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Author Alex Gomez-Villa; Adrian Martin; Javier Vazquez; Marcelo Bertalmio; Jesus Malo edit  url
doi  openurl
  Title On the synthesis of visual illusions using deep generative models Type Journal Article
  Year 2022 Publication (down) Journal of Vision Abbreviated Journal JOV  
  Volume 22(8) Issue 2 Pages 1-18  
  Keywords  
  Abstract Visual illusions expand our understanding of the visual system by imposing constraints in the models in two different ways: i) visual illusions for humans should induce equivalent illusions in the model, and ii) illusions synthesized from the model should be compelling for human viewers too. These constraints are alternative strategies to find good vision models. Following the first research strategy, recent studies have shown that artificial neural network architectures also have human-like illusory percepts when stimulated with classical hand-crafted stimuli designed to fool humans. In this work we focus on the second (less explored) strategy: we propose a framework to synthesize new visual illusions using the optimization abilities of current automatic differentiation techniques. The proposed framework can be used with classical vision models as well as with more recent artificial neural network architectures. This framework, validated by psychophysical experiments, can be used to study the difference between a vision model and the actual human perception and to optimize the vision model to decrease this difference.  
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  Notes LAMP; 600.161; 611.007 Approved no  
  Call Number Admin @ si @ GMV2022 Serial 3682  
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Author Rada Deeb; Joost Van de Weijer; Damien Muselet; Mathieu Hebert; Alain Tremeau edit   pdf
url  openurl
  Title Deep spectral reflectance and illuminant estimation from self-interreflections Type Journal Article
  Year 2019 Publication (down) Journal of the Optical Society of America A Abbreviated Journal JOSA A  
  Volume 31 Issue 1 Pages 105-114  
  Keywords  
  Abstract In this work, we propose a convolutional neural network based approach to estimate the spectral reflectance of a surface and spectral power distribution of light from a single RGB image of a V-shaped surface. Interreflections happening in a concave surface lead to gradients of RGB values over its area. These gradients carry a lot of information concerning the physical properties of the surface and the illuminant. Our network is trained with only simulated data constructed using a physics-based interreflection model. Coupling interreflection effects with deep learning helps to retrieve the spectral reflectance under an unknown light and to estimate spectral power distribution of this light as well. In addition, it is more robust to the presence of image noise than classical approaches. Our results show that the proposed approach outperforms state-of-the-art learning-based approaches on simulated data. In addition, it gives better results on real data compared to other interreflection-based approaches.  
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  Notes LAMP; 600.120 Approved no  
  Call Number Admin @ si @ DWM2019 Serial 3362  
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Author AN Ruchai; VI Kober; KA Dorofeev; VN Karnaukhov; Mikhail Mozerov edit  url
doi  openurl
  Title Classification of breast abnormalities using a deep convolutional neural network and transfer learning Type Journal Article
  Year 2021 Publication (down) Journal of Communications Technology and Electronics Abbreviated Journal  
  Volume 66 Issue 6 Pages 778–783  
  Keywords  
  Abstract A new algorithm for classification of breast pathologies in digital mammography using a convolutional neural network and transfer learning is proposed. The following pretrained neural networks were chosen: MobileNetV2, InceptionResNetV2, Xception, and ResNetV2. All mammographic images were pre-processed to improve classification reliability. Transfer training was carried out using additional data augmentation and fine-tuning. The performance of the proposed algorithm for classification of breast pathologies in terms of accuracy on real data is discussed and compared with that of state-of-the-art algorithms on the available MIAS database.  
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  Notes LAMP; Approved no  
  Call Number Admin @ si @ RKD2022 Serial 3680  
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Author Muhammad Anwer Rao; Fahad Shahbaz Khan; Joost Van de Weijer; Matthieu Molinier; Jorma Laaksonen edit   pdf
url  openurl
  Title Binary patterns encoded convolutional neural networks for texture recognition and remote sensing scene classification Type Journal Article
  Year 2018 Publication (down) ISPRS Journal of Photogrammetry and Remote Sensing Abbreviated Journal ISPRS J  
  Volume 138 Issue Pages 74-85  
  Keywords Remote sensing; Deep learning; Scene classification; Local Binary Patterns; Texture analysis  
  Abstract Designing discriminative powerful texture features robust to realistic imaging conditions is a challenging computer vision problem with many applications, including material recognition and analysis of satellite or aerial imagery. In the past, most texture description approaches were based on dense orderless statistical distribution of local features. However, most recent approaches to texture recognition and remote sensing scene classification are based on Convolutional Neural Networks (CNNs). The de facto practice when learning these CNN models is to use RGB patches as input with training performed on large amounts of labeled data (ImageNet). In this paper, we show that Local Binary Patterns (LBP) encoded CNN models, codenamed TEX-Nets, trained using mapped coded images with explicit LBP based texture information provide complementary information to the standard RGB deep models. Additionally, two deep architectures, namely early and late fusion, are investigated to combine the texture and color information. To the best of our knowledge, we are the first to investigate Binary Patterns encoded CNNs and different deep network fusion architectures for texture recognition and remote sensing scene classification. We perform comprehensive experiments on four texture recognition datasets and four remote sensing scene classification benchmarks: UC-Merced with 21 scene categories, WHU-RS19 with 19 scene classes, RSSCN7 with 7 categories and the recently introduced large scale aerial image dataset (AID) with 30 aerial scene types. We demonstrate that TEX-Nets provide complementary information to standard RGB deep model of the same network architecture. Our late fusion TEX-Net architecture always improves the overall performance compared to the standard RGB network on both recognition problems. Furthermore, our final combination leads to consistent improvement over the state-of-the-art for remote sensing scene  
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  Notes LAMP; 600.109; 600.106; 600.120 Approved no  
  Call Number Admin @ si @ RKW2018 Serial 3158  
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Author Marçal Rusiñol; Volkmar Frinken; Dimosthenis Karatzas; Andrew Bagdanov; Josep Llados edit  doi
openurl 
  Title Multimodal page classification in administrative document image streams Type Journal Article
  Year 2014 Publication (down) International Journal on Document Analysis and Recognition Abbreviated Journal IJDAR  
  Volume 17 Issue 4 Pages 331-341  
  Keywords Digital mail room; Multimodal page classification; Visual and textual document description  
  Abstract In this paper, we present a page classification application in a banking workflow. The proposed architecture represents administrative document images by merging visual and textual descriptions. The visual description is based on a hierarchical representation of the pixel intensity distribution. The textual description uses latent semantic analysis to represent document content as a mixture of topics. Several off-the-shelf classifiers and different strategies for combining visual and textual cues have been evaluated. A final step uses an n-gram model of the page stream allowing a finer-grained classification of pages. The proposed method has been tested in a real large-scale environment and we report results on a dataset of 70,000 pages.  
  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 1433-2833 ISBN Medium  
  Area Expedition Conference  
  Notes DAG; LAMP; 600.056; 600.061; 601.240; 601.223; 600.077; 600.079 Approved no  
  Call Number Admin @ si @ RFK2014 Serial 2523  
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