Edge Detection Algorithm of Magnetic Tile Crack Based on Wavelet Modulus Maxima

In order to accurately extract edge information of magnetic tile surface defect, an edge detection algorithm based on image weighted information entropy and wavelet modulus maxima is proposed. Because the magnetic tile surface with low contrast and textured background has a negative influence on edge extraction, a new BHPF filter with adaptive changing cutoff frequency is designed. The clarity and complexity of textured background are quantitatively described by weighted information entropy of image gradient variance. The filter changes its parameter through matching the non-linear relationship between information entropy and cutoff frequency. To prevent the losing of edge information, the best decomposition scale is obtained by the level determination function. In order to ensure the edge continuity and veracity, wavelet modulus maxima is judged through a double threshold to get the edge point. Experimental results show that the algorithm outperforms the conventional canny and sobel algorithms in detection of magnetic tile crack edge. This edge detection algorithm can also detect other defects.