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Author Daniel Ponsa; Robert Benavente; Felipe Lumbreras; J. Martinez; Xavier Roca edit  openurl
  Title Quality control of safety belts by machine vision inspection for real-time production Type Journal
  Year 2003 Publication (up) Optical Engineering, 42:1114–1120 (IF: 0.877) Abbreviated Journal  
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  Area Expedition Conference  
  Notes ADAS;ISE;CIC Approved no  
  Call Number ADAS @ adas @ PRL2003 Serial 399  
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Author Mikhail Mozerov; V. Kober edit  openurl
  Title Impulse Noise Removal with Gradient Adaptive Neighborhoods Type Journal
  Year 2006 Publication (up) Optical Engineering, 45: 67003 Abbreviated Journal  
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  Notes ISE Approved no  
  Call Number ISE @ ise @ MoK2006 Serial 676  
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Author Mikhail Mozerov; Ariel Amato; Xavier Roca; Jordi Gonzalez edit  openurl
  Title Trajectory Occlusion Handling with Multiple View Distance Minimisation Clustering Type Journal
  Year 2008 Publication (up) Optical Engineering, vol. 47(04)04702, DOI:10.11781.2909665 Abbreviated Journal  
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  Notes ISE Approved no  
  Call Number ISE @ ise @ MAR2008c Serial 970  
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Author X. Varona; Jordi Gonzalez; Ignasi Rius; Juan J. Villanueva edit  openurl
  Title Importance of Detection for Video Surveillance Applications Type Journal
  Year 2008 Publication (up) Optical Engineering, vol. 47(8), 087201/1–9 Abbreviated Journal  
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  Notes Approved no  
  Call Number ISE @ ise @ VGR2008 Serial 998  
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Author Xavier Roca; X. Binefa; Jordi Vitria edit  openurl
  Title A New Autofocus Algorithm for Cytological Tissue in a Microscopy Environment. Type Miscellaneous
  Year 1998 Publication (up) Optical Engineering. Abbreviated Journal  
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  Notes OR;ISE;MV Approved no  
  Call Number BCNPCL @ bcnpcl @ RBV1998 Serial 16  
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Author Javier Vazquez; Graham D. Finlayson; Luis Herranz edit  url
openurl 
  Title Improving the perception of low-light enhanced images Type Journal Article
  Year 2024 Publication (up) Optics Express Abbreviated Journal  
  Volume 32 Issue 4 Pages 5174-5190  
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  Abstract Improving images captured under low-light conditions has become an important topic in computational color imaging, as it has a wide range of applications. Most current methods are either based on handcrafted features or on end-to-end training of deep neural networks that mostly focus on minimizing some distortion metric —such as PSNR or SSIM— on a set of training images. However, the minimization of distortion metrics does not mean that the results are optimal in terms of perception (i.e. perceptual quality). As an example, the perception-distortion trade-off states that, close to the optimal results, improving distortion results in worsening perception. This means that current low-light image enhancement methods —that focus on distortion minimization— cannot be optimal in the sense of obtaining a good image in terms of perception errors. In this paper, we propose a post-processing approach in which, given the original low-light image and the result of a specific method, we are able to obtain a result that resembles as much as possible that of the original method, but, at the same time, giving an improvement in the perception of the final image. More in detail, our method follows the hypothesis that in order to minimally modify the perception of an input image, any modification should be a combination of a local change in the shading across a scene and a global change in illumination color. We demonstrate the ability of our method quantitatively using perceptual blind image metrics such as BRISQUE, NIQE, or UNIQUE, and through user preference tests.  
