@inproceedings{PalmMetzlerMohametal., author = {Palm, Christoph and Metzler, V. and Moham, B. and Dieker, O. and Lehmann, Thomas M. and Spitzer, Klaus}, title = {Co-Occurrence Matrizen zur Texturklassifikation in Vektorbildern}, series = {Bildverarbeitung f{\"u}r die Medizin}, booktitle = {Bildverarbeitung f{\"u}r die Medizin}, editor = {Evers, H. and Glombitza, G. and Lehmann, Thomas M. and Meinzer, H.-P.}, publisher = {Springer}, address = {Berlin}, doi = {10.1007/978-3-642-60125-5_69}, pages = {367 -- 371}, abstract = {Statistische Eigenschaften nat{\"u}rlicher Grauwerttexturen werden mit Co-Occurrence Matrizen, basierend auf der Grauwertstatistik zweiter Ordnung, modelliert. Die Matrix gibt dann die apriori Wahrscheinlichkeiten aller Grauwertpaare an. Da in der medizinischen Bildverarbeitung verst{\"a}rkt Multispektralbilder ausgewertet werden, wird das bekannte Konzept hier auf beliebige Vektorbilder erweitert. Dadurch kann bei der Texturklassifikation die zur Verf{\"u}gung stehende Information vollst{\"a}ndig genutzt werden. Insbesondere zur Detektion von Farbtexturen ist dieser Ansatz geeignet, da Wertepaare unterschiedlicher Spektralebenen ausgewertet werden k{\"o}nnen. Ebenso kann die Methode auch bei der Multiskalendekomposition von Intensit{\"a}tsbildern zur Verbesserung der Texturerkennung beitragen. Die in den Matrizen entstehenden Muster lassen dann {\"u}ber die Extraktion geeigneter Texturdeskriptoren R{\"u}ckschl{\"u}sse auf die Texturen des Bildes zu.}, language = {de} } @inproceedings{MetzlerAachPalmetal., author = {Metzler, V. and Aach, T. and Palm, Christoph and Lehmann, Thomas M.}, title = {Texture Classification of Graylevel Images by Multiscale Cross-Co-Occurrence Matrices}, series = {Proceedings 15th International Conference on Pattern Recognition (ICPR-2000)}, booktitle = {Proceedings 15th International Conference on Pattern Recognition (ICPR-2000)}, doi = {10.1109/ICPR.2000.906133}, pages = {549 -- 552}, abstract = {Local gray level dependencies of natural images can be modelled by means of co-occurrence matrices containing joint probabilities of gray-level pairs. Texture, however, is a resolution-dependent phenomenon and hence, classification depends on the chosen scale. Since there is no optimal scale for all textures we employ a multiscale approach that acquires textural features at several scales. Thus linear and nonlinear scale-spaces are analyzed by multiscale co-occurrence matrices that describe the statistical behavior of a texture in scale-space. Classification is then performed on the basis of texture features taken from the individual scale with the highest discriminatory power. By considering cross-scale occurrences of gray level pairs, the impact of filters on the feature is described and used for classification of natural textures. This novel method was found to improve classification rates of the common co-occurrence matrix approach on standard textures significantly.}, language = {en} }