Results 151 to 160 of about 429,650 (191)

Texture feature coding method for texture classification

Optical Engineering, 2003
We present a new texture analysis method, namely a texture feature coding method (TFCM), for classification of the Brodatz's natural textures. The TFCM is a coding scheme that transforms an image into a feature image, in which each pixel is encoded by TFCM into a texture feature number (TFN) that represents a certain type of local texture.
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Decorrelation Methods of Texture Feature Extraction

IEEE Transactions on Pattern Analysis and Machine Intelligence, 1980
This paper presents the development and evaluation of a visual texture feature extraction method based on a stochastic field model of texture. Results of recent visual texture discrimination experiments are reviewed in order to establish necessary and sufficient conditions for texture features that are in agreement with human discrimination.
O D, Faugeras, W K, Pratt
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Image Texture, Texture Features, and Image Texture Classification and Segmentation

2019
In this chapter, we will discuss the basic concept of image texture, texture features, and image texture classification and segmentation. These concepts will be the foundation to understand image texture models and algorithms used for image texture analysis.
Chih-Cheng Hung, Enmin Song, Yihua Lan
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Circular-Mellin features for texture segmentation

IEEE Transactions on Image Processing, 1995
Texture is an important cue in region-based segmentation of images. We provide an insight into the development of a new set of distortion-invariant texture operators. These "circular-Mellin" operators are invariant to both scale and orientation of the target and represent the spectral decomposition of the image scene in the polar-log coordinate system.
G, Ravichandran, M M, Trivedi
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Model Based Texture Features

2019
Model-based texture analysis attempts to represent an image texture using the stochastic model and generative image model.
Jyotismita Chaki, Nilanjan Dey
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Applications of Texture Features

2019
Texture is a vital visual and the emergent feature for image content explanation. The utilization of object texture is one of the utmost challenging problems in forming effective content-based image retrieval [1].
Jyotismita Chaki, Nilanjan Dey
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Extracting textural features from tactile sensors

Bioinspiration & Biomimetics, 2008
This paper describes an experiment to quantify texture using an artificial finger equipped with a microphone to detect frictional sound. Using a microphone to record tribological data is a biologically inspired approach that emulates the Pacinian corpuscle.
Edwards, JR, Lawry, J, Rossiter, JM, Melhuish, CR   +3 more
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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

2019
This chapter focuses on another image feature called the texture feature. Two types of texture feature methods are discussed: traditional spatial methods and contemporary spectral methods. The chapter first introduces four spatial or handcrafted methods including Tamura, GLCM, MRF, and FD.
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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, Patchara Sutthiwan, Licong Chen   +2 more
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