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Author Katerine Diaz; Francesc J. Ferri; W. Diaz
Title Fast Approximated Discriminative Common Vectors using rank-one SVD updates Type Conference Article
Year 2013 Publication 20th International Conference On Neural Information Processing Abbreviated Journal (up)
Volume 8228 Issue III Pages 368-375
Keywords
Abstract An efficient incremental approach to the discriminative common vector (DCV) method for dimensionality reduction and classification is presented. The proposal consists of a rank-one update along with an adaptive restriction on the rank of the null space which leads to an approximate but convenient solution. The algorithm can be implemented very efficiently in terms of matrix operations and space complexity, which enables its use in large-scale dynamic application domains. Deep comparative experimentation using publicly available high dimensional image datasets has been carried out in order to properly assess the proposed algorithm against several recent incremental formulations.
K. Diaz-Chito, F.J. Ferri, W. Diaz
Address Daegu; Korea; November 2013
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 978-3-642-42050-4 Medium
Area Expedition Conference ICONIP
Notes ADAS Approved no
Call Number Admin @ si @ DFD2013 Serial 2439
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Author Katerine Diaz; Francesc J. Ferri
Title Extensiones del método de vectores comunes discriminantes Aplicadas a la clasificación de imágenes Type Book Whole
Year 2013 Publication Extensiones del método de vectores comunes discriminantes Aplicadas a la clasificación de imágenes Abbreviated Journal (up)
Volume Issue Pages
Keywords
Abstract Los métodos basados en subespacios son una herramienta muy utilizada en aplicaciones de visión por computador. Aquí se presentan y validan algunos algoritmos que hemos propuesto en este campo de investigación. El primer algoritmo está relacionado con una extensión del método de vectores comunes discriminantes con kernel, que reinterpreta el espacio nulo de la matriz de dispersión intra-clase del conjunto de entrenamiento para obtener las características discriminantes. Dentro de los métodos basados en subespacios existen diferentes tipos de entrenamiento. Uno de los más populares, pero no por ello uno de los más eficientes, es el aprendizaje por lotes. En este tipo de aprendizaje, todas las muestras del conjunto de entrenamiento tienen que estar disponibles desde el inicio. De este modo, cuando nuevas muestras se ponen a disposición del algoritmo, el sistema tiene que ser reentrenado de nuevo desde cero. Una alternativa a este tipo de entrenamiento es el aprendizaje incremental. Aquí­ se proponen diferentes algoritmos incrementales del método de vectores comunes discriminantes.
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 978-3-639-55339-0 Medium
Area Expedition Conference
Notes ADAS Approved no
Call Number Admin @ si @ DiF2013 Serial 2440
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Author Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera
Title Generic Subclass Ensemble: A Novel Approach to Ensemble Classification Type Conference Article
Year 2014 Publication 22nd International Conference on Pattern Recognition Abbreviated Journal (up)
Volume Issue Pages 1254 - 1259
Keywords
Abstract Multiple classifier systems, also known as classifier ensembles, have received great attention in recent years because of their improved classification accuracy in different applications. In this paper, we propose a new general approach to ensemble classification, named generic subclass ensemble, in which each base classifier is trained with data belonging to a subset of classes, and thus discriminates among a subset of target categories. The ensemble classifiers are then fused using a combination rule. The proposed approach differs from existing methods that manipulate the target attribute, since in our approach individual classification problems are not restricted to two-class problems. We perform a series of experiments to evaluate the efficiency of the generic subclass approach on a set of benchmark datasets. Experimental results with multilayer perceptrons show that the proposed approach presents a viable alternative to the most commonly used ensemble classification approaches.
