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Author J. Mauri; Eduard Fernandez-Nofrerias; J. Comin; B. Garcia del Blanco; E. Iraculis; J.A. Gomez-Hospital; P. Valdovinos; F. Jara; A. Cequier; E. Esplugas; Oriol Pujol; Cristina Cañero; Debora Gil; Petia Radeva; Ricardo Toledo
Title (up) Avaluació del Conjunt Stent/Artèria mitjançant ecografia intracoronària: lentorn informàtic Type Conference Article
Year 2000 Publication Congrés de la Societat Catalana de Cardiologia. Abbreviated Journal
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Notes IAM;RV;MILAB;ADAS;HuPBA Approved no
Call Number IAM @ iam @ MNC2000 Serial 1622
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Author Marçal Rusiñol; Lluis Gomez
Title (up) Avances en clasificación de imágenes en los últimos diez años. Perspectivas y limitaciones en el ámbito de archivos fotográficos históricos Type Journal
Year 2018 Publication Revista anual de la Asociación de Archiveros de Castilla y León Abbreviated Journal
Volume 21 Issue Pages 161-174
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Notes DAG; 600.121; 600.129 Approved no
Call Number Admin @ si @ RuG2018 Serial 3239
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Author Carles Sanchez; Miguel Viñas; Coen Antens; Agnes Borras; Debora Gil
Title (up) Back to Front Architecture for Diagnosis as a Service Type Conference Article
Year 2018 Publication 20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing Abbreviated Journal
Volume Issue Pages 343-346
Keywords
Abstract Software as a Service (SaaS) is a cloud computing model in which a provider hosts applications in a server that customers use via internet. Since SaaS does not require to install applications on customers' own computers, it allows the use by multiple users of highly specialized software without extra expenses for hardware acquisition or licensing. A SaaS tailored for clinical needs not only would alleviate licensing costs, but also would facilitate easy access to new methods for diagnosis assistance. This paper presents a SaaS client-server architecture for Diagnosis as a Service (DaaS). The server is based on docker technology in order to allow execution of softwares implemented in different languages with the highest portability and scalability. The client is a content management system allowing the design of websites with multimedia content and interactive visualization of results allowing user editing. We explain a usage case that uses our DaaS as crowdsourcing platform in a multicentric pilot study carried out to evaluate the clinical benefits of a software for assessment of central airway obstruction.
Address Timisoara; Rumania; September 2018
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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ISSN ISBN Medium
Area Expedition Conference SYNASC
Notes IAM; 600.145 Approved no
Call Number Admin @ si @ SVA2018 Serial 3360
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Author Huamin Ren; Nattiya Kanhabua; Andreas Mogelmose; Weifeng Liu; Kaustubh Kulkarni; Sergio Escalera; Xavier Baro; Thomas B. Moeslund
Title (up) Back-dropout Transfer Learning for Action Recognition Type Journal Article
Year 2018 Publication IET Computer Vision Abbreviated Journal IETCV
Volume 12 Issue 4 Pages 484-491
Keywords Learning (artificial intelligence); Pattern Recognition
Abstract Transfer learning aims at adapting a model learned from source dataset to target dataset. It is a beneficial approach especially when annotating on the target dataset is expensive or infeasible. Transfer learning has demonstrated its powerful learning capabilities in various vision tasks. Despite transfer learning being a promising approach, it is still an open question how to adapt the model learned from the source dataset to the target dataset. One big challenge is to prevent the impact of category bias on classification performance. Dataset bias exists when two images from the same category, but from different datasets, are not classified as the same. To address this problem, a transfer learning algorithm has been proposed, called negative back-dropout transfer learning (NB-TL), which utilizes images that have been misclassified and further performs back-dropout strategy on them to penalize errors. Experimental results demonstrate the effectiveness of the proposed algorithm. In particular, the authors evaluate the performance of the proposed NB-TL algorithm on UCF 101 action recognition dataset, achieving 88.9% recognition rate.
