A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Design and Use of Steerable Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimation of noise in images: an evaluation
CVGIP: Graphical Models and Image Processing
Local Scale Control for Edge Detection and Blur Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Edge Detection and Ridge Detection with Automatic Scale Selection
International Journal of Computer Vision
IEEE Transactions on Signal Processing
Wavelet-based statistical signal processing using hidden Markovmodels
IEEE Transactions on Signal Processing
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Image Processing
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To recognize or identify objects it is desirable to use features which are minimally affected by changes in lighting and non-stationary noise. This requires accurate estimation of both signal and noise. In response to this challenge, this paper proposes a method for estimation of non-stationary isotropic noise based on steering filters to directions perpendicular and parallel to the local signal. From the filter responses in this direction equations for signal and noise are obtained which lead to an edge detection method dependent solely upon local signal-to-noise ratio. The proposed method is compared to various common edge detection methods from the literature, on synthetic and real images. Quantitative improvement is demonstrated on synthetic images and qualitative improvement on real images.