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Texture classification and retrieval using random neural network model
Date
2004-03-30
Author
Teke, A
Atalay, Mehmet Volkan
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This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
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URI
https://hdl.handle.net/11511/35264
DOI
https://doi.org/10.1109/iai.2004.1300955
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Department of Computer Engineering, Conference / Seminar
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Texture classification and retrieval using random neural network model
Teke, Alper; Atalay, Mehmet Volkan; Department of Computer Engineering (2003)
Texture is one of the most important characteristics used in computer vision and image processing applications. In this thesis, a new texture classification and retrieval method is proposed for texture analysis applications. The technique makes use of the random neural network model and it is supervised. The main aim is to represent textures with parameters which are the random neural network weights and classify and retrieve textures using this texture definition. The network has neurons that correspond to...
TEXTURE GENERATION WITH THE RANDOM NEURAL NETWORK MODEL
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Texture discrimination and segmentation of remotely sensed imagery by using adaptive subband decomposition
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Texture extraction and reconsturuction from multiple images
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TEXTURE PRESERVING MULTI FRAME SUPER RESOLUTION WITH SPATIALLY VARYING IMAGE PRIOR
Turgay, Emre; Akar, Gözde (2012-10-03)
This paper proposes a new maximum a posteriori (MAP) based super-resolution (SR) image reconstruction method targeting edges and textures in images. Unlike conventional MAP based SR image reconstruction methods a spatially varying image prior is employed which is updated according to the frequency content of the reconstructed image at each iteration at different locations. Two alternative methods based on discrete cosine transforms (DCT) and Gabor filters are proposed for determining the image prior. The pr...
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A. Teke and M. V. Atalay, “Texture classification and retrieval using random neural network model,” 2004, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/35264.