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Appearance-based object recognition using optimal feature transforms

✍ Scribed by Joachim Hornegger; Heinrich Niemann; Robert Risack

Publisher
Elsevier Science
Year
2000
Tongue
English
Weight
424 KB
Volume
33
Category
Article
ISSN
0031-3203

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✦ Synopsis

In this paper we discuss and compare di!erent approaches to appearance-based object recognition and pose estimation. Images are considered as high-dimensional feature vectors which are transformed in various manners: we use di!erent types of non-linear image-to-image transforms composed with linear mappings to reduce the feature dimensions and to beat the curse of dimensionality. The transforms are selected such that special objective functions are optimized and available image data provide some invariance properties. The paper mainly concentrates on the comparison of preprocessing operations combined with di!erent linear projections in the context of appearance-based object recognition. The experimental evaluation provides recognition rates and pose estimation accuracy.

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## Abstract A new algorithm for object classification based on an extension of Fisher's discriminant analysis is presented. Object recognition algorithms using the standard Fisher's algorithm, such as the Fisherface, train the classifier using sample‐class pairs, where, for the classes, object cate