Results 261 to 270 of about 371,752 (272)

Affine-invariant texture classification

Proceedings 15th International Conference on Pattern Recognition. ICPR-2000, 2002
In content-based image retrieval, a texture pattern may appear in a wide range of 3D views. Affine transformation is an approximation frequently used in practice to represent the variation of a pattern. The existing approaches to texture classification cannot cope with this variation.
D. Chetverikov, Z. Foldvari
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Texture Classification by ICA

2007 International Symposium on Signals, Circuits and Systems, 2007
ICA (Independent Component Analysis) is a mathematical tool traditionally employed for source separation. In this paper, we test its ability for texture analysis, in order to provide a new texture classification method. From the multitude of the existing algorithms, we have chosen FastICA, a version based on the forth order statistics of the analyzed ...
Daniela Coltuc, Thierry Fournel, Jean-Marie Becker   +2 more
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TEXTURAL CLASSIFICATION OF SEDIMENTS

Sedimentology, 1966
SUMMARYIn the light of present‐day requirements and the author's personal experience, existing systems of nomenclature and classification of sediments on a textural basis need revision. A system is proposed that is based on a three end‐member relationship involving sand‐, silt‐ and clay‐size particles.
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Rotationally invariant texture classification

IEE Seminar on Time-Scale and Time-Frequency Analysis and Applications, 2000
Texture based features used for content based retrieval of images and videos should ideally be invariant to simple transforms such as rotation. This paper introduces the recently developed dual tree complex wavelet transform (DT-CWT) as a tool to extract rotationally invariant texture based features.
Hill, Paul R, Bull, David, Canagarajah, CN   +2 more
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Cost-sensitive texture classification

2014 IEEE Congress on Evolutionary Computation (CEC), 2014
Texture recognition plays an important role in many computer vision tasks including segmentation, scene understanding and interpretation, medical imaging and object recognition. In some situations, the correct identification of particular textures is more important compared to others, for example recognition of enemy uniforms for automatic defense ...
Gerald Schaefer, Bartosz Krawczyk, Niraj P. Doshi, Tomoharu Nakashima   +3 more
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Texture Classification and Segmentation

2011
The first part of this chapter provides an introduction to the most common texture image test sets and overviews some texture classification experiments involving LBP descriptors. An unsupervised method for texture segmentation using LBP and contrast (LBP/C) distributions is presented in the second part of the chapter.
Matti Pietikäinen, Abdenour Hadid, Guoying Zhao, Timo Ahonen   +3 more
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Classification of Rock Textures

2012
This paper presents a novel method for the classification of rocks into the three major categories, namely, igneous, sedimentary and metamorphic. Each of these rock types has various sub-types too. The various Tamura Features are formulated and calculated from the input image.
Thiagarajan Harinie, I. Janani Chellam, S. B. Sathya Bama, S. Raju, V. Abhaikumar   +4 more
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Texture Classification

2020
Li Liu, Matti Pietikäinen
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Texture Models and Classification

1995
In contrast to the last chapter, regions of pixels will now be classified as belonging to particular types or classes of texture. There are numerous deterministic or probabilistic approaches to classification, and in particular, to texture classification. We restrict our attention to some model-based methods.
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Texture classification

2023
Pietikäinen Matti, Ojala Timo
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