Results 241 to 250 of about 578,151 (295)

Texture feature extraction

Pattern Recognition Letters, 1987
Abstract We present a new approach to texture feature extraction from a cooccurrence matrix. Computationally, the method is much faster than traditional uses of cooccurrence matrices. Using Brodatz's textures, the proposed features are evaluated and compared with those suggested by Conners et al. (1984).
Dong-Chen He, Li Wang 0002, Jean Guibert
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Feature-preserving procedural texture

The Visual Computer, 2017
This paper presents how to synthesize a texture in a procedural way that preserves the features of the input exemplar. The exemplar is analyzed in both spatial and frequency domains to be decomposed into feature and non-feature parts. Then, the non-feature parts are reproduced as a procedural noise, whereas the features are independently synthesized ...
HyeongYeop Kang, JungHyun Han
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Textural Features for Image Classification

IEEE Transactions on Systems, Man, and Cybernetics, 1973
Texture is one of the important characteristics used in identifying objects or regions of interest in an image, whether the image be a photomicrograph, an aerial photograph, or a satellite image. This paper describes some easily computable textural features based on gray-tone spatial dependancies, and illustrates their application in category ...
Robert M. Haralick, Karthikeyan S. Shanmugam, Its'hak Dinstein   +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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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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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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Texture Features and Image Texture Models

2019
Image texture is an important phenomenon in many applications of pattern recognition and computer vision. Hence, several models for deriving texture properties have been proposed and developed. Although there is no formal definition of image texture in the literature, image texture is usually considered the spatial arrangement of grayscale pixels in a ...
Chih-Cheng Hung, Enmin Song, Yihua Lan
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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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