Address Stockholm; August 2014
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 1051-4651 ISBN Medium
Area Expedition Conference ICPR
Notes HuPBA;MILAB Approved no
Call Number Admin @ si @ BGE2014b Serial 2445
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Author Mohammad Ali Bagheri; Gang Hu; Qigang Gao; Sergio Escalera
Title A Framework of Multi-Classifier Fusion for Human Action Recognition Type Conference Article
Year 2014 Publication 22nd International Conference on Pattern Recognition Abbreviated Journal (up)
Volume Issue Pages 1260 - 1265
Keywords
Abstract The performance of different action-recognition methods using skeleton joint locations have been recently studied by several computer vision researchers. However, the potential improvement in classification through classifier fusion by ensemble-based methods has remained unattended. In this work, we evaluate the performance of an ensemble of five action learning techniques, each performing the recognition task from a different perspective. The underlying rationale of the fusion approach is that different learners employ varying structures of input descriptors/features to be trained. These varying structures cannot be attached and used by a single learner. In addition, combining the outputs of several learners can reduce the risk of an unfortunate selection of a poorly performing learner. This leads to having a more robust and general-applicable framework. Also, we propose two simple, yet effective, action description techniques. In order to improve the recognition performance, a powerful combination strategy is utilized based on the Dempster-Shafer theory, which can effectively make use of diversity of base learners trained on different sources of information. The recognition results of the individual classifiers are compared with those obtained from fusing the classifiers' output, showing advanced performance of the proposed methodology.
Address Stockholm; Sweden; August 2014
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 1051-4651 ISBN Medium
Area Expedition Conference ICPR
Notes HuPBA;MILAB Approved no
Call Number Admin @ si @ BHG2014 Serial 2446
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Author Naveen Onkarappa
Title Optical Flow in Driver Assistance Systems Type Book Whole
Year 2013 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal (up)
Volume Issue Pages
Keywords
Abstract Motion perception is one of the most important attributes of the human brain. Visual motion perception consists in inferring speed and direction of elements in a scene based on visual inputs. Analogously, computer vision is assisted by motion cues in the scene. Motion detection in computer vision is useful in solving problems such as segmentation, depth from motion, structure from motion, compression, navigation and many others. These problems are common in several applications, for instance, video surveillance, robot navigation and advanced driver assistance systems (ADAS). One of the most widely used techniques for motion detection is the optical flow estimation. The work in this thesis attempts to make optical flow suitable for the requirements and conditions of driving scenarios. In this context, a novel space-variant representation called reverse log-polar representation is proposed that is shown to be better than the traditional log-polar space-variant representation for ADAS. The space-variant representations reduce the amount of data to be processed. Another major contribution in this research is related to the analysis of the influence of specific characteristics from driving scenarios on the optical flow accuracy. Characteristics such as vehicle speed and
road texture are considered in the aforementioned analysis. From this study, it is inferred that the regularization weight has to be adapted according to the required error measure and for different speeds and road textures. It is also shown that polar represented optical flow suits driving scenarios where predominant motion is translation. Due to the requirements of such a study and by the lack of needed datasets a new synthetic dataset is presented; it contains: i) sequences of different speeds and road textures in an urban scenario; ii) sequences with complex motion of an on-board camera; and iii) sequences with additional moving vehicles in the scene. The ground-truth optical flow is generated by the ray-tracing technique. Further, few applications of optical flow in ADAS are shown. Firstly, a robust RANSAC based technique to estimate horizon line is proposed. Then, an egomotion estimation is presented to compare the proposed space-variant representation with the classical one. As a final contribution, a modification in the regularization term is proposed that notably improves the results
in the ADAS applications. This adaptation is evaluated using a state of the art optical flow technique. The experiments on a public dataset (KITTI) validate the advantages of using the proposed modification.
