Edgedetectioniswelldevelopedareaofimageanalysis.Manyvariouskindsoftechniquesweredesignedforone-channelimages.Also,aconsiderableattentionwaspaidtoedgedetectionincolor,multispectral,andhyperspectralimages.However,therearestillmanyopenissuesinedgedetectioninmultichannelimages.Forexample,eventhede¿nitionofmultichanneledgeisratherempiricalandisnotwellestablished.Inthispaperstatisticalpatternrecognitionmethodologyisusedtoapproachtheproblemofedgedetectionbyconsideringimagepixelsaspointsinamultidimensionalfeaturespace.Appropriatemultivariatetechniquesareusedtoretrieveinformationwhichcanbeusefulforedgedetection.Theproposedapproachesweretestedonthereal-worlddata.

In the past few years a variety of successful algorithms to select/extract discriminative spectral bands was introduced. By exploiting the connectivity of neighbouring spectral bins, these techniques may be more beneficial than the standard feature selection/extraction methods applied for spectral classification. The goal of this paper is to study the effect of the training sample size on the performance of different strategies to select/extract informative spectral regions. We also consider the success of these methods compared to Principal Component Analysis (PCA) for different numbers of extracted components/groups of spectral bands.

This paper presents paper retrieval using the speci¿c paper features chain and laid lines. Paper features are detected in digitized paper images and they are represented such that they could be used for retrieval. Optimal retrieval performance is achieved by means of a trainable similarity measure for a given set of paper features. By means of these methods a retrieval system is developed that art experts could use real-time in order to speed up their paper research.

In image retrieval systems, images can be represented by single feature vectors or by clouds of points. A cloud of points offers a more flexible description but suffers from class overlap. We propose a novel approach for describing clouds of points based on support vector data description (SVDD). We show that combining SVDD-based classifiers improves the retrieval precision. We investigate the performance of the proposed retrieval technique on a database of 368 texture images and compare it to other methods.

Feature selection is an important tool reducing necessary feature acquisition time in some applications. Standard methods, proposed in the literature, do not cope with the measurement cost issue. Including the measurement cost into the feature selection process is difficult when features are grouped together due to the implementation. If one feature from a group is requested, all others are available for zero additional measurement cost. In the paper, we investigate two approaches how to use the measurement cost and feature grouping in the selection process. We show, that employing grouping improves the performance significantly for low measurement costs. We discuss an application where limiting the computation time is a very important topic: the segmentation of backscatter images in product analysis.