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A. Laine, J. Fan, "Texture Classification by Wavelet Packet Signatures," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 15, no. 11, pp. 11861191, November, 1993.  
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@article{ 10.1109/34.244679, author = {A. Laine and J. Fan}, title = {Texture Classification by Wavelet Packet Signatures}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {15}, number = {11}, issn = {01628828}, year = {1993}, pages = {11861191}, doi = {http://doi.ieeecomputersociety.org/10.1109/34.244679}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
RefWorks Procite/RefMan/Endnote  x  
TY  JOUR JO  IEEE Transactions on Pattern Analysis and Machine Intelligence TI  Texture Classification by Wavelet Packet Signatures IS  11 SN  01628828 SP1186 EP1191 EPD  11861191 A1  A. Laine, A1  J. Fan, PY  1993 KW  texture classification; wavelet packet signatures; scaleindependence; wavelet packet spaces; sensitivity; selectivity; energy metrics; entropy metrics; scale space representations; scale sensitivity; orientation sensitivity; twolayer network classifier; feature extraction; feedforward neural nets; image recognition; wavelet transforms VL  15 JA  IEEE Transactions on Pattern Analysis and Machine Intelligence ER   
This correspondence introduces a new approach to characterize textures at multiple scales. The performance of wavelet packet spaces are measured in terms of sensitivity and selectivity for the classification of twentyfive natural textures. Both energy and entropy metrics were computed for each wavelet packet and incorporated into distinct scale space representations, where each wavelet packet (channel) reflected a specific scale and orientation sensitivity. Wavelet packet representations for twentyfive natural textures were classified without error by a simple twolayer network classifier. An analyzing function of large regularity (D/sub 20/) was shown to be slightly more efficient in representation and discrimination than a similar function with fewer vanishing moments (D/sub 6/) In addition, energy representations computed from the standard wavelet decomposition alone (17 features) provided classification without error for the twentyfive textures included in our study. The reliability exhibited by texture signatures based on wavelet packets analysis suggest that the multiresolution properties of such transforms are beneficial for accomplishing segmentation, classification and subtle discrimination of texture.
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