Peer group image processing identifies a "peer group" for each pixel and then replaces the pixel intensity with the average over the peer group. Two parameters provide direct control over which image features are selectively enhanced: area (number of pixels in the feature) and window diameter (window size needed to enclose the feature). A discussion is given of how these parameters determine which features in the image are smoothed or preserved. We show that the Fisher discriminant can be used to automatically adjust the peer group averaging (PGA) parameters at each point in the image. This local parameter selection allows smoothing over uniform regions while preserving features like corners and edges. This adaptive procedure extends to multilevel and color forms of PGA. Comparisons are made with a variety of standard filtering techniques and an analysis is given of computational complexity and convergence issues.
Selecting salient points from two or more images for computing correspondence is a well studied problem in image analysis. This paper describes a new and effective technique for selecting these tiepoints using condition numbers, with application to image registration and mosaicking. Condition numbers are derived for point-matching methods based on minimizing windowed objective functions for 1) translation, 2) rotation-scaling-translation (RST) and 3) affine transformations. Our principal result is that the condition numbers satisfy K T rans ≤ K RST ≤ K Af f ine. That is, if a point is ill-conditioned with respect to point-matching via translation then it is also unsuited for matching with respect to RST and affine transforms. This is fortunate since K T rans is easily computed whereas K RST and K Af f ine are not. The second half of the paper applies the condition estimation results to the problem of identifying tiepoints in pairs of images for the purpose of registration. Once these points have been matched (after culling outliers using a RANSAC-like procedure) the registration parameters are computed. The postregistration error between the reference image and the stabilized image is then estimated by evaluating the translation between these images at points exhibiting good conditioning with respect to translation. The proposed method of tiepoint selection and matching using condition number provides a reliable basis for registration. The method has been tested on a large number of diverse collection of images-multi-date Landsat images, aerial images, aerial videos, and infra-red images. A web site where the users can try our registration software is available and is being actively used by researchers around the world.
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