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Proceedings Paper

Analytical approach to classification by object reconstruction from features
Author(s): Albrecht Melan; Stephan Rudolph
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Paper Abstract

Classification is a central task in pattern recognition. To classify objects into object classes, features are calculated from objects. Objects classes are determined by class boundaries. If it is thus possible to reconstruct objects from their features, variations of feature on their objects and on class boundaries can be studied explicitly. In this work the classical steps in pattern recognition form object space to feature space are extended by the concept of physical similarity and by a back-transform form feature space to object space. The analytic assumptions and numeric properties of this back-transformation from feature space into object space are investigated using gray scale images. Higher moments of these grey scale images are computed and later used for reconstruction. When a grey scale image is written as a discrete valued 2D function, the function lies in the Hilbert space of quadratic integrable functions. Quadratic integrable functions can be written as a series of orthonormal functions, where the coefficients of the series are calculated using a scalar product of the image and the orthonormal base functions. Using Legendre polynomials as base functions, the scalar products for the determination of the series' coefficients can be calculated from the moments and the polynomials coefficients only thus yielding the back-transformation.

Paper Details

Date Published: 4 August 2000
PDF: 9 pages
Proc. SPIE 4052, Signal Processing, Sensor Fusion, and Target Recognition IX, (4 August 2000); doi: 10.1117/12.395061
Show Author Affiliations
Albrecht Melan, Univ. of Stuttgart (Germany)
Stephan Rudolph, Univ. of Stuttgart (Germany)

Published in SPIE Proceedings Vol. 4052:
Signal Processing, Sensor Fusion, and Target Recognition IX
Ivan Kadar, Editor(s)

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