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Hough Transform

Hough Transform. Omri Zorea and Alon Lipnik Group #11. Introduction. Technique to find imperfect instances of object within a certain class of shapes. (i.e. lines, cycles, ellipses, parabolas etc.). Use in image analysis, computer vision and digital image processing.

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Hough Transform

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  1. Hough Transform Omri Zorea and Alon Lipnik Group #11

  2. Introduction • Technique to find imperfect instances of object within a certain class of shapes. • (i.e. lines, cycles, ellipses, parabolas etc.). • Use in image analysis, computer vision and digital image processing. • Was invented by Richard Duda and Peter Hart in 1972 (patent of Paul Hough, 1962). 1. Aerial photo 2. X-Y plane 3. Hough plane transform detecting Hough Transform

  3. Hough Transform • Find lines in picture (y = -mx + b). • Match dots on the picture to lines and match line to a dot. • The slope (m) can goes to infinity (unbounded domain)  polar coordinates.

  4. Hough Transform • Detect arbitrary shapes in picture. • Each point in Image space is now a sinusoid:ρ = x cosθ+ y sinθ • For each edge point on image it compute his gradient and know which shape is it. Hough Transform

  5. Hough Transform • Detect arbitrary shapes in picture. • Accumulator matrix - find lines with maximum points. • determines Threshold values in matrix. • the values are the points-density of shape. Hough Transform

  6. Parallel Algorithm • The image is divided into rows with the same number of columns. • PVM is a programming tool used for the message routing, data conversion and task scheduling. • Complexity of O(m*n^2). • m – different theta values. • nxn – image size • (* Algorithm LARPBS – linear array reconfigurable pipeline bus system) Hough Transform

  7. Parallel Algorithm • Speed Up: • Check on 4, 8, 16 and 32 processors. • Two different algorithms. • Image density range 5% - 15%. Hough Transform

  8. Parallel Algorithm • Efficiency: • Image density range from 5% to 25%. • Check on 4, 8, 12, 16 and 32 processors (process 0 is the master). • Trade-Off (processors and image density). Hough Transform

  9. References • Parallel algorithms for Hough Transform, FevziOktayOzbek : http://preserve.lehigh.edu/cgi/viewcontent.cgi?article=1073&context=etd • A fast efficient parallel HT algorithm on LARPBS: • http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.99.8684&rep=rep1&type=pdf • Guillermo Sapiro of Duke university : • https://www.youtube.com/watch?v=kMK8DjdGtZo

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