1. Maximum-Likelihood Image Matching Zheng Lu

2. Introduction SSD(sum of squared difference) –Is not so robust A new image matching measure –Based on maximum-likelihood estimation of position –More robust

3. Maximum-Likelihood Matching Set of template feature Set of image feature The position of template in the image –t a random variable

4. Maximum-Likelihood Matching Distance from each template pixel to the closest image pixel. Probability density function(PDF) for the distance Find the t that can maximize this function

5. Estimating the PDF The density can be modeled by inliers and outliers

6. Estimating the PDF The second term should also decrease as d increases In practice, expected probability density for a random outlier is excellent

7. Search Strategy

8. multi-resolution technique divides the space of model positions into cells and determines which cells could contain a position satisfying the criterion Can find the best location, If a conservative test is used

9. c is the center of cell distance between the location to template edge pixel template mapped by c and any other pose in the cell.

10. The criterion will be