Feature Match for Medical Images

This paper represents an algorithm for Feature Match, a summed up estimated approximate nearest neighbor field (ANNF) calculation system, between a source and target image. The proposed calculation can estimate ANNF maps between any image sets, not as a matter of course related. This generalization is accomplished through proper spatial-range changes. To register ANNF maps, worldwide shading adjustment is connected as a reach change on the source picture. Image patches from the pair of pictures are approximated utilizing low-dimensional elements, which are utilized alongside KD-tree to appraise the ANNF map. This ANNF guide is further enhanced in view of picture coherency and spatial changes. The proposed generalization, empowers to handle a more extensive scope of vision applications, which have not been handled utilizing the ANNF structure. Here one such application is outlined namely: optic plate discovery .This application manages restorative imaging, where optic circles are found in retinal pictures utilizing a sound optic circle picture as regular target picture. ANNF mappings is used in this application and is shown experimentally that the proposed approaches are faster and accurate, compared with the state-of the-art techniques

k-Nearest Neighbour Classifiers: 2nd Edition (with Python examples)

Perhaps the most straightforward classifier in the arsenal or machine learning techniques is the Nearest Neighbour Classifier -- classification is achieved by identifying the nearest neighbours to a query example and using those neighbours to determine the class of the query. This approach to classification is of particular importance because issues of poor run-time performance is not such a problem these days with the computational power that is available. This paper presents an overview of techniques for Nearest Neighbour classification focusing on; mechanisms for assessing similarity (distance), computational issues in identifying nearest neighbours and mechanisms for reducing the dimension of the data. This paper is the second edition of a paper previously published as a technical report. Sections on similarity measures for time-series, retrieval speed-up and intrinsic dimensionality have been added. An Appendix is included providing access to Python code for the key methods.Comment: 22 pages, 15 figures: An updated edition of an older tutorial on kN

Transfer of albedo and local depth variation to photo-textures

Acquisition of displacement and albedo maps for full building façades is a difficult problem and traditionally achieved through a labor intensive artistic process. In this paper, we present a material appearance transfer method, Transfer by Analogy, designed to infer surface detail and diffuse reflectance for textured surfaces like the present in building façades. We begin by acquiring small exemplars (displacement and albedo maps), in accessible areas, where capture conditions can be controlled. We then transfer these properties to a complete phototexture constructed from reference images and captured under diffuse daylight illumination. Our approach allows super-resolution inference of albedo and displacement from information in the photo-texture. When transferring appearance from multiple exemplars to façades containing multiple materials, our approach also sidesteps the need for segmentation. We show how we use these methods to create relightable models with a high degree of texture detail, reproducing the visually rich self-shadowing effects that would normally be difficult to capture using just simple consumer equipment. Copyright © 2012 by the Association for Computing Machinery, Inc

What is a good nearest neighbors algorithm for finding similar patches in images

Abstract. Many computer vision algorithms require searching a set of images for similar patches, which is a very expensive operation. In this work, we compare and evaluate a number of nearest neighbors algorithms for speeding up this task. Since image patches follow very different distributions from the uniform and Gaussian distributions that are typically used to evaluate nearest neighbors methods, we determine the method with the best performance via extensive experimentation on real images. Furthermore, we take advantage of the inherent structure and properties of images to achieve highly efficient implementations of these algorithms. Our results indicate that vantage point trees, which are not well known in the vision community, generally offer the best performance.