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i) 1d signal with different edge types
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(a)
(b)
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(c)
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(a) Input signal with various edge types.
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(b) Edge map displayed superimposed to the input signal.
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(c) Edge map corresponding to l=0.1 and
it =104.
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iii) SUSAN test image
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(a)
(b)
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(c)
(d)
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(a) SUSAN test image used in the paper 'SUSAN - A new approach to low level image processing' by Smith S.M. and Brady J.M. in International J. Computer Vision 23 (1997) 45-78.
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(b) Canny edge detection applied to the SUSAN Test image.
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(c) SUSAN edge detection applied to the SUSAN Test image with parameter t=10.
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(d) Our edge detector applied to the SUSAN Test image using l=0.1,
it =1 and thres =1.
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iv) SUSAN test image with an affine perturbation
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(a)
(b)
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(c)
(d)
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(a) SUSAN test image with the addition of the affine function (i,j)=5∙(i-j).
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(b) Canny edges.
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(c) SUSAN edges with parameter t=10.
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(d) Our edges for l=0.1,
it =1 and thres =1.
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v) SUSAN test image with a quadratic perturbation
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(a)
(b)
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(c)
(d)
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(a) SUSAN test image with the addition of the quadratic function
(i,j)=(j-255)2-(i-255)2.
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(b) Canny edges.
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(c) SUSAN edges with parameter t=10.
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(d) Our edges for l=0.1,
it =1 and thres =7.
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vi) Test image with Gaussian noise
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(a)
(b)
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(c)
(d)
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(a) Test image with the addition of Gaussian noise.
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(b) Canny edges with s=3.
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(c) The edges displayed in this picture have been obtained by applying
twice the SUSAN algorithm.
When applied the first time, the SUSAN algorithm filters the Gaussian noise and returns a binary image
with sligthly perturbed edges. By applying again the SUSAN algorithm on the filtered image, one is then able to extract the edges of the
background picture.
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(d) Our edges for l=0.001,
it =1 and thres =90.
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vii) Chessboard test image
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(a)
(b)
(c)
(d)
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(a) Chessboard test image.
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(b) Canny edges.
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(c) SUSAN edge detector.
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(d) Our edges for l=0.1,
it =10 and thres =190.