We present a practical pattern recognition system that is invariant with respect to translation, scale and rotation of objects. The system is also insensitive to large variations of the threshold used. As feature vectors, Zernike moments are used and we compare them with Hu's seven moment invariants. For a practical machine vision system, three key issues are discussed: pattern normalization, fast computation of Zernike moments, and classification using k-NN rule. As testing results, the system recognizes a set of 62 alphanumeric machine-printed characters with different sizes, at arbitrary orientations, and with different thresholds where the size of the characters varies from 10/spl times/10 to 512/spl times/512 pixels.<>
A practical pattern recognition system for translation, scale and rotation invariance
01.01.1994
503023 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
A Practical Pattern Recognition System for Translation, Scale, and Rotation Invariance
British Library Conference Proceedings | 1994
|Pattern recognition method is rotation-invariant
British Library Online Contents | 2001
A Neural Architecture for Pattern Recognition Insensitive to Translation, Scale, and Line Thickness
British Library Online Contents | 1994
|