Automatic color edge detection with similarity transformation
Özet
Edge detection is an important step in image processing. As edge is intensity variation with spatial coordinates, the similarities between neighboring pixels could be used for edge detection. It has been observed that the effective results could be attained by thresholding the homogeneity images generated by means of the similarity transformation. Nevertheless, the user-defined normalization coefficient in similarity transform stage seriously effects edge detection performance and it needs to be automatically selected for every particular image. In this study, a new approach in which the normalization coefficient is automatically determined has been presented. The automating process of the similarity transform has been performed according to the gray level values of the neighboring pixels. The gray level differences of the central pixel and other neighboring pixels have been used to determine the similarity coefficient. Subsequently, the binarization process of the homogeneity images obtained with proposed algorithm have been completed with different thresholding techniques. Additionally, the F-score of the proposed edge detection has been obtained with 200 images in the BSDS training dataset. The achieved F-score values have showed that the performance of automatic approach is quite high.
