We propose a framework and a complete implementation of a translation and scale invariant image recognition system for natural indoor scenes. The system employs higher order autocorrelation features of scale space data which permit linear classification. An optimal linear classification method is presented, which is able to cope with a large number of classes represented by many, as well as very few samples. In the course of the analysis of our system, we examine which numerical methods for feature transformation and classification show sufficient stability to fulfill these demands. The implementation has been extensively tested. We present the results of our own application and several classification benchmarks. Image recognition Face recognition Scale invariancy Scale space Higher order autocorrelation Optimal linear classification 1. INTRODUCTION The task of visual recognition which was defined by Marr (1) with the question: "What objects are where in the environment?" is still ...

In order to recognize an object in an image, we must determine the best-fit transformation which maps an object model into the image. In this paper, we first show that for features from coplanar surfaces which undergo linear transformations in space, there exists a class of transformations that yield projections invariant to the surface motions up to rotations in the image field. To use this property, we propose a new alignment approach to object recognition based on centroid alignment of corresponding feature groups built on these invariant projections of planar surfaces. This method uses only a single pair of 2D model and data pictures. Experimental results show that the proposed method can tolerate considerable errors in extracting features from images and can tolerate perturbations from coplanarity, as well as cases involving occlusions. As part of the method, we also present an operator for finding planar surfaces of an object using two model views and show its effectiveness by em...

Given a set of points, the goal of data clustering is to group them into clusters, such that the internal homogeneity of points within each cluster contrasts to inter-cluster heterogeneity. Over the last fifty years, many methods for data clustering have been developed in diverse scientific communities. However, many of these methods suffer from several shortcomings, and are unable to handle the rich diversity of cluster structures that are usually present in data. We develop an unsupervised, nonparametric approach to data clustering that addresses these shortcomings. Our goal is to build on the strengths of these methods, while simultaneously offering innovative solutions to their limitations. In our cluster model, clusters are seen as groups of points, with overlapping neighborhoods, that have similar spatial structures that are in contrast with their surroundings. We use the isotropy of a point distribution to characterize spatial structure. We argue that identifying the isotropic density neighborhoods of a point, helps in the detection of a diversity of cluster structures that are challenging to many other methods. We develop three different criteria for identifying neighborhoods with isotropic density. The first criterion is based on examining properties of one-dimensional projections in a hyperspherical neighborhood with uniform point distribution. The second and third criteria are based on the analysis of the force