Results 261 to 270 of about 100,090 (313)

Jordan features for texture segmentation

Proceedings of Third International Conference on Electronics, Circuits, and Systems, 2002
This paper deals with the application to texture segmentation of two concepts, namely the Jordan Decomposition Theorem, which says that any bounded variation function can be expressed as the difference of two non-decreasing functions, and the Peano-scan, which is a 1D traversal of square domains.
Dinu Coltuc, Jean-Marie Becker, Vasile Buzuloiu   +2 more
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Discriminative features for texture description

Pattern Recognition, 2012
In this paper, a feature extraction method is developed for texture description. To obtain discriminative patterns, we present a learning framework which is formulated into a three-layered model. It can estimate the optimal pattern subset of interest by simultaneously considering the robustness, discriminative power and representation capability of ...
Zhao Guoying, Pietikäinen Matti, Guo Yimo   +2 more
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Feature extraction for texture classification

Pattern Recognition, 1980
Abstract We address the problem of texture classification. Random walks are simulated for plane domains A bounded by absorbing boundaries Γ, and the absorption distributions are estimated. Measurements derived from the above distributions are the features used for texture classification.
Harry Wechsler, Todd K. Citron
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Statistical geometrical features for texture classification [PDF]

Pattern Recognition, 1995
Abstract This paper proposes a novel set of 16 features based on the statistics of geometrical attributes of connected regions in a sequence of binary images obtained from a texture image. Systematic comparison using all the Brodatz textures shows that the new set achieves a higher correct classification rate than the well-known Statistical Gray ...
Yan Qiu Chen, Mark S. Nixon, David W. Thomas   +2 more
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Textural Features for Steganalysis

2013
It is observed that the co-occurrence matrix, one kind of textural features proposed by Haralick et al., has played a very critical role in steganalysis. On the other hand, the data hidden in the image texture area has been known difficult to detect for years, and the modern steganographic schemes tend to embed data into complicated texture area where ...
Yun Q. Shi 0001, Patchara Sutthiwan, Licong Chen   +2 more
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Texture features and learning similarity

Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1996
This paper addresses two important issues related to texture pattern retrieval: feature extraction and similarity search. A Gabor feature representation for textured images is proposed, and its performance in pattern retrieval is evaluated on a large texture image database. These features compare favorably with other existing texture representations. A
Wei-Ying Ma, B. S. Manjunath
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Feature Analysis and Texture Synthesis

2007 10th IEEE International Conference on Computer-Aided Design and Computer Graphics, 2007
Most texture synthesis algorithms explicitly or implicitly adopt Markov random field or similar distribution as their basic model to guide the synthesis process. However, MRF-like models can 't handle textures well with large scale structure or unstable structure due to their inherent local and stable assumptions. To make improvement in this regard, we
Yuanting Gu, Enhua Wu
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Feature-Based Texture Synthesis

2005
We introduce a new method for texture synthesis on regular and irregular example textures. In this paper, an enhanced patch-based algorithm is proposed to select patches with the best structural similarity and to avoid discontinuity at the boundary of adjacent patches.
Tong-Yee Lee, Chung-Ren Yan
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Texture Feature Extraction and Classification

2001
This paper describes a novel technique for texture feature extraction and classification. The proposed feature extraction technique uses an Auto-Associative Neural Network (AANN) and the classification technique uses a Multi-Layer Perceptron (MLP) with a single hidden layer. The two approaches such as AANN-MLP and statistical-MLP were investigated. The
Brijesh K. Verma, Siddhivinayak Kulkarni
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Texture discrimination based on an optimal utilization of texture features

Pattern Recognition, 1988
Abstract We present a new approach to texture discrimination based on an algorithm which automatically selects the texture features best suited to a particular classification problem. Promising results are obtained when applying the method to the discrimination of 10 Brodatz's features.
Dong-Chen He, Li Wang 0002, Jean Guibert
openaire   +1 more source