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  Notes MACO Approved no  
  Call Number Admin @ si @ VFH2024 Serial 4018  
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Author M.J. Yzuel; J. Pladellorens; Joan Serrat; A. Dupuy edit  openurl
  Title Application restauration and edge detection techniques in the calculation of left ventricular volumes. Type Conference Article
  Year 1993 Publication (up) Optics in Medicine, Biology and Environmental Research : Selected contributions to the first International Conference on Optics within Life Sciences (OWLS I) Abbreviated Journal  
  Volume Issue Pages 374-375  
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  Publisher Elsevier Place of Publication Editor  
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  Notes ADAS Approved no  
  Call Number ADAS @ adas @ YPS1993 Serial 244  
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Author Esmitt Ramirez; Carles Sanchez; Agnes Borras; Marta Diez-Ferrer; Antoni Rosell; Debora Gil edit   pdf
url  openurl
  Title Image-Based Bronchial Anatomy Codification for Biopsy Guiding in Video Bronchoscopy Type Conference Article
  Year 2018 Publication (up) OR 2.0 Context-Aware Operating Theaters, Computer Assisted Robotic Endoscopy, Clinical Image-Based Procedures, and Skin Image Analysis Abbreviated Journal  
  Volume 11041 Issue Pages  
  Keywords Biopsy guiding; Bronchoscopy; Lung biopsy; Intervention guiding; Airway codification  
  Abstract Bronchoscopy examinations allow biopsy of pulmonary nodules with minimum risk for the patient. Even for experienced bronchoscopists, it is difficult to guide the bronchoscope to most distal lesions and obtain an accurate diagnosis. This paper presents an image-based codification of the bronchial anatomy for bronchoscopy biopsy guiding. The 3D anatomy of each patient is codified as a binary tree with nodes representing bronchial levels and edges labeled using their position on images projecting the 3D anatomy from a set of branching points. The paths from the root to leaves provide a codification of navigation routes with spatially consistent labels according to the anatomy observes in video bronchoscopy explorations. We evaluate our labeling approach as a guiding system in terms of the number of bronchial levels correctly codified, also in the number of labels-based instructions correctly supplied, using generalized mixed models and computer-generated data. Results obtained for three independent observers prove the consistency and reproducibility of our guiding system. We trust that our codification based on viewer’s projection might be used as a foundation for the navigation process in Virtual Bronchoscopy systems.  
  Address Granada; September 2018  
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  Series Editor Series Title Abbreviated Series Title LNCS  
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  Area Expedition Conference MICCAIW  
  Notes IAM; 600.096; 600.075; 601.323; 600.145 Approved no  
  Call Number Admin @ si @ RSB2018b Serial 3137  
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Author S.Grau; Ana Puig; Sergio Escalera; Maria Salamo edit   pdf
url  doi
isbn  openurl
  Title Intelligent Interactive Volume Classification Type Conference Article
  Year 2013 Publication (up) Pacific Graphics Abbreviated Journal  
  Volume 32 Issue 7 Pages 23-28  
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  Abstract This paper defines an intelligent and interactive framework to classify multiple regions of interest from the original data on demand, without requiring any preprocessing or previous segmentation. The proposed intelligent and interactive approach is divided in three stages: visualize, training and testing. First, users visualize and label some samples directly on slices of the volume. Training and testing are based on a framework of Error Correcting Output Codes and Adaboost classifiers that learn to classify each region the user has painted. Later, at the testing stage, each classifier is directly applied on the rest of samples and combined to perform multi-class labeling, being used in the final rendering. We also parallelized the training stage using a GPU-based implementation for
obtaining a rapid interaction and classification.
 
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  ISSN ISBN 978-3-905674-50-7 Medium  
  Area Expedition Conference PG  
  Notes HuPBA; 600.046;MILAB Approved no  
  Call Number Admin @ si @ GPE2013b Serial 2355  
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Author Bogdan Raducanu; Jordi Vitria edit  openurl
  Title Learning to Learn: From Smarts Machines to Intelligent Machines Type Journal
  Year 2008 Publication (up) Patter Recognition Letters Abbreviated Journal PRL  
  Volume 29 Issue 8 Pages 1024–1032  
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  Notes OR;MV Approved no  
  Call Number BCNPCL @ bcnpcl @ RaV2008a Serial 950  
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Author H.Martin Kjer; Jens Fagertuna; Sergio Vera; Debora Gil; Miguel Angel Gonzalez Ballester; Rasmus R. Paulsena edit   pdf
url  openurl
  Title Free-form image registration of human cochlear uCT data using skeleton similarity as anatomical prior Type Journal Article
  Year 2016 Publication (up) Patter Recognition Letters Abbreviated Journal PRL  
  Volume 76 Issue 1 Pages 76-82  
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  Notes IAM; 600.060 Approved no  
  Call Number Admin @ si @ MFV2017b Serial 2941  
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Author Marçal Rusiñol; Josep Llados; Gemma Sanchez edit  doi
openurl 
  Title Symbol Spotting in Vectorized Technical Drawings Through a Lookup Table of Region Strings Type Journal Article
  Year 2010 Publication (up) Pattern Analysis and Applications Abbreviated Journal PAA  
  Volume 13 Issue 3 Pages 321-331  
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  Abstract In this paper, we address the problem of symbol spotting in technical document images applied to scanned and vectorized line drawings. Like any information spotting architecture, our approach has two components. First, symbols are decomposed in primitives which are compactly represented and second a primitive indexing structure aims to efficiently retrieve similar primitives. Primitives are encoded in terms of attributed strings representing closed regions. Similar strings are clustered in a lookup table so that the set median strings act as indexing keys. A voting scheme formulates hypothesis in certain locations of the line drawing image where there is a high presence of regions similar to the queried ones, and therefore, a high probability to find the queried graphical symbol. The proposed approach is illustrated in a framework consisting in spotting furniture symbols in architectural drawings. It has been proved to work even in the presence of noise and distortion introduced by the scanning and raster-to-vector processes.  