Address Bellaterra
Corporate Author Thesis Ph.D. thesis
Publisher Ediciones Graficas Rey Place of Publication Editor Angel Sappa
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-84-940902-1-9 Medium
Area Expedition Conference
Notes ADAS Approved no
Call Number Admin @ si @ Nav2013 Serial 2447
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Author Jorge Bernal; Fernando Vilariño; F. Javier Sanchez; M. Arnold; Anarta Ghosh; Gerard Lacey
Title Experts vs Novices: Applying Eye-tracking Methodologies in Colonoscopy Video Screening for Polyp Search Type Conference Article
Year 2014 Publication 2014 Symposium on Eye Tracking Research and Applications Abbreviated Journal (up)
Volume Issue Pages 223-226
Keywords
Abstract We present in this paper a novel study aiming at identifying the differences in visual search patterns between physicians of diverse levels of expertise during the screening of colonoscopy videos. Physicians were clustered into two groups -experts and novices- according to the number of procedures performed, and fixations were captured by an eye-tracker device during the task of polyp search in different video sequences. These fixations were integrated into heat maps, one for each cluster. The obtained maps were validated over a ground truth consisting of a mask of the polyp, and the comparison between experts and novices was performed by using metrics such as reaction time, dwelling time and energy concentration ratio. Experimental results show a statistically significant difference between experts and novices, and the obtained maps show to be a useful tool for the characterisation of the behaviour of each group.
Address USA; March 2014
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 978-1-4503-2751-0 Medium
Area Expedition Conference ETRA
Notes MV; 600.047; 600.060;SIAI Approved no
Call Number Admin @ si @ BVS2014 Serial 2448
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Author Fahad Shahbaz Khan; Joost Van de Weijer; Andrew Bagdanov; Michael Felsberg
Title Scale Coding Bag-of-Words for Action Recognition Type Conference Article
Year 2014 Publication 22nd International Conference on Pattern Recognition Abbreviated Journal (up)
Volume Issue Pages 1514-1519
Keywords
Abstract Recognizing human actions in still images is a challenging problem in computer vision due to significant amount of scale, illumination and pose variation. Given the bounding box of a person both at training and test time, the task is to classify the action associated with each bounding box in an image.
Most state-of-the-art methods use the bag-of-words paradigm for action recognition. The bag-of-words framework employing a dense multi-scale grid sampling strategy is the de facto standard for feature detection. This results in a scale invariant image representation where all the features at multiple-scales are binned in a single histogram. We argue that such a scale invariant
strategy is sub-optimal since it ignores the multi-scale information
available with each bounding box of a person.
This paper investigates alternative approaches to scale coding for action recognition in still images. We encode multi-scale information explicitly in three different histograms for small, medium and large scale visual-words. Our first approach exploits multi-scale information with respect to the image size. In our second approach, we encode multi-scale information relative to the size of the bounding box of a person instance. In each approach, the multi-scale histograms are then concatenated into a single representation for action classification. We validate our approaches on the Willow dataset which contains seven action categories: interacting with computer, photography, playing music,
riding bike, riding horse, running and walking. Our results clearly suggest that the proposed scale coding approaches outperform the conventional scale invariant technique. Moreover, we show that our approach obtains promising results compared to more complex state-of-the-art methods.
Address Stockholm; August 2014
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 ICPR
Notes CIC; LAMP; 601.240; 600.074; 600.079 Approved no
Call Number Admin @ si @ KWB2014 Serial 2450
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Author Q. Xue; Laura Igual; A. Berenguel; M. Guerrieri; L. Garrido
Title Active Contour Segmentation with Affine Coordinate-Based Parametrization Type Conference Article
Year 2014 Publication 9th International Conference on Computer Vision Theory and Applications Abbreviated Journal (up)
Volume 1 Issue Pages 5-14
Keywords Active Contours; Affine Coordinates; Mean Value Coordinates
Abstract In this paper, we present a new framework for image segmentation based on parametrized active contours. The contour and the points of the image space are parametrized using a set of reduced control points that have to form a closed polygon in two dimensional problems and a closed surface in three dimensional problems. By moving the control points, the active contour evolves. We use mean value coordinates as the parametrization tool for the interface, which allows to parametrize any point of the space, inside or outside the closed polygon
or surface. Region-based energies such as the one proposed by Chan and Vese can be easily implemented in both two and three dimensional segmentation problems. We show the usefulness of our approach with several experiments.
