Results 261 to 270 of about 140,951 (296)

Unsupervised textured image segmentation

[Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing, 1992
A new algorithm for unsupervised textured image segmentation is presented. The image comprises M textured regions, each of which is modeled by a stationary Gaussian Markov random field. A feature vector is computed for each pixel in the original image where these vectors are normally distributed and cluster about some vector means. Thus, the problem is
George K. Gregoriou, Oleh J. Tretiak
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Use Of Texture Operators In Segmentation

Optical Engineering, 1985
In this paper we consider the analysis of complex scenes such as commonly appear in aerial scenes of urban areas. In developing a computer vision system for these scenes we have had to address several problem areas. The first problem area is to structure a vision system so that all possible information is utilized in the analysis of the scene. Examples
Charles A. Harlow, Mohan M. Trivedi, Richard W. Conners   +2 more
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TEXTURE SEGMENTATION: AN UNSUPERVISED APPROACH

International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 1995
The segmentation of scenes into perceptually meaningful partitions has been a basic problem in image understanding, especially when unsupervised methodology has been desired. A novel unsupervised segmentation approach based on texture is developed.
Mukul V. Shirvaikar, Mohan M. Trivedi
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Evolving descriptors for texture segmentation

Pattern Recognition, 1997
A new method for texture segmentation is proposed. In this supervised method, texture descriptors are based on grey level co-occurrences. A texture descriptor corresponds to an individual in a population, and a population corresponds to a given texture class.
Christophe Jacquelin, André Aurengo, Gilles Hejblum   +2 more
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Image segmentation by texture analysis

Proceedings 10th International Conference on Image Analysis and Processing, 2003
Texture segmentation is a significant and primary issue in texture analysis. It is concerned with automatically determining the boundaries between various textured regions in an image. In this paper we propose a region-based method for textured image segmentation. First, we consider the basic problem of texel identification and the determination of its
DI RUBERTO, CECILIA, RODRIGUEZ, GIUSEPPE, VITULANO S.   +2 more
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Visual texture segmentation in cats

Behavioural Brain Research, 1986
The segmentation of visual images on the basis of textural discontinuity was studied in cats using two behavioural paradigms. The first task (Experiment 1) required the animals to detect the presence of a target square of one texture embedded in a background of a second texture.
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Factorization-Based Texture Segmentation

IEEE Transactions on Image Processing, 2015
This paper introduces a factorization-based approach that efficiently segments textured images. We use local spectral histograms as features, and construct an M × N feature matrix using M-dimensional feature vectors in an N-pixel image. Based on the observation that each feature can be approximated by a linear combination of several representative ...
Jiangye Yuan, DeLiang Wang, Anil M. Cheriyadat   +2 more
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Evaluation of texture segmentation algorithms

Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149), 2003
This paper presents a method of evaluating unsupervised texture segmentation algorithms. The control scheme of texture segmentation has been conceptualized as two modular processes: (1) feature computation and (2) segmentation of homogeneous regions based on the feature values.
Kyong I. Chang, Kevin W. Bowyer, Munish Sivagurunath   +2 more
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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
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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
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