Fuzzy sets in pattern recognition: methodology and methods
Pattern Recognition
Characterization of Signals from Multiscale Edges
IEEE Transactions on Pattern Analysis and Machine Intelligence
Differentiation-Based Edge DetectionUsing the Logarithmic Image Processing Model
Journal of Mathematical Imaging and Vision
Adaptive image denoising and edge enhancement in scale-space using the wavelet transform
Pattern Recognition Letters - Special issue: Sibgrapi 2001
A wavelet-based multiresolution edge detection and tracking
Image and Vision Computing
Wavelet transform domain filters: a spatially selective noise filtration technique
IEEE Transactions on Image Processing
A novel iris segmentation using radial-suppression edge detection
Signal Processing
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An illumination-independent edge detection and fuzzy enhancement algorithm based on wavelet transform is proposed to extract edges out of the non-uniform weak illumination image. Firstly, to work out the illumination-independent edge detection method based on wavelet transform, the illumination-reflection image formation model and CCD camera imaging formula are introduced. In succession, the pixels' wavelet module coefficients in the local edge area of the image are analyzed and compared, and the illumination-independent edge detection formula insensitive to illumination variation is presented. Then, for increasing the difference between edges, background and noise, the fuzzy enhancement operator which takes not only the wavelet module magnitude into account, but also the wavelet gradient direction is designed. Finally, single pixel edges are taken out of images according to edge pixels' characteristic after wavelet transform. Through synthetic and real images experiments, this edge detection method's performance for the non-uniform weak illumination images is analyzed and compared with other two edge detection methods quantitatively and qualitatively. The experiment result proves the edge detection method works well for the uneven gray and low contrast images.