Dipartimento d'Ingegneria

Monday, 19 November 2018 11:25

Hand-designed descriptors vs. pre-trained convolutional networks: a comparison of two strategies for colour texture classification Featured

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ELG 10 at 14:00 Tuesday 20th November 2018.
City, University of London.

Abstract:
Colour texture classification plays a fundamental role in a wide range of applications, as for instance surface inspection and grading, content-based image retrieval, computer-assisted diagnosis and remote sensing. Traditionally, the problem has been approached by manually designing suitable functions capable of extracting meaningful visual features from the input images: the so-called ‘hand-crafted’ paradigm. In recent years, however, the arrival on the scene of Convolutional Neural Networks (`Deep Learning') has brought about dramatic advances in many areas of computer vision, and has the potential to significantly change the approach to texture analysis as well. This seminar will comparatively evaluate – both at a theoretical and experimental level – the hand-crafted and Deep Learning paradigms for colour texture classification. There will be specific focus on the use of pre-trained networks as generic feature extractors for colour textures.


Abstract: Colour texture classification plays a fundamental role in a wide range of applications, as for instance surface inspection and grading, content-based image retrieval, computer-assisted diagnosis and remote sensing. Traditionally, the problem has been approached by manually designing suitable functions capable of extracting meaningful visual features from the input images: the so-called ‘hand crafted’ paradigm. In recent years, however, the arrival on the scene of Convolutional Neural Networks (`Deep Learning') has brought about dramatic advances in many areas of computer vision, and has the potential to significantly change the approach to texture analysis as well. This seminar will comparatively evaluate – both at a theoretical and experimental level – the hand-crafted and Deep Learning paradigms for colour texture classification. There will be specific focus on the use of pre-trained networks as generic feature extractors for colour textures.

Read 8101 times Last modified on Monday, 19 November 2018 11:42

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