A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Ten lectures on wavelets
Local feature extraction and its applications using a library of bases
Local feature extraction and its applications using a library of bases
Feature Extraction for Bank Note Classification Using Wavelet Transform
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Texture classification and segmentation using wavelet frames
IEEE Transactions on Image Processing
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Segmentation and feature extraction algorithms based on Wavelet Transform or Wavelet Packet Transform are established in pattern recognition. Especially in the field of texture analysis they are known to be practical. One difficulty of texture analysis was in the past the characterization of different printing processes. In this paper we present a new algorithmic concept to feature extraction of textures, printed by different printing techniques, without the necessity of a previous teaching phase. The typical characters of distinct printed textures are extracted by first order statistical moments of wavelet coefficients. The algorithm uses the 2D incomplete shift invariant Wavelet Packet Transform, resulting in a fast execution time of O(N log2 (N )). Since the incomplete shift invariant Wavelet Packet Transform was exclusively defined for 1D-signals, it has been modified in this research. The application describes the detection of different printed security textures.