Address Lisboa; January 2014
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 VISAPP
Notes OR;MILAB Approved no
Call Number Admin @ si @ XIB2014 Serial 2452
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Author Alejandro Gonzalez Alzate; Sebastian Ramos; David Vazquez; Antonio Lopez; Jaume Amores
Title Spatiotemporal Stacked Sequential Learning for Pedestrian Detection Type Conference Article
Year 2015 Publication Pattern Recognition and Image Analysis, Proceedings of 7th Iberian Conference , ibPRIA 2015 Abbreviated Journal (up)
Volume Issue Pages 3-12
Keywords SSL; Pedestrian Detection
Abstract Pedestrian classifiers decide which image windows contain a pedestrian. In practice, such classifiers provide a relatively high response at neighbor windows overlapping a pedestrian, while the responses around potential false positives are expected to be lower. An analogous reasoning applies for image sequences. If there is a pedestrian located within a frame, the same pedestrian is expected to appear close to the same location in neighbor frames. Therefore, such a location has chances of receiving high classification scores during several frames, while false positives are expected to be more spurious. In this paper we propose to exploit such correlations for improving the accuracy of base pedestrian classifiers. In particular, we propose to use two-stage classifiers which not only rely on the image descriptors required by the base classifiers but also on the response of such base classifiers in a given spatiotemporal neighborhood. More specifically, we train pedestrian classifiers using a stacked sequential learning (SSL) paradigm. We use a new pedestrian dataset we have acquired from a car to evaluate our proposal at different frame rates. We also test on a well known dataset: Caltech. The obtained results show that our SSL proposal boosts detection accuracy significantly with a minimal impact on the computational cost. Interestingly, SSL improves more the accuracy at the most dangerous situations, i.e. when a pedestrian is close to the camera.
Address Santiago de Compostela; España; June 2015
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 ACDC Expedition Conference IbPRIA
Notes ADAS; 600.057; 600.054; 600.076 Approved no
Call Number GRV2015; ADAS @ adas @ GRV2015 Serial 2454
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Author Jiaolong Xu; Sebastian Ramos; David Vazquez; Antonio Lopez
Title Incremental Domain Adaptation of Deformable Part-based Models Type Conference Article
Year 2014 Publication 25th British Machine Vision Conference Abbreviated Journal (up)
Volume Issue Pages
Keywords Pedestrian Detection; Part-based models; Domain Adaptation
Abstract Nowadays, classifiers play a core role in many computer vision tasks. The underlying assumption for learning classifiers is that the training set and the deployment environment (testing) follow the same probability distribution regarding the features used by the classifiers. However, in practice, there are different reasons that can break this constancy assumption. Accordingly, reusing existing classifiers by adapting them from the previous training environment (source domain) to the new testing one (target domain)
is an approach with increasing acceptance in the computer vision community. In this paper we focus on the domain adaptation of deformable part-based models (DPMs) for object detection. In particular, we focus on a relatively unexplored scenario, i.e. incremental domain adaptation for object detection assuming weak-labeling. Therefore, our algorithm is ready to improve existing source-oriented DPM-based detectors as soon as a little amount of labeled target-domain training data is available, and keeps improving as more of such data arrives in a continuous fashion. For achieving this, we follow a multiple
instance learning (MIL) paradigm that operates in an incremental per-image basis. As proof of concept, we address the challenging scenario of adapting a DPM-based pedestrian detector trained with synthetic pedestrians to operate in real-world scenarios. The obtained results show that our incremental adaptive models obtain equally good accuracy results as the batch learned models, while being more flexible for handling continuously arriving target-domain data.
