Dipartimento d'Ingegneria

Bianconi Francesco

Bianconi Francesco

Received the M.Eng. degree from the University of Perugia (Italy) and the Ph.D. in Computer-aided Design from a consortium of Italian universities. He has been visiting researcher at the University of Vigo (Spain) and the University of East Anglia (UK). Currently, he is Lecturer within the Department of Engineering of the University of Perugia. His research interests include computer vision, image processing and pattern recognition, with a special focus on texture and colour analysis. He is IEEE senior member.
URL del sito web: http://dismac.dii.unipg.it/bianco Email: Questo indirizzo email è protetto dagli spambots. E' necessario abilitare JavaScript per vederlo.

Altre informazioni

  • Telefono
    075 585 3706
  • Ruolo
    Professore associato - Associate professor
  • Area
    Progettazione industriale, costruzioni meccaniche e metallurgia - Engineering design and metallurgy
  • Curriculum Vitae

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.

Intense research is being conducted throughout the world to identify biomarkers seen on computed tomography (CT) imaging that can help predict prognoses in patients with non-small-cell lung cancer (NSCLC). Such biomarkers could help improve and personalize treatment plans for patients. A team of radiologists, engineers, and biomedical specialists at the University of Perugia evaluated 30 three-dimensional shape and textural CT-derived features as potential biomarkers predictive of overall survival of 203 NSCLC patients. Find out more.
Secondo il nuovo rapporto di Almalaurea il 93,6% dei laureati magistrali in Ingegneria trova occupazione dopo un anno dalla fine degli studi e con lo stipendio più alto di tutti gli altri laureati (€ 1.717 in media netti al mese). Anche i laureati triennali hanno ottimi risultati con il 76% occupati ed uno stipendio medio di € 1.283.
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