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Author Angel Sappa; Boris Vintimilla edit  openurl
  Title (up) Cost-Based Closed Contour Representations Type Journal
  Year 2007 Publication Journal of Electronic Imaging, 16(2), 023009 (9 pages) Abbreviated Journal  
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  Notes ADAS Approved no  
  Call Number ADAS @ adas @ SaV2007 Serial 803  
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Author A.F. Sole; Antonio Lopez; G. Sapiro edit   pdf
openurl 
  Title (up) Crease Enhancement Diffusion Type Journal Article
  Year 2001 Publication Computer Vision and Image Understanding, 84(2): 241–248 (IF: 1.298) Abbreviated Journal  
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  Address New York; USA  
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  Notes ADAS Approved no  
  Call Number ADAS @ adas @ SLS2001 Serial 485  
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Author Cristhian A. Aguilera-Carrasco; Angel Sappa; Cristhian Aguilera; Ricardo Toledo edit  doi
openurl 
  Title (up) Cross-Spectral Local Descriptors via Quadruplet Network Type Journal Article
  Year 2017 Publication Sensors Abbreviated Journal SENS  
  Volume 17 Issue 4 Pages 873  
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  Abstract This paper presents a novel CNN-based architecture, referred to as Q-Net, to learn local feature descriptors that are useful for matching image patches from two different spectral bands. Given correctly matched and non-matching cross-spectral image pairs, a quadruplet network is trained to map input image patches to a common Euclidean space, regardless of the input spectral band. Our approach is inspired by the recent success of triplet networks in the visible spectrum, but adapted for cross-spectral scenarios, where, for each matching pair, there are always two possible non-matching patches: one for each spectrum. Experimental evaluations on a public cross-spectral VIS-NIR dataset shows that the proposed approach improves the state-of-the-art. Moreover, the proposed technique can also be used in mono-spectral settings, obtaining a similar performance to triplet network descriptors, but requiring less training data.  
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  Notes ADAS; 600.086; 600.118 Approved no  
  Call Number Admin @ si @ ASA2017 Serial 2914  
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Author Lluis Pere de las Heras; Oriol Ramos Terrades; Sergi Robles; Gemma Sanchez edit  doi
openurl 
  Title (up) CVC-FP and SGT: a new database for structural floor plan analysis and its groundtruthing tool Type Journal Article
  Year 2015 Publication International Journal on Document Analysis and Recognition Abbreviated Journal IJDAR  
  Volume 18 Issue 1 Pages 15-30  
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  Abstract Recent results on structured learning methods have shown the impact of structural information in a wide range of pattern recognition tasks. In the field of document image analysis, there is a long experience on structural methods for the analysis and information extraction of multiple types of documents. Yet, the lack of conveniently annotated and free access databases has not benefited the progress in some areas such as technical drawing understanding. In this paper, we present a floor plan database, named CVC-FP, that is annotated for the architectural objects and their structural relations. To construct this database, we have implemented a groundtruthing tool, the SGT tool, that allows to make specific this sort of information in a natural manner. This tool has been made for general purpose groundtruthing: It allows to define own object classes and properties, multiple labeling options are possible, grants the cooperative work, and provides user and version control. We finally have collected some of the recent work on floor plan interpretation and present a quantitative benchmark for this database. Both CVC-FP database and the SGT tool are freely released to the research community to ease comparisons between methods and boost reproducible research.  
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  Publisher Springer Berlin Heidelberg Place of Publication Editor  
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  Series Volume Series Issue Edition  
  ISSN 1433-2833 ISBN Medium  
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  Notes DAG; ADAS; 600.061; 600.076; 600.077 Approved no  
  Call Number Admin @ si @ HRR2015 Serial 2567  
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Author Katerine Diaz; Jesus Martinez del Rincon; Aura Hernandez-Sabate edit  url
openurl 
  Title (up) Decremental generalized discriminative common vectors applied to images classification Type Journal Article
  Year 2017 Publication Knowledge-Based Systems Abbreviated Journal KBS  
  Volume 131 Issue Pages 46-57  
  Keywords Decremental learning; Generalized Discriminative Common Vectors; Feature extraction; Linear subspace methods; Classification  
  Abstract In this paper, a novel decremental subspace-based learning method called Decremental Generalized Discriminative Common Vectors method (DGDCV) is presented. The method makes use of the concept of decremental learning, which we introduce in the field of supervised feature extraction and classification. By efficiently removing unnecessary data and/or classes for a knowledge base, our methodology is able to update the model without recalculating the full projection or accessing to the previously processed training data, while retaining the previously acquired knowledge. The proposed method has been validated in 6 standard face recognition datasets, showing a considerable computational gain without compromising the accuracy of the model.  
