Computer Vision Lab Contour Matching Using Epipolar Geometry (PAMI, April 2000) Young Ki Baik

Computer Vision Lab Contour matching Key idea Key idea  Initial matching refinement from the matched sets of contours.

Computer Vision Lab Contour matching Several primitives to match Several primitives to match  Points / Straight lines  Both points and straight lines  Line segments  Contour A set of chained image points.A set of chained image points.

Computer Vision Lab Contour matching Previous contour matching methods Previous contour matching methods  Smoothness constraints of the contour  Smoothness constraints on the second derivative of velocity  Or minimization of curvature variations Contour matching methods Contour matching methods  Contour matching using Epipolar geometry

Computer Vision Lab Contour matching Assumption Assumption  The images are taken with a moving camera and the scene is static.  The intensity value of a region does not change much as the camera moves.  If a space contour is observed by two cameras, there can be matching between images of the contour in image space with the same parametric value.

Computer Vision Lab Epipolar geometry Fundamental matrix F Fundamental matrix F

Computer Vision Lab Contour parameterization Let C(S) be a space curve parameterized by Let C(S) be a space curve parameterized by arc length S. arc length S. are projected contours of C(S). are projected contours of C(S).

Computer Vision Lab Epipolar geometry for Contour

Computer Vision Lab Contour matching algorithm Algorithm (Initial matching) Algorithm (Initial matching)  Step1:Find contours in each image Using a zero-crossing edge detector and an edge linker.Using a zero-crossing edge detector and an edge linker.  Step2:Find a set of seed matches Using classical correlation-based matching technique.Using classical correlation-based matching technique.  Step3:Compute the epipolar geometry Using 8-point algorithm (Hartley)Using 8-point algorithm (Hartley)

Computer Vision Lab Contour matching algorithm Algorithm (Contour matching) Algorithm (Contour matching)  Step4:For each contour point, do steps 5-7  Step5:Find the initial estimation  Step6:Match points Using the epipolar constraint and correlation.Using the epipolar constraint and correlation.  Step7:Choose the major corresponding contour. Discard contours which match to minor corresponding contours.Discard contours which match to minor corresponding contours.

Computer Vision Lab Contour matching algorithm Step5: Find the initial estimation ( 1) Step5: Find the initial estimation ( 1)  Finding contour point correspondence. Using epipolar lineUsing epipolar line

Computer Vision Lab Contour matching algorithm Step5: Find the initial estimation ( 2 ) Step5: Find the initial estimation ( 2 )  Finding contour point correspondence.

Computer Vision Lab Contour matching algorithm Step5: Find the initial estimation ( 3 ) Step5: Find the initial estimation ( 3 )  Fast Finding contour point correspondence. q nearest neighbors ( ) of.q nearest neighbors ( ) of. Let match points be.Let match points be. Let is.Let is. is initial estimate location. is initial estimate location.

Computer Vision Lab Contour matching algorithm Step6: Match point Step6: Match point  Finding contour point correspondence. Using the epipolar constraint and correlation.Using the epipolar constraint and correlation.

Computer Vision Lab Contour matching algorithm Step7:Choose the major corresponding contour Step7:Choose the major corresponding contour  Major corresponding contour All matches not on the major corresponding contour are removed.All matches not on the major corresponding contour are removed.

Computer Vision Lab Contour matching algorithm Algorithm (Re-computing) Algorithm (Re-computing)  Step8:Re-compute the epipolar geometry Using points in matched contours.Using points in matched contours.  Step9:For each contour point, rematch along the contour Using epipolar constraint Using epipolar constraint

Computer Vision Lab Extension to contour matching in three views

Computer Vision Lab Contour matching result Mosaic box image set Mosaic box image set

Computer Vision Lab Contour matching result Etc. image set Etc. image set

Computer Vision Lab Conclusion Contour matching algorithm uses Geometric constraints has been presented. Contour matching algorithm uses Geometric constraints has been presented. Fail case Fail case  Bad initial match  Lack of corner features  Simple repetition of a pattern  Highly blurred patterns Computing time : 17sec Computing time : 17sec  Number of contours : 1084  Number of points :  SGI O2 workstation with an R processor