Defining A Fuzzy Partition for Coarseness Modelling in Texture Images

In this paper, the texture feature "coarseness" is mod- elled by means of a fuzzy partition on the domain of coarseness mea- sures. The number of linguistic labels to be used, and the parameters of the membership functions associated to each fuzzy set are calcu- lated relating representative coarseness measures (our reference set) with the human perception of this texture property. A wide variety of measures is studied, analyzing its capability to discriminate dif- ferent coarseness categories. Data about the human perception of fineness is collected by means of a pools, performing an aggregation of their assessments by means of OWA operators. This information is used to obtain a fuzzy partition adapted to the human perception of coarseness-fineness

[1]  Joseph Naor,et al.  Multiple Resolution Texture Analysis and Classification , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Madasu Hanmandlu,et al.  A fuzzy approach to texture segmentation , 2004, International Conference on Information Technology: Coding and Computing, 2004. Proceedings. ITCC 2004..

[3]  A. Rosenfeld,et al.  IEEE TRANSACTIONS ON SYSTEMS , MAN , AND CYBERNETICS , 2022 .

[4]  Chih-Yi Chiu,et al.  Finding textures by textual descriptions, visual examples, and relevance feedbacks , 2003, Pattern Recognit. Lett..

[5]  Mary M. Galloway,et al.  Texture analysis using gray level run lengths , 1974 .

[6]  A. Tamhane,et al.  Multiple Comparison Procedures , 2009 .

[7]  Hideyuki Tamura,et al.  Textural Features Corresponding to Visual Perception , 1978, IEEE Transactions on Systems, Man, and Cybernetics.

[8]  Brijesh Verma,et al.  Fuzzy logic based texture queries for CBIR , 2003, Proceedings Fifth International Conference on Computational Intelligence and Multimedia Applications. ICCIMA 2003.

[9]  Belur V. Dasarathy.  Data Mining and Knowledge Discovery: Theory, Tools, and Technology VI , 2004 .

[10]  William G. Wee,et al.  Neighboring gray level dependence matrix for texture classification , 1982, Comput. Graph. Image Process..

[11]  Chandrika Kamath,et al.  Retrieval using texture features in high-resolution multispectral satellite imagery , 2004, SPIE Defense + Commercial Sensing.

[12]  A. Tamhane,et al.  Multiple Comparison Procedures , 1989 .

[13]  Eduard Montseny,et al.  Fuzzy Texture Unit and Fuzzy Texture Spectrum for texture characterization , 2007, Fuzzy Sets Syst..

[14]  Azriel Rosenfeld,et al.  A Comparative Study of Texture Measures for Terrain Classification , 1975, IEEE Transactions on Systems, Man, and Cybernetics.

[15]  H. Yoshida,et al.  Wavelet-packet-based texture analysis for differentiation between benign and malignant liver tumours in ultrasound images. , 2003, Physics in medicine and biology.

[16]  Shengrui Wang,et al.  Autocovariance-based perceptual textural features corresponding to human visual perception , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[17]  Robert King,et al.  Textural features corresponding to textural properties , 1989, IEEE Trans. Syst. Man Cybern..

[18]  William G. Wee,et al.  Neighboring gray level dependence matrix for texture classification , 1982, Comput. Graph. Image Process..

[19]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  M.,et al.  Statistical and Structural Approaches to Texture , 2022 .

[21]  José M. Soto-Hidalgo,et al.  Using Fuzzy Sets for Coarseness Representation in Texture Images , 2007, IFSA.

[22]  Sunhyo Kim,et al.  Texture classification using run difference matrix , 1991, IEEE 1991 Ultrasonics Symposium,.

[23]  Ronald R. Yager,et al.  On ordered weighted averaging aggregation operators in multicriteria decisionmaking , 1988, IEEE Trans. Syst. Man Cybern..