Address
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Notes HUPBA; no proj Approved no
Call Number Admin @ si @ RKM2018 Serial 3071
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Author Joaquin Salas; P. Martinez; Jordi Gonzalez
Title (up) Background Updating with the Use of Intrinsic Curves Type Book Chapter
Year 2006 Publication International Conference on Image Analysis and Recognition (ICIAR´06), LNCS 4141 (A. Campilho et al., eds.), 1: 731–742, ISBN 978–3–540–44891–4 Abbreviated Journal
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Notes Approved no
Call Number ISE @ ise @ SMG2006 Serial 768
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Author Bojana Gajic; Ariel Amato; Ramon Baldrich; Carlo Gatta
Title (up) Bag of Negatives for Siamese Architectures Type Conference Article
Year 2019 Publication 30th British Machine Vision Conference Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Training a Siamese architecture for re-identification with a large number of identities is a challenging task due to the difficulty of finding relevant negative samples efficiently. In this work we present Bag of Negatives (BoN), a method for accelerated and improved training of Siamese networks that scales well on datasets with a very large number of identities. BoN is an efficient and loss-independent method, able to select a bag of high quality negatives, based on a novel online hashing strategy.
Address Cardiff; United Kingdom; September 2019
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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ISSN ISBN Medium
Area Expedition Conference BMVC
Notes CIC; 600.140; 600.118 Approved no
Call Number Admin @ si @ GAB2019b Serial 3263
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Author L. Rothacker; Marçal Rusiñol; G.A. Fink
Title (up) Bag-of-Features HMMs for segmentation-free word spotting in handwritten documents Type Conference Article
Year 2013 Publication 12th International Conference on Document Analysis and Recognition Abbreviated Journal
Volume Issue Pages 1305 - 1309
Keywords
Abstract Recent HMM-based approaches to handwritten word spotting require large amounts of learning samples and mostly rely on a prior segmentation of the document. We propose to use Bag-of-Features HMMs in a patch-based segmentation-free framework that are estimated by a single sample. Bag-of-Features HMMs use statistics of local image feature representatives. Therefore they can be considered as a variant of discrete HMMs allowing to model the observation of a number of features at a point in time. The discrete nature enables us to estimate a query model with only a single example of the query provided by the user. This makes our method very flexible with respect to the availability of training data. Furthermore, we are able to outperform state-of-the-art results on the George Washington dataset.
Address Washington; USA; August 2013
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 1520-5363 ISBN Medium
Area Expedition Conference ICDAR
Notes DAG Approved no
Call Number Admin @ si @ RRF2013 Serial 2344
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Author Anjan Dutta; Josep Llados; Umapada Pal
Title (up) Bag-of-GraphPaths Descriptors for Symbol Recognition and Spotting in Line Drawings Type Conference Article
Year 2011 Publication In proceedings of 9th IAPR Workshop on Graphic Recognition Abbreviated Journal
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Abstract Graphical symbol recognition and spotting recently have become an important research activity. In this work we present a descriptor for symbols, especially for line drawings. The descriptor is based on the graph representation of graphical objects. We construct graphs from the vectorized information of the binarized images, where the critical points detected by the vectorization algorithm are considered as nodes and the lines joining them are considered as edges. Graph paths between two nodes in a graph are the finite sequences of nodes following the order from the starting to the final node. The occurrences of different graph paths in a given graph is an important feature, as they capture the geometrical and structural attributes of a graph. So the graph representing a symbol can efficiently be represent by the occurrences of its different paths. Their occurrences in a symbol can be obtained in terms of a histogram counting the number of some fixed prototype paths, we call the histogram as the Bag-of-GraphPaths (BOGP). These BOGP histograms are used as a descriptor to measure the distance among the symbols in vector space. We use the descriptor for three applications, they are: (1) classification of the graphical symbols, (2) spotting of the architectural symbols on floorplans, (3) classification of the historical handwritten words.
