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Author (up) Y. Mori; M.Misawa; Jorge Bernal; M. Bretthauer; S.Kudo; A. Rastogi; Gloria Fernandez Esparrach edit  url
doi  openurl
  Title Artificial Intelligence for Disease Diagnosis-the Gold Standard Challenge Type Journal Article
  Year 2022 Publication Gastrointestinal Endoscopy Abbreviated Journal  
  Volume 96 Issue 2 Pages 370-372  
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  Area Expedition Conference  
  Notes ISE Approved no  
  Call Number Admin @ si @ MMB2022 Serial 3701  
Permanent link to this record
 

 
Author (up) Yecong Wan; Yuanshuo Cheng; Miingwen Shao; Jordi Gonzalez edit  doi
openurl 
  Title Image rain removal and illumination enhancement done in one go Type Journal Article
  Year 2022 Publication Knowledge-Based Systems Abbreviated Journal KBS  
  Volume 252 Issue Pages 109244  
  Keywords  
  Abstract Rain removal plays an important role in the restoration of degraded images. Recently, CNN-based methods have achieved remarkable success. However, these approaches neglect that the appearance of real-world rain is often accompanied by low light conditions, which will further degrade the image quality, thereby hindering the restoration mission. Therefore, it is very indispensable to jointly remove the rain and enhance illumination for real-world rain image restoration. To this end, we proposed a novel spatially-adaptive network, dubbed SANet, which can remove the rain and enhance illumination in one go with the guidance of degradation mask. Meanwhile, to fully utilize negative samples, a contrastive loss is proposed to preserve more natural textures and consistent illumination. In addition, we present a new synthetic dataset, named DarkRain, to boost the development of rain image restoration algorithms in practical scenarios. DarkRain not only contains different degrees of rain, but also considers different lighting conditions, and more realistically simulates real-world rainfall scenarios. SANet is extensively evaluated on the proposed dataset and attains new state-of-the-art performance against other combining methods. Moreover, after a simple transformation, our SANet surpasses existing the state-of-the-art algorithms in both rain removal and low-light image enhancement.  
  Address Sept 2022  
  Corporate Author Thesis  
  Publisher Elsevier Place of Publication Editor  
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  Area Expedition Conference  
  Notes ISE; 600.157; 600.168 Approved no  
  Call Number Admin @ si @ WCS2022 Serial 3744  
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Author (up) Zeynep Yucel; Albert Ali Salah; Çetin Meriçli; Tekin Meriçli; Roberto Valenti; Theo Gevers edit  doi
openurl 
  Title Joint Attention by Gaze Interpolation and Saliency Type Journal
  Year 2013 Publication IEEE Transactions on cybernetics Abbreviated Journal T-CIBER  
  Volume 43 Issue 3 Pages 829-842  
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  Abstract Joint attention, which is the ability of coordination of a common point of reference with the communicating party, emerges as a key factor in various interaction scenarios. This paper presents an image-based method for establishing joint attention between an experimenter and a robot. The precise analysis of the experimenter's eye region requires stability and high-resolution image acquisition, which is not always available. We investigate regression-based interpolation of the gaze direction from the head pose of the experimenter, which is easier to track. Gaussian process regression and neural networks are contrasted to interpolate the gaze direction. Then, we combine gaze interpolation with image-based saliency to improve the target point estimates and test three different saliency schemes. We demonstrate the proposed method on a human-robot interaction scenario. Cross-subject evaluations, as well as experiments under adverse conditions (such as dimmed or artificial illumination or motion blur), show that our method generalizes well and achieves rapid gaze estimation for establishing joint attention.  
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  ISSN 2168-2267 ISBN Medium  
  Area Expedition Conference  
  Notes ALTRES;ISE Approved no  
  Call Number Admin @ si @ YSM2013 Serial 2363  
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