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Construction of texture features
2009 Proceedings of 6th International Symposium on Image and Signal Processing and Analysis, 2009One well-known and effective method used for computationally efficient texture classification is the use of statistical information on 3×3 pixel blocks such as local binary patterns (LBP). However, there has been negligible research on sizes of pixel blocks beyond 3×3 while using the histogram approach. Specifically, larger or non-square features might
A. Oerlemans, null Qi Zhang, M.S. Lew
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Applications of Texture Features
2019Texture 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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Texture Defect Detection Using Invariant Textural Features
2001In this paper we propose a novel method for the construction of invariant textural features for grey scale images. The textural features are based on an averaging over the 2D Euclidean transformation group with relational kernels. They are invariant against 2D Euclidean motion and strictly increasing grey scale transformations.
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2004
This paper introduces feature-based textures, a new image representation that combines features and texture samples for high-quality texture mapping. Features identify boundaries within a texture where samples change discontinuously. They can be extracted from vector graphics representations, or explicity added to raster images to improve sharpness ...
Ganesh Ramanarayanan +2 more
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This paper introduces feature-based textures, a new image representation that combines features and texture samples for high-quality texture mapping. Features identify boundaries within a texture where samples change discontinuously. They can be extracted from vector graphics representations, or explicity added to raster images to improve sharpness ...
Ganesh Ramanarayanan +2 more
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2019
Structural methods depict texture through well-defined primitives and a structure of those primitives’ spatial relationships.
Jyotismita Chaki, Nilanjan Dey
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Structural methods depict texture through well-defined primitives and a structure of those primitives’ spatial relationships.
Jyotismita Chaki, Nilanjan Dey
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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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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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Multi-Scale Boosting Feature Encoding Network for Texture Recognition
IEEE Transactions on Circuits and Systems for Video Technology, 2021Kaiyou Song, Hua Yang, Zhouping Yin
exaly
LTGH: A Dynamic Texture Feature for Working Condition Recognition in the Froth Flotation
IEEE Transactions on Instrumentation and Measurement, 2021Jin Luo, Zhaohui Tang, Hu Zhang
exaly