5segmorph - 5. Edge Detection, Image Segmentation, and...

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1 5. Edge Detection, Image Segmentation, and Mathematical Morphology • Chapter 7 (Image Segmentation) • Chapter 8 pp.518-528 (Morphology)
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2 Edge Detection Using Derivative Operators Edges: the image portions which have large gradients Magnitude of the gradient often approximated by Thresholding Edge Thinning & Grouping Edge Map n n n f n n n f n n f 2 2 1 1 2 1 2 1 ) , ( ) , ( | ) , ( | 2 2 n n n n n n f f 2 2 1 1 2 1 ) , ( ) , (
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3 Edge Enhancement by Gradients (a): original (b): magnitude of the gradient using the Prewitt operator (c): Gradient > 25 --> 255 (d): (c) and gradient < 25 --> 0.
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4 Edge Extraction via Gradients (a) original (b) vertical Sobel filtering (c) horizontal Sobel filter (d) magnitude of gradients
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5 Canny Edge Detector Reference: Canny, J.F., “A computational approach to edge detection,” IEEE Trans Pattern Analysis and Machine Intelligence , 8(6): 679-698, Nov 1986. The image is smoothed by Gaussian convolution – The larger the width of the Gaussian mask, the lower is the detector's sensitivity to noise. – The localization error in the detected edges also increases slightly as the Gaussian width is increased. First Step - Smoothing
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6 Second Step—Find Edge Strength Find the edge strength by taking the gradient of the image The magnitude, or EDGE STRENGTH, of the gradient is then approximated: (Norm of the gradient) |G| = |Gx| + |Gy| Sobel operator Canny Edge Detector x y
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7 Third Step - Non-Maximum Suppression (Thinning) Check if the pixel is a local maximum along the gradient direction – requires checking interpolated pixels p and r Localize the peaks of the gradient magnitude Canny Edge Detector
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This note was uploaded on 07/08/2011 for the course EE 440 taught by Professor Jenq-nenghwang during the Spring '11 term at University of Washington.

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5segmorph - 5. Edge Detection, Image Segmentation, and...

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