Blur Insensitive Texture Classification Using Local Phase Quantization View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2008

AUTHORS

Ville Ojansivu , Janne Heikkilä

ABSTRACT

In this paper, we propose a new descriptor for texture classification that is robust to image blurring. The descriptor utilizes phase information computed locally in a window for every image position. The phases of the four low-frequency coefficients are decorrelated and uniformly quantized in an eight-dimensional space. A histogram of the resulting code words is created and used as a feature in texture classification. Ideally, the low-frequency phase components are shown to be invariant to centrally symmetric blur. Although this ideal invariance is not completely achieved due to the finite window size, the method is still highly insensitive to blur. Because only phase information is used, the method is also invariant to uniform illumination changes. According to our experiments, the classification accuracy of blurred texture images is much higher with the new method than with the well-known LBP or Gabor filter bank methods. Interestingly, it is also slightly better for textures that are not blurred. More... »

PAGES

236-243

Book

TITLE

Image and Signal Processing

ISBN

978-3-540-69904-0
978-3-540-69905-7

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-540-69905-7_27

DOI

http://dx.doi.org/10.1007/978-3-540-69905-7_27

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1009665193


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