Scale-Invariant Image Recognition Based On Higher Order Autocorrelation Features

Scale-Invariant Image Recognition Based On Higher Order Autocorrelation Features (1996)

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by M. Kreutz , B. Völpel , H. Janßen

Venue:	Pattern Recognition
Citations:	11 - 1 self

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Datum	Value	Source
TITLE	Scale-Invariant Image Recognition Based On Higher Order Autocorrelation Features	user correction - Legacy Corrections
AUTHOR NAME	M. Kreutz	user correction - Legacy Corrections
AUTHOR AFFIL	Institut fur Neuroinformatik, Lehrstuhl fur Theoretische Biologie	user correction - Legacy Corrections
AUTHOR ADDR	Ruhr--Universitat Bochum, 44780 Bochum, Germany	user correction - Legacy Corrections
AUTHOR NAME	B. Völpel	user correction - Legacy Corrections
AUTHOR AFFIL	Institut fur Neuroinformatik, Lehrstuhl fur Theoretische Biologie	user correction - Legacy Corrections
AUTHOR ADDR	Ruhr--Universitat Bochum, 44780 Bochum, Germany	user correction - Legacy Corrections
AUTHOR NAME	H. Janßen	user correction - Legacy Corrections
ABSTRACT	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 ...	user correction - Legacy Corrections
YEAR	1996	INFERENCE
VENUE	Pattern Recognition	INFERENCE
VENUE TYPE	JOURNAL	INFERENCE
VOLUME	29	INFERENCE
CITATIONS	42 found	ParsCit 1.0