Dipartimento d'Ingegneria

Grey-level co-occurrence matrices (GLCM) have been on the scene for almost forty years and continue to be widely used today. We developed a method to improve accuracy and robustness against rotation of GLCM features for image classification. Co-occurrences are computed through digital circles as an alternative to the standard four directions. We use discrete Fourier transform normalization to convert rotation dependent features into rotation invariant ones. We tested or method on four different datasets of natural and synthetic images. Experimental results show that our approach is more accurate and robust against rotation than the standard GLCM features.


F Bianconi and A. Fernández, Rotation invariant co-occurrence features based on digital circles and discrete Fourier transform, Pattern Recognition Letters, 48:34-41,2014. Special issue 'Celebrating the life and work of Maria Petrou'.
Code and data available here.
Pubblicato in Research
Lunedì, 05 Maggio 2014 11:48

IMVIP 2014

The Irish Machine Vision and Image Processing conference (IMVIP 2014) will be hosted by the Intelligent Systems Research Centre (ISRC) at the University of Ulster, Magee from 27th-29th August 2014. IMVIP 2014 is the main conference of the Irish Pattern Recognition and Classification Society. The conference emphasises both theoretical research results and practical engineering experience in all areas of machine vision and image processing. Call for papers and additional information can be found at https://sites.google.com/site/imvip2014/. Please note that the deadline for submissions has been extended to Wednesday 14th May 2014.
Pubblicato in Research
Venerdì, 02 Maggio 2014 17:30

Texture databases for benchmarking

Texture analysis is an area of intense research activity. Like in other fields, the availability of public data for benchmarking is vital to the development of the discipline. In ‘‘Texture databases – A comprehensive survey’’, Hossain and Serikawa recently provided a precious review of a good number of texture datasets. We have recently proposed an appendix to complement the cited work by providing reference to additional image databases of bio-medical textures, textures of materials and natural textures that have been recently employed in experiments with texture analysis.

F Bianconi and A. Fernández, An appendix to ‘‘Texture databases – A comprehensive survey’’, Pattern Recognition Letters, 45(1):33-38,2014
Pubblicato in Research
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