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  Publisher Springer-Verlag Place of Publication Editor  
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  ISSN 1433-7541 ISBN Medium  
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  Notes DAG Approved no  
  Call Number DAG @ dag @ RLS2010 Serial 1165  
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Author Eloi Puertas; Sergio Escalera; Oriol Pujol edit   pdf
url  doi
openurl 
  Title Generalized Multi-scale Stacked Sequential Learning for Multi-class Classification Type Journal Article
  Year 2015 Publication (up) Pattern Analysis and Applications Abbreviated Journal PAA  
  Volume 18 Issue 2 Pages 247-261  
  Keywords Stacked sequential learning; Multi-scale; Error-correct output codes (ECOC); Contextual classification  
  Abstract In many classification problems, neighbor data labels have inherent sequential relationships. Sequential learning algorithms take benefit of these relationships in order to improve generalization. In this paper, we revise the multi-scale sequential learning approach (MSSL) for applying it in the multi-class case (MMSSL). We introduce the error-correcting output codesframework in the MSSL classifiers and propose a formulation for calculating confidence maps from the margins of the base classifiers. In addition, we propose a MMSSL compression approach which reduces the number of features in the extended data set without a loss in performance. The proposed methods are tested on several databases, showing significant performance improvement compared to classical approaches.  
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  Publisher Springer-Verlag Place of Publication Editor  
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  ISSN 1433-7541 ISBN Medium  
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  Notes HuPBA;MILAB Approved no  
  Call Number Admin @ si @ PEP2013 Serial 2251  
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Author Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera edit  doi
openurl 
  Title Combining Local and Global Learners in the Pairwise Multiclass Classification Type Journal Article
  Year 2015 Publication (up) Pattern Analysis and Applications Abbreviated Journal PAA  
  Volume 18 Issue 4 Pages 845-860  
  Keywords Multiclass classification; Pairwise approach; One-versus-one  
  Abstract Pairwise classification is a well-known class binarization technique that converts a multiclass problem into a number of two-class problems, one problem for each pair of classes. However, in the pairwise technique, nuisance votes of many irrelevant classifiers may result in a wrong class prediction. To overcome this problem, a simple, but efficient method is proposed and evaluated in this paper. The proposed method is based on excluding some classes and focusing on the most probable classes in the neighborhood space, named Local Crossing Off (LCO). This procedure is performed by employing a modified version of standard K-nearest neighbor and large margin nearest neighbor algorithms. The LCO method takes advantage of nearest neighbor classification algorithm because of its local learning behavior as well as the global behavior of powerful binary classifiers to discriminate between two classes. Combining these two properties in the proposed LCO technique will avoid the weaknesses of each method and will increase the efficiency of the whole classification system. On several benchmark datasets of varying size and difficulty, we found that the LCO approach leads to significant improvements using different base learners. The experimental results show that the proposed technique not only achieves better classification accuracy in comparison to other standard approaches, but also is computationally more efficient for tackling classification problems which have a relatively large number of target classes.  
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  Publisher Springer London Place of Publication Editor  
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  ISSN 1433-7541 ISBN Medium  
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  Notes HuPBA;MILAB Approved no  
  Call Number Admin @ si @ BGE2014 Serial 2441  
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Author Alejandro Cartas; Juan Marin; Petia Radeva; Mariella Dimiccoli edit   pdf
url  openurl
  Title Batch-based activity recognition from egocentric photo-streams revisited Type Journal Article
  Year 2018 Publication (up) Pattern Analysis and Applications Abbreviated Journal PAA  
  Volume 21 Issue 4 Pages 953–965  
  Keywords Egocentric vision; Lifelogging; Activity recognition; Deep learning; Recurrent neural networks  
  Abstract Wearable cameras can gather large amounts of image data that provide rich visual information about the daily activities of the wearer. Motivated by the large number of health applications that could be enabled by the automatic recognition of daily activities, such as lifestyle characterization for habit improvement, context-aware personal assistance and tele-rehabilitation services, we propose a system to classify 21 daily activities from photo-streams acquired by a wearable photo-camera. Our approach combines the advantages of a late fusion ensemble strategy relying on convolutional neural networks at image level with the ability of recurrent neural networks to account for the temporal evolution of high-level features in photo-streams without relying on event boundaries. The proposed batch-based approach achieved an overall accuracy of 89.85%, outperforming state-of-the-art end-to-end methodologies. These results were achieved on a dataset consists of 44,902 egocentric pictures from three persons captured during 26 days in average.  
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  Notes MILAB; no proj Approved no  
  Call Number Admin @ si @ CMR2018 Serial 3186  
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