) for modeling the contextual information embedded in image formation.
Jaehyun Park, Ludwik Kurz
openaire   +1 more source

Unsupervised Color-Texture Segmentation

2004
An improved approach for JSEG is presented for unsupervised color image segmentation. Instead of color quantization, an automatic classification method based on adaptive mean shift (AMS) based clustering is used for nonparametric clustering of image data set.
Yuzhong Wang, Jie Yang 0002, Yue Zhou 0005   +2 more
openaire   +1 more source

Automatic texture segmentation for texture-based image retrieval

10th International Multimedia Modelling Conference, 2004. Proceedings., 2004
Texture-segmentation is the crucial initial step for texture-based image retrieval. Texture is the main difficulty faced to a segmentation method. Many image segmentation algorithms either can’t handle texture properly or can’t obtain texture features directly during segmentation which can be used for retrieval purpose.
Ying Liu 0026, Xiaofang Zhou 0001
openaire   +2 more sources

Color and Texture Image Segmentation

2012
For applications, such as image recognition or scene understanding, we cannot process the whole image directly for the reason that it is inefficient and unpractical. Therefore, to reduce the complexity of the recognition of the image, segmentation is a necessary step.
Chitti Kokil Kumar, Arun Agarwal, Chillarige Raghavendra Rao   +2 more
openaire   +1 more source

Model-Based Texture Segmentation

2004
An efficient and robust type of unsupervised multispectral texture segmentation method is presented. Single decorrelated monospectral texture factors are assumed to be represented by a set of local Gaussian Markov random field (GMRF) models evaluated for each pixel centered image window and for each spectral band.
Michal Haindl, Stanislav Mikes
openaire   +1 more source

Unsupervised Dynamic Textures Segmentation

2013
This paper presents an unsupervised dynamic colour texture segmentation method with unknown and variable number of texture classes. Single regions with dynamic textures can furthermore change their location as well as their shape. Individual dynamic multispectral texture mosaic frames are locally represented by Markovian features derived from four ...
Michal Haindl, Stanislav Mikes
openaire   +1 more source

Textural segmentation, second-order statistics, and textural elements

Biological Cybernetics, 1983
Beck (1972, 1973) hypothesized that textural segmentation occurs strongly on the basis of simple properties such as brightness, color, size, and the slopes of contours and lines of the elemental descriptors of a texture or textural elements. The experiment reported supports the hypothesis that specific stimulus features, rather than second-order ...
openaire   +2 more sources

Texture classification and segmentation using multiresolution simultaneous autoregressive models

Pattern Recognition, 1992
Jianchang Mao, Anil K Jain
exaly  

Unsupervised texture segmentation using Gabor filters

Pattern Recognition, 1991
Anil K Jain
exaly  

Segmentation of kidney from ultrasound images based on texture and shape priors

IEEE Transactions on Medical Imaging, 2005
Yifeng Jiang, Hung-Tat Tsui
exaly