Adaptive Thresholding Based on Co-occurrence Matrix Edge Information

In this paper, an adaptive thresholding technique based on gray level co-occurrence matrix (GLCM) is presented to handle images with fuzzy boundaries. As GLCM contains information on the distribution of gray level transition frequency and edge information, it is very useful for the computation of threshold value. Here the algorithm is designed to have flexibility on the edge definition so that it can handle the object's fuzzy boundaries. By manipulating information in the GLCM, a statistical feature is derived to act as the threshold value for the image segmentation process. The proposed method is tested with the starfruit defect images. To demonstrate the ability of the proposed method, experimental results are compared with three other thresholding techniques

[1]  Chein-I Chang,et al.  Image segmentation by local entropy methods , 1995, Proceedings., International Conference on Image Processing.

[2]  Shi-Jinn Horng,et al.  Entropy thresholding and its parallel algorithm on the reconfigurable array of processors with wider bus networks , 1999, IEEE Trans. Image Process..

[3]  Chien-Hsing Chou,et al.  Learning to binarize document images using a decision cascade , 2005, IEEE International Conference on Image Processing 2005.

[4]  Hui Zhu,et al.  Adaptive thresholding by variational method , 1998, IEEE Trans. Image Process..

[5]  Y. Ebrahim.  Entropy based thresholding of cross-dissolved ultrasound images , 2003, CCECE 2003 - Canadian Conference on Electrical and Computer Engineering. Toward a Caring and Humane Technology (Cat. No.03CH37436).

[6]  Josef Kittler,et al.  Minimum error thresholding , 1986, Pattern Recognit..

[7]  Andrew K. C. Wong,et al.  A new method for gray-level picture thresholding using the entropy of the histogram , 1985, Comput. Vis. Graph. Image Process..

[8]  F. Parmiggiani,et al.  An investigation of the textural characteristics associated with gray level cooccurrence matrix statistical parameters , 1995, IEEE Transactions on Geoscience and Remote Sensing.

[9]  Chein-I Chang,et al.  A relative entropy-based approach to image thresholding , 1994, Pattern Recognit..

[10]  Akio Shio.  An automatic thresholding algorithm based on an illumination-independent contrast measure , 1989, Proceedings CVPR '89: IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[11]  David L. Milgram,et al.  Region extraction using convergent evidence , 1979 .

[12]  P. D. Thouin,et al.  Survey and comparative analysis of entropy and relative entropy thresholding techniques , 2006 .

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

[14]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[15]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[16]  Alfred M. Bruckstein,et al.  A new method for image segmentation , 1988, [1988 Proceedings] 9th International Conference on Pattern Recognition.

[17]  Ching Y. Suen,et al.  A threshlod selection method based on multiscale and graylevel co-occurrence matrix analysis , 2005, Eighth International Conference on Document Analysis and Recognition (ICDAR'05).

[18]  Zhonghua liu,et al.  Edge detection and automatic threshold based on wavelet transform in the VPPAW keyhole image processing , 2000, Conference Record of the 2000 IEEE Industry Applications Conference. Thirty-Fifth IAS Annual Meeting and World Conference on Industrial Applications of Electrical Energy (Cat. No.00CH37129).

[19]  Richard W. Conners,et al.  A Theoretical Comparison of Texture Algorithms , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  Xiao-Ping Zhang,et al.  Segmentation of bright targets using wavelets and adaptive thresholding , 2001, IEEE Trans. Image Process..

[21]  Hui Tian,et al.  Implementing Otsu's thresholding process using area-time efficient logarithmic approximation unit , 2003, Proceedings of the 2003 International Symposium on Circuits and Systems, 2003. ISCAS '03..