Edge linking in image processing
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This document discusses image segmentation techniques, specifically linking edge points through local and global processing. Local processing involves linking edge-detected pixels that are similar in gradient strength and direction within a neighborhood. Global processing uses the Hough transform to link edge points into lines by mapping points in the image space to the parameter space of slope-intercept or polar coordinates. Thresholding in parameter space identifies coherent lines composed of edge points. The Hough transform allows finding lines even if there are gaps or other defects in detected edge points.
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Edge linking in image processing
- 1. Image Segmentation-2 Subject: Image Procesing & Computer Vision Dr. Varun Kumar Subject: Image Procesing & Computer Vision Dr. Varun Kumar (IIIT Surat)Lecture 27 1 / 10
- 2. Outlines 1 Linking of edge points Local processing Global processing (Hough transform) 2 References Subject: Image Procesing & Computer Vision Dr. Varun Kumar (IIIT Surat)Lecture 27 2 / 10
- 3. Previous discussion Edge in an image It is a region, where the image intensity changes drastically. Methods for edge points detection: 1 Local processing 2 Global processing Note: Ideally discontinuity detection techniques should identify pixels lying on the boundary between regions. ⇒ In practice there may be breaks in boundary and spurious intensity discontinuities. 1 Due to non-uniform illumination 2 Presence of noise Subject: Image Procesing & Computer Vision Dr. Varun Kumar (IIIT Surat)Lecture 27 3 / 10
- 4. Local processing: Take an edge detected image. Analyze each pixel in small neighborhood of every point (x,y). All points that are similar in nature are linked. This forms a boundary of pixels that are similar in nature. Similarity Strength of the response of the gradient operator Direction of gradient Edge pixels (x , y ) and (x, y) are similar, if | f (x, y) − f (x , y )| ≤ T and |α f (x, y) − α f (x , y )| < A (x , y ) ∈ Nxy Here, T → Non-negative threshold and A → Angle threshold Subject: Image Procesing & Computer Vision Dr. Varun Kumar (IIIT Surat)Lecture 27 4 / 10
- 5. Global processing (Hough transform) Note: If intercept and slope remain constant then infinite number of points can exist in the given line y = mx + c. In Fig.2, (x, y) is supposed to be constant and c and m are variable. For fixed point (x, y), there may exist infinite number of lines for different c and m. Subject: Image Procesing & Computer Vision Dr. Varun Kumar (IIIT Surat)Lecture 27 5 / 10
- 6. Hugh transform Let (xk, yk) is a image point that is mapped into a m − c plane c = −mxk + yk Here m varies from mmin to mmax . mp ⇒ cq and A(p, q) → A(p, q) + 1 Subject: Image Procesing & Computer Vision Dr. Varun Kumar (IIIT Surat)Lecture 27 6 / 10
- 7. Continued– Let ρ = x cos θ + y sin θ → Normal form θ = ±90o ρ = √ M2 + N2 where M × N → Image size Subject: Image Procesing & Computer Vision Dr. Varun Kumar (IIIT Surat)Lecture 27 7 / 10
- 8. Continued– Subject: Image Procesing & Computer Vision Dr. Varun Kumar (IIIT Surat)Lecture 27 8 / 10
- 9. Important observation (Hough transform) when c and m are variable 1 Our image location (x,y) remain constant, we change c and m. 2 By changing c and m, we get infinite number of lines. 3 We put some thresholding criteria, that gives us the more suitable line. These lines can be treated as the edge line. when ρ and θ are variables 1 Similarly in normal form, if x and y remain constant, and by changing ρ and θ, infinite numbers of line can be drawn. 2 By applying the thresholding criteria, we can get the more suitable line that will be our edge line.. Subject: Image Procesing & Computer Vision Dr. Varun Kumar (IIIT Surat)Lecture 27 9 / 10
- 10. References M. Sonka, V. Hlavac, and R. Boyle, Image processing, analysis, and machine vision. Cengage Learning, 2014. D. A. Forsyth and J. Ponce, “A modern approach,” Computer vision: a modern approach, vol. 17, pp. 21–48, 2003. L. Shapiro and G. Stockman, “Computer vision prentice hall,” Inc., New Jersey, 2001. R. C. Gonzalez, R. E. Woods, and S. L. Eddins, Digital image processing using MATLAB. Pearson Education India, 2004. Subject: Image Procesing & Computer Vision Dr. Varun Kumar (IIIT Surat)Lecture 27 10 / 10