A Structural Approach To Identify Defects In Textured Images

From a structural point of view, a textured image is considered to be composed of skeleton and background primitives which occur repeatedly according to certain placement rule. Identification of defects in textured images is an important topic in computer vision. In this paper, an approach to defect detection is developed. A textured image is first thresholded using histogram analysis. Then it is mapped into a special data structure called the skeleton representation. Based on both location and length histograms from the data structure, several statistical measurements are defined. These measurements are used to identify and locate defects such as fluctuation, mean jump, and end influency in the textured image. The experimental results show satisfactory performance of this approach and its potential usefulness in industrial environments.

[1]  Wei-Chung Lin,et al.  Metal surface inspection using image processing techniques , 1984, IEEE Transactions on Systems, Man, and Cybernetics.

[2]  Anil K. Jain,et al.  Markov Random Field Texture Models , 1983, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Yoshiaki Shirai,et al.  Description of Textures by a Structural Analysis , 1979, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Anil K. Jain,et al.  A spatial filtering approach to texture analysis , 1985, Pattern Recognit. Lett..

[5]  Michael Unser,et al.  Feature extraction and decision procedure for automated inspection of textured materials , 1984, Pattern Recognit. Lett..

[6]  Béla Julesz,et al.  Visual Pattern Discrimination , 1962, IRE Trans. Inf. Theory.

[7]  Azriel Rosenfeld,et al.  Mosaic Models for Textures , 1981, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  A. Rosenfeld,et al.  Visual texture analysis , 1970 .

[9]  Michael S. Landy,et al.  Vectorgraph coding: Efficient coding of line drawings , 1985, Comput. Vis. Graph. Image Process..

[10]  Robert M. Haralick,et al.  Structural pattern recognition, homomorphisms, and arrangements , 1978, Pattern Recognit..

[11]  Catherine Garbay,et al.  Image Structure Representation and Processing: A Discussion of Some Segmentation Methods in Cytology , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  King-Sun Fu,et al.  A syntactic approach to texture analysis , 1978 .

[13]  Ramakant Nevatia,et al.  Structural Analysis of Natural Textures , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  N. Gramenopoulos.  Terrain type recognition using ERTS-1 MSS images , 1973 .

[15]  Bruce H. McCormick,et al.  Time series model for texture synthesis , 2004, International Journal of Computer & Information Sciences.

[16]  S. Zucker.  Toward a model of texture , 1976 .

[17]  William G. Wee,et al.  Detecting the spatial structure of natural textures based on shape analysis , 1985, Comput. Vis. Graph. Image Process..

[18]  R.M. Haralick,et al.  Statistical and structural approaches to texture , 1979, Proceedings of the IEEE.