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# lec17 - Announcements Stereo Vision Wrapup & Intro...

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1 CSE152, Spring 2011 Intro Computer Vision Stereo Vision Wrapup & Intro Recognition Introduction to Computer Vision CSE 152 Lecture 17 CSE152, Spring 2011 Intro Computer Vision Announcements • HW3 due date postpone to Thursday • HW4 to posted by Thursday, due next Friday. • Order of material– we’ll first cover recognition so that you’re prepared for assignment. Then return to motion. • Final Exam: Tuesday 6/7 8:00-11:00 AM, Here CSE152, Spring 2011 Intro Computer Vision Rectification CSE152, Spring 2011 Intro Computer Vision Rectification Given a pair of images, transform both images so that epipolar lines are scan lines. CSE152, Spring 2011 Intro Computer Vision Using epipolar & constant Brightness constraints for stereo matching For each epipolar line For each pixel in the left image compare with every pixel on same epipolar line in right image pick pixel with minimum match cost This will never work, so: Improvement: match windows (Seitz) CSE152, Spring 2011 Intro Computer Vision Correspondence Search Algorithm For i = 1:nrows for j=1:ncols best(i,j) = -1 for k = mindisparity:maxdisparity c = Match_Metric(I 1 (i,j),I 2 (i,j+k),winsize) if (c > best(i,j)) best(i,j) = c disparities(i,j) = k end end end end O(nrows * ncols * disparities * winx * winy)

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2 CSE152, Spring 2011 Intro Computer Vision Match Metric Summary MATCH METRIC DEFINITION Normalized Cross-Correlation (NCC) Sum of Squared Dif erences (SSD) Normalized SSD Sum of Absolute Dif erences (SAD) Zero Mean SAD Rank Census These two are actually the same CSE152, Spring 2011 Intro Computer Vision Stereo results Ground truth Disparity Map Scene – Data from University of Tsukuba (Seitz) CSE152, Spring 2011 Intro Computer Vision Results with window correlation Window-based matching (best window size) Ground truth (Seitz) CSE152, Spring 2011 Intro Computer Vision Results with better method Near State of the art method Boykov et al., Fast Approximate Energy Minimization via Graph Cuts , International Conference on Computer Vision, September 1999. Ground truth (Seitz) CSE152, Spring 2011 Intro Computer Vision Some Issues • Window size • Window shape • Lighting • Ambiguity • Ordering • Half occluded regions CSE152, Spring 2011 Intro Computer Vision Window size W = 3 W = 20 Better results with adaptive window T. Kanade and M. Okutomi, A Stereo Matching Algorithm with an Adaptive Window: Theory and Experiment ,, Proc. International Conference on Robotics and
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## This note was uploaded on 08/05/2011 for the course CSE 152 taught by Professor Staff during the Spring '08 term at UCSD.

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lec17 - Announcements Stereo Vision Wrapup & Intro...

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