[Faculty] Fwd: [CSRC.COLLOQUIUM] "Unsupervised and Supervised Texture Segmentation/Classification"

[Jose Castillo]

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DATE:  *Friday, November 8, 2019*



TITLE:

*Unsupervised and Supervised Texture Segmentation/Classification*


TIME:  *3:30-4:30PM*




LOCATION:
*GMCS 314*




SPEAKER/BIO:



*Dr. Jerome Gilles, Associate Professor, Department of Mathematics and
Statistics, SDSU*




ABSTRACT:

Texture analysis in image processing remains a challenging problem mainly
due to the fact that textures can vary a lot which makes difficult to find
a mathematical model for textures. However, the community usually agree
that some characteristics like orientation, periodicity and scale are very
important to characterize textures. These observations have naturally led
to the use of wavelet to extract features suitable for textures. Such
features can then be used to feed either an unsupervised or supervised
classifier to perform texture segmentation/classification. In this work, we
show that empirical wavelets are particularly relevant to characterize
textures since they are well adapted to capture "optimized" features. We
then use those feature with standard classifier like k-means or the Nystrom
algorithm to perform unsupervised texture segmentation. Finally, we
introduce the use of these features to train neural networks to classify
textures. We show that such easy change largely boost existing neural
networks and permits reaching performances close to the perfection.



Host: Jose Castillo

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