CS223B-L5-AdvancedFeatures

CS223B-L5-AdvancedFeatures - Stanford CS223B Computer...

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Unformatted text preview: Stanford CS223B Computer Vision, Winter 2008/09 Lecture 5 Advanced Image Feaures Professor Sebastian Thrun CAs: Ethan Dreyfuss, Young Min Kim, Alex Teichman Sebastian Thrun CS223B Computer Vision, Winter 2009 Advanced Features: Topics Advanced Edge Detection Global Image Features (Hough Transform) Templates, Image Pyramid SIFT Features Sebastian Thrun CS223B Computer Vision, Winter 2009 Features in Matlab im = imread('bridge.jpg'); bw = rgb2gray(im); edge(im,sobel) - (almost) linear edge(im,canny) - not local, no closed form Sebastian Thrun CS223B Computer Vision, Winter 2009 Sobel Operator-1 -2 -1 0 0 0 1 2 1-1 0 1-2 0 2 -1 0 1 S 1 = S 2 = Edge Magnitude = Edge Direction = S 1 + S 2 2 2 tan-1 S 1 S 2 Sebastian Thrun CS223B Computer Vision, Winter 2009 Sobel in Matlab edge(im,sobel) Sebastian Thrun CS223B Computer Vision, Winter 2009 Canny Edge Detector edge(im,canny) Sebastian Thrun CS223B Computer Vision, Winter 2009 Comparison Canny Sobel Sebastian Thrun CS223B Computer Vision, Winter 2009 Canny Edge Detection Steps: 1. Apply derivative of Gaussian 2. Non-maximum suppression Thin multi-pixel wide ridges down to single pixel width 3. Linking and thresholding Low, high edge-strength thresholds Accept all edges over low threshold that are connected to edge over high threshold Sebastian Thrun CS223B Computer Vision, Winter 2009 Non-Maximum Supression Non-maximum suppression: Select the single maximum point across the width of an edge. Sebastian Thrun CS223B Computer Vision, Winter 2009 Linking to the Next Edge Point Assume the marked point q is an edge point. Take the normal to the gradient at that point and use this to predict continuation points (either r or p). Sebastian Thrun CS223B Computer Vision, Winter 2009 Edge Hysteresis Hysteresis : A lag or momentum factor Idea: Maintain two thresholds k high and k low Use k high to find strong edges to start edge chain Use k low to find weak edges which continue edge chain Typical ratio of thresholds is roughly k high / k low = 2 Sebastian Thrun CS223B Computer Vision, Winter 2009 Canny Edge Detection (Example) courtesy of G. Loy gap is gone Original image Strong edges only Strong + connected weak edges Weak edges Sebastian Thrun CS223B Computer Vision, Winter 2009 Canny Edge Detection (Example) Using Matlab with default thresholds Sebastian Thrun CS223B Computer Vision, Winter 2009 Bridge Example Again edge(im,canny) Sebastian Thrun CS223B Computer Vision, Winter 2009 Corner Effects Sebastian Thrun CS223B Computer Vision, Winter 2009 Summary: Canny Edge Detection Most commonly used method Traces edges, accommodates variations in contrast Not a linear filter!...
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CS223B-L5-AdvancedFeatures - Stanford CS223B Computer...

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