摘要
In order to extract the defect edge information on the magnetic tile surface with low contrast and textured background,an edge detection algorithm based on image weighted information entropy and wavelet modulus maxima is proposed.At first,a new Butterworth high pass filter(BHPF) with adaptive cutoff frequency is produced,because the clarity and complexity of the textured background are described by the weighted information entropy of the image gradient variance quantitatively,and the filter can change its parameters through matching the non-linear relationship between the information entropy and the cutoff frequency.And then,the best decomposition scale is obtained by the level determination function to prevent edge information from missing.At last,edge points are got by double threshold after obtaining the wavelet modulus maxima,and then the edge image is linked by the edge points to ensure the edge continuity and veracity.Experiment results indicate that the proposed algorithm outperforms the conventional Canny and Sobel algorithm,and the edge detection algorithm can also detect other defects,and lays the foundation for defecting auto- recognition.
In order to extract the defect edge information on the magnetic tile surface with low contrast and textured background, an edge detection algorithm based on image weighted information entropy and wavelet modulus maxima is proposed. At first, a new Butterworth high pass filter （BHPF） with adaptive cutoff frequency is produced, because the clarity and complexity of the textured background are described by the weighted information entropy of the image gradient variance quantitatively, and the filter can change its parameters through matching the non-linear relationship between the infor- mation entropy and the cutoff frequency. And then, the best decomposition scale is obtained by the level determination function to prevent edge information from missing. At last, edge points are got by double threshold after obtaining the wavelet modulus maxima, and then the edge image is linked by the edge points to ensure the edge continuity and veracity. Experiment results indicate that the pro- posed algorithm outperforms the conventional Canny and Sobel algorithm, and the edge detection al- gorithm can also detect other defects, and lays the foundation for defecting auto- recognition.
基金
Supported by the National Natural Science Foundation of China(No.51205265)
