Results 131 to 140 of about 75,969 (161)

Vector quantization for texture classification

IEEE Transactions on Systems, Man, and Cybernetics, 1993
A method for classifying and coding textures that is based upon transform vector quantization is presented. Techniques for texture classification and vector quantization similarly process small, nonoverlapping blocks of image data. Local spatial frequency features have been identified as being appropriate for texture classification, indicating that a ...
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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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Relational Features for Texture Classification

2011
Texture features play an important role in facilitating various applications, for instance, image retrieval and object recognition. In this work, we investigate the relational features as a texture descriptor in classifying materials and visual textures from their appearance.
Wan Nural Jawahir Hj Wan Yussof, Hans Burkhardt   +1 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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A Comparison on Textured Motion Classification

2006
Textured motion – generally known as dynamic or temporal texture – analysis, classification, synthesis, segmentation and recognition is popular research areas in several fields such as computer vision, robotics, animation, multimedia databases etc.
Kaan Öztekin, Gozde Bozdagi Akar
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An adaptive model for texture classification

Proceedings 15th International Conference on Pattern Recognition. ICPR-2000, 2002
This paper presents an adaptive texture model for texture classification. In this model, a texture is considered containing both structural and stochastic components. These two components are indeterministic and deterministic parts as in the Wold texture model that are represented by Gaussian Markov random field (GMRF) model and multichannel filtering ...
Yong Huang, Kap Luk Chan, ZhongYang Huang   +2 more
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A Novel Texture Sensor for Fabric Texture Measurement and Classification

IEEE Transactions on Instrumentation and Measurement, 2014
Surface texture is one of the important cues for human beings to identify different fabrics. This paper presents a novel design of a surface texture sensor by imitating human active texture perception by touch. A thin polyvinylidene fluoride (PVDF) film is used as the sensitive element to fabricate a high-accuracy, high-speed-response fabric surface ...
Aiguo Song, Yezhen Han, Haihua Hu, Jianqing Li 0006   +3 more
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Dictionary Learning in Texture Classification

2011
Texture analysis is used in numerous applications in various fields. There have been many different approaches/techniques in the literature for texture analysis among which the texton-based approach that computes the primitive elements representing textures using k-means algorithm has shown great success. Recently, dictionary learning and sparse coding
Mehrdad J. Gangeh, Ali Ghodsi 0001, Mohamed S. Kamel   +2 more
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Review of Texture Descriptors for Texture Classification

2017
Texture classification is a process of distinguishing or classifying different textures into separate classes. Finding an efficient texture descriptor is a vital step for performing accurate texture classification. The research area of texture classification is widely investigated in several computer vision and pattern recognition problems.
Philomina Simon, V. Uma
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Distributed learning of texture classification

1990
A large number of statistical measures have been postulated for the description and discrimination of textures. While most are useful in some situations, none are totally effective in all of them. An alternative approach is to learn which measures are best for particular circumstances.
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