Maximum-Likelihood Image Matching Zheng Lu

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

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

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

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

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

Search Strategy

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

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.

The criterion will be