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Author Bogdan Raducanu; Jordi Vitria edit  openurl
  Title Face Recognition by Artificial Vision Systems: A Cognitive Perspective Type (up) Journal
  Year 2008 Publication International Journal of Pattern Recognition and Artificial Intelligence Abbreviated Journal IJPRAI  
  Volume 22 Issue 5 Pages 899–913  
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  Notes OR;MV Approved no  
  Call Number BCNPCL @ bcnpcl @ RaV2008b Serial 1007  
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Author Fadi Dornaika; Bogdan Raducanu edit  openurl
  Title 3D Face Pose Detection and Tracking Using Monocular Videos: Tool and Application Type (up) Journal
  Year 2008 Publication IEEE Transactions on Systems, Man and Cybernetics (Part B) (IEEE) Abbreviated Journal  
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  Notes OR;MV Approved no  
  Call Number BCNPCL @ bcnpcl @ DoR2008d Serial 1109  
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Author Juan Ramon Terven Salinas; Joaquin Salas; Bogdan Raducanu edit   pdf
openurl 
  Title Estado del Arte en Sistemas de Vision Artificial para Personas Invidentes Type (up) Journal
  Year 2013 Publication Komputer Sapiens Abbreviated Journal KS  
  Volume 1 Issue Pages 20-25  
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  Notes OR;MV Approved no  
  Call Number Admin @ si @ TSR2013 Serial 2231  
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Author R. Clariso; David Masip; A. Rius edit  url
openurl 
  Title Student projects empowering mobile learning in higher education Type (up) Journal
  Year 2014 Publication Revista de Universidad y Sociedad del Conocimiento Abbreviated Journal RUSC  
  Volume 11 Issue Pages 192-207  
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  ISSN 1698-580X ISBN Medium  
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  Notes OR;MV Approved no  
  Call Number Admin @ si @ CMR2014 Serial 2619  
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Author Patrick Brandao; O. Zisimopoulos; E. Mazomenos; G. Ciutib; Jorge Bernal; M. Visentini-Scarzanell; A. Menciassi; P. Dario; A. Koulaouzidis; A. Arezzo; D.J. Hawkes; D. Stoyanov edit   pdf
url  doi
openurl 
  Title Towards a computed-aided diagnosis system in colonoscopy: Automatic polyp segmentation using convolution neural networks Type (up) Journal
  Year 2018 Publication Journal of Medical Robotics Research Abbreviated Journal JMRR  
  Volume 3 Issue 2 Pages  
  Keywords convolutional neural networks; colonoscopy; computer aided diagnosis  
  Abstract Early diagnosis is essential for the successful treatment of bowel cancers including colorectal cancer (CRC) and capsule endoscopic imaging with robotic actuation can be a valuable diagnostic tool when combined with automated image analysis. We present a deep learning rooted detection and segmentation framework for recognizing lesions in colonoscopy and capsule endoscopy images. We restructure established convolution architectures, such as VGG and ResNets, by converting them into fully-connected convolution networks (FCNs), ne-tune them and study their capabilities for polyp segmentation and detection. We additionally use Shape-from-Shading (SfS) to recover depth and provide a richer representation of the tissue's structure in colonoscopy images. Depth is
incorporated into our network models as an additional input channel to the RGB information and we demonstrate that the resulting network yields improved performance. Our networks are tested on publicly available datasets and the most accurate segmentation model achieved a mean segmentation IU of 47.78% and 56.95% on the ETIS-Larib and CVC-Colon datasets, respectively. For polyp
detection, the top performing models we propose surpass the current state of the art with detection recalls superior to 90% for all datasets tested. To our knowledge, we present the rst work to use FCNs for polyp segmentation in addition to proposing a novel combination of SfS and RGB that boosts performance.		  
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  Notes MV; no menciona Approved no  
  Call Number BZM2018 Serial 2976  
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