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Ribas LC, Gonçalves DN, Oruê JPM, Gonçalves WN. Fractal dimension of maximum response filters applied to texture analysis. Pattern Recognit Lett 2015. [DOI: 10.1016/j.patrec.2015.07.030] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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ROOMI SMOHAMEDMANSOOR, RAJA R, KALAIYARASI D. COMPUTING IMAGE TEXTURE BY ISOPATTERN. INT J PATTERN RECOGN 2014. [DOI: 10.1142/s0218001414540032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Texture is an important feature that aids in identifying objects of interest or region of interest irrespective of the source of the image. In this paper, a novel and simple isopattern-based texture feature is introduced. Spatial gray scale dependencies represented by bit plane is analyzed for specific patterns and are accumulated in bins. These are scaled by half-normal weighting function to provide isopattern texture feature. The ability of this texture feature in capturing textural variations of the images despite the presence of illumination, scale and rotation is demonstrated by conducting texture analysis on Brodatz, OuTex texture datasets and its classification accuracy on Kylberg dataset. The results of these two experimentation indicate that the proposed textural feature picks variation in texture significantly and has a better texture classification accuracy of 98.26% when compared with the state-of-the-art features like Gabor, GLCM and LBP.
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Affiliation(s)
- S. MOHAMED MANSOOR ROOMI
- Department of Electronics and Communication Engineering, Thiagarajar College of Engineering, Madurai-625 015, India
| | - R. RAJA
- Department of Electronics and Communication Engineering, Pandian Saraswathy Yadav Engineering College, Sivagangai-630 561, India
| | - D. KALAIYARASI
- Department of Electronics and Communication Engineering, Thiagarajar College of Engineering, Madurai-625 015, India
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CHEN YUNWEN, CHEN YANQIU. ADAPTIVE IMAGE SEGMENTATION USING ARTIFICIAL CO-EVOLVING TRIBES. INT J PATTERN RECOGN 2011. [DOI: 10.1142/s0218001407005922] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Deriving from the artificial life theory, this paper proposes an artificial co-evolving tribes model and applies it to solve the image segmentation problem. During the evolution process, the individuals in this model making up the tribes effect communication cooperatively from one agent to the other in order to increase the homogeneity of the ensemble of the image regions they represent. Two remarkable properties, that is, the monotone contraction and the conservation of the system are proved. Stability and scale control of the proposed method are carefully analyzed. Experimental results are presented and compared with two latest segmentation methods, both quantitatively and visually. We also discuss the results matching with human visual perception.
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Affiliation(s)
- YUN WEN CHEN
- Department of Computer Science and Engineering, School of Information Science and Engineering, Fudan University, Shanghai 200043, China
| | - YAN QIU CHEN
- Department of Computer Science and Engineering, School of Information Science and Engineering, Fudan University, Shanghai 200043, China
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