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Author Fahad Shahbaz Khan; Shida Beigpour; Joost Van de Weijer; Michael Felsberg edit  doi
openurl 
  Title Painting-91: A Large Scale Database for Computational Painting Categorization Type Journal Article
  Year 2014 Publication Machine Vision and Applications Abbreviated Journal MVAP  
  Volume 25 Issue 6 Pages 1385-1397  
  Keywords  
  Abstract Computer analysis of visual art, especially paintings, is an interesting cross-disciplinary research domain. Most of the research in the analysis of paintings involve medium to small range datasets with own specific settings. Interestingly, significant progress has been made in the field of object and scene recognition lately. A key factor in this success is the introduction and availability of benchmark datasets for evaluation. Surprisingly, such a benchmark setup is still missing in the area of computational painting categorization. In this work, we propose a novel large scale dataset of digital paintings. The dataset consists of paintings from 91 different painters. We further show three applications of our dataset namely: artist categorization, style classification and saliency detection. We investigate how local and global features popular in image classification perform for the tasks of artist and style categorization. For both categorization tasks, our experimental results suggest that combining multiple features significantly improves the final performance. We show that state-of-the-art computer vision methods can correctly classify 50 % of unseen paintings to its painter in a large dataset and correctly attribute its artistic style in over 60 % of the cases. Additionally, we explore the task of saliency detection on paintings and show experimental findings using state-of-the-art saliency estimation algorithms.  
  Address  
  Corporate Author Thesis  
  Publisher Springer Berlin Heidelberg Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0932-8092 ISBN Medium  
  Area Expedition Conference  
  Notes (down) CIC; LAMP; 600.074; 600.079 Approved no  
  Call Number Admin @ si @ KBW2014 Serial 2510  
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Author C. Alejandro Parraga; Jordi Roca; Dimosthenis Karatzas; Sophie Wuerger edit   pdf
url  doi
openurl 
  Title Limitations of visual gamma corrections in LCD displays Type Journal Article
  Year 2014 Publication Displays Abbreviated Journal Dis  
  Volume 35 Issue 5 Pages 227–239  
  Keywords Display calibration; Psychophysics; Perceptual; Visual gamma correction; Luminance matching; Observer-based calibration  
  Abstract A method for estimating the non-linear gamma transfer function of liquid–crystal displays (LCDs) without the need of a photometric measurement device was described by Xiao et al. (2011) [1]. It relies on observer’s judgments of visual luminance by presenting eight half-tone patterns with luminances from 1/9 to 8/9 of the maximum value of each colour channel. These half-tone patterns were distributed over the screen both over the vertical and horizontal viewing axes. We conducted a series of photometric and psychophysical measurements (consisting in the simultaneous presentation of half-tone patterns in each trial) to evaluate whether the angular dependency of the light generated by three different LCD technologies would bias the results of these gamma transfer function estimations. Our results show that there are significant differences between the gamma transfer functions measured and produced by observers at different viewing angles. We suggest appropriate modifications to the Xiao et al. paradigm to counterbalance these artefacts which also have the advantage of shortening the amount of time spent in collecting the psychophysical measurements.  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes (down) CIC; DAG; 600.052; 600.077; 600.074 Approved no  
  Call Number Admin @ si @ PRK2014 Serial 2511  
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Author Fahad Shahbaz Khan; Muhammad Anwer Rao; Joost Van de Weijer; Andrew Bagdanov; Antonio Lopez; Michael Felsberg edit   pdf
doi  openurl
  Title Coloring Action Recognition in Still Images Type Journal Article
  Year 2013 Publication International Journal of Computer Vision Abbreviated Journal IJCV  
  Volume 105 Issue 3 Pages 205-221  
  Keywords  
  Abstract In this article we investigate the problem of human action recognition in static images. By action recognition we intend a class of problems which includes both action classification and action detection (i.e. simultaneous localization and classification). Bag-of-words image representations yield promising results for action classification, and deformable part models perform very well object detection. The representations for action recognition typically use only shape cues and ignore color information. Inspired by the recent success of color in image classification and object detection, we investigate the potential of color for action classification and detection in static images. We perform a comprehensive evaluation of color descriptors and fusion approaches for action recognition. Experiments were conducted on the three datasets most used for benchmarking action recognition in still images: Willow, PASCAL VOC 2010 and Stanford-40. Our experiments demonstrate that incorporating color information considerably improves recognition performance, and that a descriptor based on color names outperforms pure color descriptors. Our experiments demonstrate that late fusion of color and shape information outperforms other approaches on action recognition. Finally, we show that the different color–shape fusion approaches result in complementary information and combining them yields state-of-the-art performance for action classification.  
  Address  
  Corporate Author Thesis  
  Publisher Springer US Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0920-5691 ISBN Medium  
  Area Expedition Conference  
  Notes (down) CIC; ADAS; 600.057; 600.048 Approved no  
  Call Number Admin @ si @ KRW2013 Serial 2285  
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Author Hassan Ahmed Sial; Ramon Baldrich; Maria Vanrell edit   pdf
url  openurl
  Title Deep intrinsic decomposition trained on surreal scenes yet with realistic light effects Type Journal Article
  Year 2020 Publication Journal of the Optical Society of America A Abbreviated Journal JOSA A  
  Volume 37 Issue 1 Pages 1-15  
  Keywords  
  Abstract Estimation of intrinsic images still remains a challenging task due to weaknesses of ground-truth datasets, which either are too small or present non-realistic issues. On the other hand, end-to-end deep learning architectures start to achieve interesting results that we believe could be improved if important physical hints were not ignored. In this work, we present a twofold framework: (a) a flexible generation of images overcoming some classical dataset problems such as larger size jointly with coherent lighting appearance; and (b) a flexible architecture tying physical properties through intrinsic losses. Our proposal is versatile, presents low computation time, and achieves state-of-the-art results.  
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  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes (down) CIC; 600.140; 600.12; 600.118 Approved no  
  Call Number Admin @ si @ SBV2019 Serial 3311  
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Author Ivet Rafegas; Maria Vanrell; Luis A Alexandre; G. Arias edit   pdf
url  openurl
  Title Understanding trained CNNs by indexing neuron selectivity Type Journal Article
  Year 2020 Publication Pattern Recognition Letters Abbreviated Journal PRL  
  Volume 136 Issue Pages 318-325  
  Keywords  
  Abstract The impressive performance of Convolutional Neural Networks (CNNs) when solving different vision problems is shadowed by their black-box nature and our consequent lack of understanding of the representations they build and how these representations are organized. To help understanding these issues, we propose to describe the activity of individual neurons by their Neuron Feature visualization and quantify their inherent selectivity with two specific properties. We explore selectivity indexes for: an image feature (color); and an image label (class membership). Our contribution is a framework to seek or classify neurons by indexing on these selectivity properties. It helps to find color selective neurons, such as a red-mushroom neuron in layer Conv4 or class selective neurons such as dog-face neurons in layer Conv5 in VGG-M, and establishes a methodology to derive other selectivity properties. Indexing on neuron selectivity can statistically draw how features and classes are represented through layers in a moment when the size of trained nets is growing and automatic tools to index neurons can be helpful.  
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  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes (down) CIC; 600.087; 600.140; 600.118 Approved no  
  Call Number Admin @ si @ RVL2019 Serial 3310  
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