HISTOGRAM AND FUZZY C-MEANS BASED AUTOMATIC THRESHOLD SELECTION FOR EDGE DETECTION PROCESS BASED ON RELATION MATRIX IN COLOR IMAGE

Abstract

Borders of objects and shadows in the image, reflections and lighting changes within objects are named as edge. The image features of the pixel with itself and its neighbors, play a significant role in detection of the edges.  Automatic threshold edge detection algorithms on the similarity image obtained from color images have been proposed in this study. Firstly, the relation matrix based on the similarity feature between neighbor pixels is utilized and the color image is converted into twodimensional similarity image. In the second stage, histogram curve and fuzzy c-means method have been employed to obtain the automatic threshold value. Threshold values obtained by virtue of these two methods have been applied to similarity images obtained separately by Linear, Exponential and Gaussian functions. Visual results have been utilized for the performance evaluations of the two algorithms. Thin edges have been created in the histogram-based edge detection algorithm while distinct and thick edges have been created in the fuzzy c-means algorithm. Clear and distinct edges have been created in linear and exponential functions. The results of the other two methods have been achieved in the Gaussian function, through utilization of the low D coefficient. The edge detection results are within acceptable measures and have responded to high performance and have the feature of to be applicable to large image types. 

Keywords

• Histogram,similarity image,relation matrix,edge detection,fuzzy C-means

References

1. Robinson, G.S., Color edge detection, Opt. Eng., vol. 16, no.5, pp. 479484, Sep/Oct. 1977.
2. Rosenfeld, A., and Kak, A.C., Digital Picture Processing, New York: Academic, 2d ed., 1982.
3. Shiozaki, A., Edge extraction using entropy operator, Comp. Vis. Graph. Image Proc., vol. 36, pp. 1-9, 1986.
4. Zenzo, S.D., A note on the gradient of a multiimage, Comp. Vis. Graph. Image Proc., vol. 33, pp. 116-125, 1986.
5. Machuca, R., and Phillips, K., Applications of vector fields to image processing, IEEE Trans. Pattern Anal. Machine Intel., vol. PAMI-5, no.3, pp. 316-329, May 1983.
6. Cumani, A., Edge detection in multispectral images, CVGIP: Graphical Models and Image Processing, vol. 53, no. 1, pp. 40-51, Jan. 1991.
7. Barnett, V., The ordering of multivariate data, J. Royal Statist. Soc. A, 139, pt. 3, pp. 318-343, 1976.
8. Feechs, R.J. and Arce, G.R., Multidimensional morphologic edge detection, In Proc. SPIE Conf. Visual Comm. and Image Proc., vol. 845, pp. 285-292, 1987.

9. Lee, J.S.J., Haralick, R.M., and Shapiro, L.G., Morphologic edge detection , IEEE J. Robot. Automat., vol. RA-3, no.2, pp. 142-156, Apr. 1987.
10. Dollár, P., & Zitnick, C. L. Fast edge detection using structured forests. IEEE transactions on pattern analysis and machine intelligence, 37(8), 1558-1570, 2015.
11. Melin, P., Gonzalez, C. I., Castro, J. R., Mendoza, O., & Castillo, O. Edge-detection method for image processing based on generalized type2 fuzzy logic. IEEE Transactions on Fuzzy Systems, 22(6), 1515-1525, 2014.
12. Sharma, K., & Chopra, V. ACO Based Color Edge Detection on the Fusion of HUA and PCA Components. International Journal, 5(6), 2015.
13. Demirci, R., “Rule-based automatic segmentation of color images”,International Journal of Electronics and Communications(AEU),60,435-442, 2006.
14. Trahanias, P.E., Venetsanopoulos, A.N., “Vektör Order Statistics Operators as Color Edge Detectors”, IEEE Tran. On Systems, Man and Cybernetics-Part B Cybernetics, Vol 26 No1 , 1996.