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24 October 2005 Edge and corner detection in grayscale and color images using first Fourier basis
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Abstract
In this paper we present an algorithm for edge and corner detection in the greyscale and colour images using Fourier Amplitude Measure. We use the magnitude of the first Fourier basis to detect changes in the image intensity profile. We show the mathematical equivalence of the first Fourier co-efficient to that of the first order partial derivatives. Thus we interpret these coefficients as an estimate of the derivative measure and call them as Fourier Derivative Estimate. We then modify the first order derivative based method used for edge and corner detection using the Fourier Derivative Estimates (FDE) to produce robust edges and corner points. We compare the repeatability rate between FDE method and first order derivative methods in grayscale images. We then show examples where in, the generalization of this method to color images is found to be stable under illumination, blur, color contrast and affine variations, suggesting the utility of this method for image registration and mosaicing purposes.
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M. C. Prakash, V. Jagannadan, R. Raghunath Sharma, and G. V. Prabhakara Rao "Edge and corner detection in grayscale and color images using first Fourier basis", Proc. SPIE 6006, Intelligent Robots and Computer Vision XXIII: Algorithms, Techniques, and Active Vision, 60060R (24 October 2005); https://doi.org/10.1117/12.629860
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