Address Seoul, Korea
Corporate Author Thesis
Publisher Springer Berlin Heidelberg Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 978-3-642-36823-3 Medium
Area Expedition Conference GREC
Notes DAG Approved no
Call Number Admin @ si @ DLP2011c Serial 1825
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Author Maedeh Aghaei; Petia Radeva
Title (up) Bag-of-Tracklets for Person Tracking in Life-Logging Data Type Conference Article
Year 2014 Publication 17th International Conference of the Catalan Association for Artificial Intelligence Abbreviated Journal
Volume 269 Issue Pages 35-44
Keywords
Abstract By increasing popularity of wearable cameras, life-logging data analysis is becoming more and more important and useful to derive significant events out of this substantial collection of images. In this study, we introduce a new tracking method applied to visual life-logging, called bag-of-tracklets, which is based on detecting, localizing and tracking of people. Given the low spatial and temporal resolution of the image data, our model generates and groups tracklets in a unsupervised framework and extracts image sequences of person appearance according to a similarity score of the bag-of-tracklets. The model output is a meaningful sequence of events expressing human appearance and tracking them in life-logging data. The achieved results prove the robustness of our model in terms of efficiency and accuracy despite the low spatial and temporal resolution of the data.
Address
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Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-1-61499-451-0 Medium
Area Expedition Conference CCIA
Notes MILAB Approved no
Call Number Admin @ si @ AgR2015 Serial 2607
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Author Santiago Segui; Laura Igual; Jordi Vitria
Title (up) Bagged One Class Classifiers in the Presence of Outliers Type Journal Article
Year 2013 Publication International Journal of Pattern Recognition and Artificial Intelligence Abbreviated Journal IJPRAI
Volume 27 Issue 5 Pages 1350014-1350035
Keywords One-class Classifier; Ensemble Methods; Bagging and Outliers
Abstract The problem of training classifiers only with target data arises in many applications where non-target data are too costly, difficult to obtain, or not available at all. Several one-class classification methods have been presented to solve this problem, but most of the methods are highly sensitive to the presence of outliers in the target class. Ensemble methods have therefore been proposed as a powerful way to improve the classification performance of binary/multi-class learning algorithms by introducing diversity into classifiers.
However, their application to one-class classification has been rather limited. In
this paper, we present a new ensemble method based on a non-parametric weighted bagging strategy for one-class classification, to improve accuracy in the presence of outliers. While the standard bagging strategy assumes a uniform data distribution, the method we propose here estimates a probability density based on a forest structure of the data. This assumption allows the estimation of data distribution from the computation of simple univariate and bivariate kernel densities. Experiments using original and noisy versions of 20 different datasets show that bagging ensemble methods applied to different one-class classifiers outperform base one-class classification methods. Moreover, we show that, in noisy versions of the datasets, the non-parametric weighted bagging strategy we propose outperforms the classical bagging strategy in a statistically significant way.
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Notes OR; 600.046;MV Approved no
Call Number Admin @ si @ SIV2013 Serial 2256
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Author Enric Marti; Debora Gil; Marc Vivet ; Carme Julia
Title (up) Balance de cuatro años de experiencia en la implantación de la metodología de Aprendizaje Basado en Proyectos en la asignatura de Gráficos por Computador en ingeniería Informática Type Miscellaneous
Year 2008 Publication Actas V Jornadas Internacionales de Innovación Universitaria Abbreviated Journal
Volume Issue Pages
Keywords Aprendizaje cooperativo; aprendizaje basado en proyectos; experiencias docentes.
Abstract En este trabajo se presentan los resultados de la aplicación de la metodología del aprendizaje cooperativo a la docencia de dos asignaturas de programación en ingeniería informática. ‘Algoritmos y programación’ y ‘Lenguajes de programación’ son dos asignaturas complementarias que se organizan entorno a un proyecto común que engloba los contenidos de ambas asignaturas. En la docencia de una parte muy importante de estas asignaturas, la metodología del aprendizaje cooperativo se ha adaptado a sus características específicas. Como muestra de esta adaptación presentamos dos ejemplos de las actividades desarrolladas dentro de la docencia de estas asignaturas. Después de tres años de aplicación, el análisis a nivel cualitativo y cuantitativo de los resultados muestra que éstos son muy satisfactorios y que la aplicación del método cooperativo ha mejorado de forma considerable el rendimiento de los alumnos en ambas asignaturas.
