# Image Rectification for Stereo Vision

## Presentation on theme: "Image Rectification for Stereo Vision"— Presentation transcript:

Image Rectification for Stereo Vision
Charles Loop Zhengyou Zhang Microsoft Research Please remind the audience that this is all completely confidential. We are in the midst of the patent process and certainly do not want to jeopardize that effort in any way. 1

Problem Statement rectification
Compute a pair of 2D projective transforms (homographies) rectification Original images Rectified images

Motivations To simplify stereo matching:
Instead of comparing pixels on skew lines, we now only compare pixels on the same scan lines. Graphics applications: view morphing Problem: Rectifying homographies are not unique Goal: to develop a technique based on geometrically well-defined criteria minimizing image distortion due to rectification

Epipolar Geometry Epipoles anywhere Epipole at Fundamental matrix
C C’ Epipoles anywhere Fundamental matrix F: a 3x3 rank-2 matrix Epipole at Fundamental matrix

Stereo Image Rectification
Compute H and H’ such that Compute rectified image points: Problem: H and H’ are not unique.

Properties of H and H’ (I)
Consider each row of H and H’ as a line: Recall: both e and e’ are sent to [1 0 0]T Observations (I): v and w must go through the epipole e v’ and w’ must go through the epipole e’ u and u’ are irrelevant to rectification

Properties of H and H’ (II)
Observation (II): Lines v and v’, and lines w and w’ must be corresponding epipolar lines. Observation (III): Lines w and w’ define the rectifying plane.

Decomposition of H Special projective transform: Similarity transform:
Shearing transform:

Special Projective Transform (I)
Sends the epipole to infinity epipolar lines become parallel Captures all image distortion due to projective transformation Subgoal: Make Hp as affine as possible.

Special Projective Transform (II)
How to do it? Let original image point be the transformed point will be Observation: If all weights are equal, then there is no distortion. Key idea: minimize the variation of wi over all pixels with weight

Similarity Transform Rotate and translate images such that the epipolar lines are horizontally aligned. Images are now rectified.

Shearing Transform Free to scale and translate in the horizontal direction. Subgoal: Preserve original image resolution as close as possible.

Example Original image pair

Intermediate result After special projective transform:

Intermediate result After similarity transform:

Final result After shearing transform

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