Abstract 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.
Applying Non-stationary Noise Estimation to Achieve Contrast Invariant Edge Detection
Computer Vision – ACCV 2006 ; 7 ; 742-751
Lecture Notes in Computer Science ; 3852 , 7
2006-01-01
10 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
Edge Detection , Gaussian Mixture Model , Real Image , Expectation Maximization Algorithm , Synthetic Image Computer Science , Image Processing and Computer Vision , Computer Imaging, Vision, Pattern Recognition and Graphics , Pattern Recognition , Artificial Intelligence (incl. Robotics) , Algorithm Analysis and Problem Complexity
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