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 ...

Introduction This chapter is an introduction to what has come to be known as datamining and knowledge discovery in the biomedical context. The major reason that datamining has attracted increasing attention in the biomedical industry in recent years is due to the increased availability of huge amount of biomedical data and the imminent need to turn such data into useful information and knowledge. The knowledge gained can lead to improved drug target, improved diagnostics, and improved treatment plan. Datamining is the task of discovering patterns from large amounts of potentially noisy data where the data can be kept in regular relational databases or other forms of information repositories such as flat text files commonly used by biologists. It is a very interdisciplinary subject, relying on ideas and developments in database systems, statistics, machine learning, data visualization, neural networks, pattern recognition, signal processing, and so on. More background on datami