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  Notes ADAS; 600.118; 600.121 Approved no  
  Call Number Admin @ si @ DMH2017a Serial 3003  
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Author Yu Jie; Jaume Amores; N. Sebe; Petia Radeva; Tian Qi edit  openurl
  Title (up) Distance Learning for Similarity Estimation Type Journal
  Year 2008 Publication IEEE Trans. on Pattern Analysis and Machine Intelligence, vol.30(3):451–462 Abbreviated Journal  
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  Notes ADAS;MILAB Approved no  
  Call Number ADAS @ adas @ JAS2008 Serial 961  
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Author Sudeep Katakol; Basem Elbarashy; Luis Herranz; Joost van de Weijer; Antonio Lopez edit   pdf
url  doi
openurl 
  Title (up) Distributed Learning and Inference with Compressed Images Type Journal Article
  Year 2020 Publication IEEE Transactions on Image Processing Abbreviated Journal TIP  
  Volume 30 Issue Pages 3069 - 3083  
  Keywords  
  Abstract arXiv:2004.10497
Modern computer vision requires processing large amounts of data, both while training the model and/or during inference, once the model is deployed. Scenarios where images are captured and processed in physically separated locations are increasingly common (e.g. autonomous vehicles, cloud computing). In addition, many devices suffer from limited resources to store or transmit data (e.g. storage space, channel capacity). In these scenarios, lossy image compression plays a crucial role to effectively increase the number of images collected under such constraints. However, lossy compression entails some undesired degradation of the data that may harm the performance of the downstream analysis task at hand, since important semantic information may be lost in the process. Moreover, we may only have compressed images at training time but are able to use original images at inference time, or vice versa, and in such a case, the downstream model suffers from covariate shift. In this paper, we analyze this phenomenon, with a special focus on vision-based perception for autonomous driving as a paradigmatic scenario. We see that loss of semantic information and covariate shift do indeed exist, resulting in a drop in performance that depends on the compression rate. In order to address the problem, we propose dataset restoration, based on image restoration with generative adversarial networks (GANs). Our method is agnostic to both the particular image compression method and the downstream task; and has the advantage of not adding additional cost to the deployed models, which is particularly important in resource-limited devices. The presented experiments focus on semantic segmentation as a challenging use case, cover a broad range of compression rates and diverse datasets, and show how our method is able to significantly alleviate the negative effects of compression on the downstream visual task.
 
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  Notes LAMP; ADAS; 600.120; 600.118 Approved no  
  Call Number Admin @ si @ KEH2020 Serial 3543  
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Author Jiaolong Xu; Sebastian Ramos; David Vazquez; Antonio Lopez edit   pdf
doi  openurl
  Title (up) Domain Adaptation of Deformable Part-Based Models Type Journal Article
  Year 2014 Publication IEEE Transactions on Pattern Analysis and Machine Intelligence Abbreviated Journal TPAMI  
  Volume 36 Issue 12 Pages 2367-2380  
  Keywords Domain Adaptation; Pedestrian Detection  
  Abstract The accuracy of object classifiers can significantly drop when the training data (source domain) and the application scenario (target domain) have inherent differences. Therefore, adapting the classifiers to the scenario in which they must operate is of paramount importance. We present novel domain adaptation (DA) methods for object detection. As proof of concept, we focus on adapting the state-of-the-art deformable part-based model (DPM) for pedestrian detection. We introduce an adaptive structural SVM (A-SSVM) that adapts a pre-learned classifier between different domains. By taking into account the inherent structure in feature space (e.g., the parts in a DPM), we propose a structure-aware A-SSVM (SA-SSVM). Neither A-SSVM nor SA-SSVM needs to revisit the source-domain training data to perform the adaptation. Rather, a low number of target-domain training examples (e.g., pedestrians) are used. To address the scenario where there are no target-domain annotated samples, we propose a self-adaptive DPM based on a self-paced learning (SPL) strategy and a Gaussian Process Regression (GPR). Two types of adaptation tasks are assessed: from both synthetic pedestrians and general persons (PASCAL VOC) to pedestrians imaged from an on-board camera. Results show that our proposals avoid accuracy drops as high as 15 points when comparing adapted and non-adapted detectors.  
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  ISSN 0162-8828 ISBN Medium  
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  Notes ADAS; 600.057; 600.054; 601.217; 600.076 Approved no  
  Call Number ADAS @ adas @ XRV2014b Serial 2436  
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Author Joan Serrat; Ferran Diego; Felipe Lumbreras; Jose Manuel Alvarez; Antonio Lopez; C. Elvira edit   pdf
openurl 
  Title (up) Dynamic Comparison of Headlights Type Journal Article
  Year 2008 Publication Journal of Automobile Engineering Abbreviated Journal  
  Volume 222 Issue 5 Pages 643–656  
  Keywords video alignment  
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  Notes ADAS Approved no  
  Call Number ADAS @ adas @ SDL2008a Serial 958  
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Author A. Restrepo; Angel Sappa; M. Devy edit  url
openurl 
  Title (up) Edge registration versus triangular mesh registration, a comparative study Type Journal
  Year 2005 Publication Signal Processing: Image Communication 20: 853–868 (IF: 1.264) Abbreviated Journal  
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  Notes ADAS Approved no  
  Call Number ADAS @ adas @ RSD2005 Serial 567  
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