Address
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Area Expedition Conference
Notes IAM;ADAS Approved no
Call Number IAM @ iam @ MGV2008a Serial 1598
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Author Enric Marti; Debora Gil; Marc Vivet; Carme Julia
Title (up) Balance de cuatro años de experiencia en la implantación de la metodología de Aprendizaje Basado en Proyectos en la asignatura de Gráficos por Computador en ingeniería Informática Type Miscellaneous
Year 2008 Publication VIII Jornadas de Innovación Universitaria Abbreviated Journal
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Notes IAM; ADAS Approved no
Call Number IAM @ iam @ MGV2008b Serial 1599
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Author Laura Lopez-Fuentes; Andrew Bagdanov; Joost Van de Weijer; Harald Skinnemoen
Title (up) Bandwidth Limited Object Recognition in High Resolution Imagery Type Conference Article
Year 2017 Publication IEEE Winter conference on Applications of Computer Vision Abbreviated Journal
Volume Issue Pages
Keywords
Abstract This paper proposes a novel method to optimize bandwidth usage for object detection in critical communication scenarios. We develop two operating models of active information seeking. The first model identifies promising regions in low resolution imagery and progressively requests higher resolution regions on which to perform recognition of higher semantic quality. The second model identifies promising regions in low resolution imagery while simultaneously predicting the approximate location of the object of higher semantic quality. From this general framework, we develop a car recognition system via identification of its license plate and evaluate the performance of both models on a car dataset that we introduce. Results are compared with traditional JPEG compression and demonstrate that our system saves up to one order of magnitude of bandwidth while sacrificing little in terms of recognition performance.
Address Santa Rosa; CA; USA; March 2017
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Area Expedition Conference WACV
Notes LAMP; 600.068; 600.109; 600.084; 600.106; 600.079; 600.120 Approved no
Call Number Admin @ si @ LBW2017 Serial 2973
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Author Albert Berenguel; Oriol Ramos Terrades; Josep Llados; Cristina Cañero
Title (up) Banknote counterfeit detection through background texture printing analysis Type Conference Article
Year 2016 Publication 12th IAPR Workshop on Document Analysis Systems Abbreviated Journal
Volume Issue Pages
Keywords
Abstract This paper is focused on the detection of counterfeit photocopy banknotes. The main difficulty is to work on a real industrial scenario without any constraint about the acquisition device and with a single image. The main contributions of this paper are twofold: first the adaptation and performance evaluation of existing approaches to classify the genuine and photocopy banknotes using background texture printing analysis, which have not been applied into this context before. Second, a new dataset of Euro banknotes images acquired with several cameras under different luminance conditions to evaluate these methods. Experiments on the proposed algorithms show that mixing SIFT features and sparse coding dictionaries achieves quasi perfect classification using a linear SVM with the created dataset. Approaches using dictionaries to cover all possible texture variations have demonstrated to be robust and outperform the state-of-the-art methods using the proposed benchmark.
Address Rumania; May 2016
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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ISSN ISBN Medium
Area Expedition Conference DAS
Notes DAG; 600.061; 601.269; 600.097 Approved no
Call Number Admin @ si @ BRL2016 Serial 2950
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Author Alejandro Cartas; Mariella Dimiccoli; Petia Radeva
Title (up) Batch-based activity recognition from egocentric photo-streams Type Conference Article
Year 2017 Publication 1st International workshop on Egocentric Perception, Interaction and Computing Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Activity recognition from long unstructured egocentric photo-streams has several applications in assistive technology such as health monitoring and frailty detection, just to name a few. However, one of its main technical challenges is to deal with the low frame rate of wearable photo-cameras, which causes abrupt appearance changes between consecutive frames. In consequence, important discriminatory low-level features from motion such as optical flow cannot be estimated. In this paper, we present a batch-driven approach for training a deep learning architecture that strongly rely on Long short-term units to tackle this problem. We propose two different implementations of the same approach that process a photo-stream sequence using batches of fixed size with the goal of capturing the temporal evolution of high-level features. The main difference between these implementations is that one explicitly models consecutive batches by overlapping them. Experimental results over a public dataset acquired by three users demonstrate the validity of the proposed architectures to exploit the temporal evolution of convolutional features over time without relying on event boundaries.
Address Venice; Italy; October 2017;
Corporate Author Thesis
Publisher Place of Publication Editor
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ISSN ISBN Medium
Area Expedition Conference ICCV - EPIC
Notes MILAB; no menciona Approved no
Call Number Admin @ si @ CDR2017 Serial 3023
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