Address Nottingham; uk; September 2014
Corporate Author Thesis
Publisher BMVA Press Place of Publication Editor Valstar, Michel and French, Andrew and Pridmore, Tony
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference BMVC
Notes ADAS; 600.057; 600.054; 600.076 Approved no
Call Number XRV2014c; ADAS @ adas @ xrv2014c Serial 2455
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Author Sergio Vera; Debora Gil; Miguel Angel Gonzalez Ballester
Title Anatomical parameterization for volumetric meshing of the liver Type Conference Article
Year 2014 Publication SPIE – Medical Imaging Abbreviated Journal (up)
Volume 9036 Issue Pages
Keywords Coordinate System; Anatomy Modeling; Parameterization
Abstract A coordinate system describing the interior of organs is a powerful tool for a systematic localization of injured tissue. If the same coordinate values are assigned to specific anatomical landmarks, the coordinate system allows integration of data across different medical image modalities. Harmonic mappings have been used to produce parametric coordinate systems over the surface of anatomical shapes, given their flexibility to set values
at specific locations through boundary conditions. However, most of the existing implementations in medical imaging restrict to either anatomical surfaces, or the depth coordinate with boundary conditions is given at sites
of limited geometric diversity. In this paper we present a method for anatomical volumetric parameterization that extends current harmonic parameterizations to the interior anatomy using information provided by the
volume medial surface. We have applied the methodology to define a common reference system for the liver shape and functional anatomy. This reference system sets a solid base for creating anatomical models of the patient’s liver, and allows comparing livers from several patients in a common framework of reference.
Address Amsterdam; September 2014
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 SPIE-MI
Notes IAM; 600.075 Approved no
Call Number Admin @ si @ VGG2014 Serial 2456
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Author Enric Marti; Antoni Gurgui; Debora Gil; Aura Hernandez-Sabate; Jaume Rocarias; Ferran Poveda
Title ABP on line: Seguimiento, estregas y evaluación en aprendizaje basado en proyectos Type Miscellaneous
Year 2014 Publication 8th International Congress on University Teaching and Innovation Abbreviated Journal (up)
Volume Issue Pages
Keywords
Abstract
Address Tarragona; juliol 2014
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 CIDUI
Notes IAM; ADAS; 600.076; 600.063; 600.075 Approved no
Call Number Admin @ si @ MGG2014 Serial 2457
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Author Carles Sanchez; Oriol Ramos Terrades; Patricia Marquez; Enric Marti; Jaume Rocarias; Debora Gil
Title Evaluación automática de prácticas en Moodle para el aprendizaje autónomo en Ingenierías Type Miscellaneous
Year 2014 Publication 8th International Congress on University Teaching and Innovation Abbreviated Journal (up)
Volume Issue Pages
Keywords
Abstract
Address Tarragona; juliol 2014
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 CIDUI
Notes IAM; 600.075;DAG Approved no
Call Number Admin @ si @ SRM2014 Serial 2458
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Author David Fernandez; Pau Riba; Alicia Fornes; Josep Llados
Title On the Influence of Key Point Encoding for Handwritten Word Spotting Type Conference Article
Year 2014 Publication 14th International Conference on Frontiers in Handwriting Recognition Abbreviated Journal (up)
Volume Issue Pages 476 - 481
Keywords Local descriptors; Interest points; Handwritten documents; Word spotting; Historical document analysis
Abstract In this paper we evaluate the influence of the selection of key points and the associated features in the performance of word spotting processes. In general, features can be extracted from a number of characteristic points like corners, contours, skeletons, maxima, minima, crossings, etc. A number of descriptors exist in the literature using different interest point detectors. But the intrinsic variability of handwriting vary strongly on the performance if the interest points are not stable enough. In this paper, we analyze the performance of different descriptors for local interest points. As benchmarking dataset we have used the Barcelona Marriage Database that contains handwritten records of marriages over five centuries.
Address Creete Island; Grecia; September 2014
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 2167-6445 ISBN 978-1-4799-4335-7 Medium
Area Expedition Conference ICFHR
Notes DAG; 600.056; 600.061; 602.006; 600.077 Approved no
Call Number Admin @ si @ FRF2014 Serial 2460
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Author David Fernandez; Jon Almazan; Nuria Cirera; Alicia Fornes; Josep Llados
Title BH2M: the Barcelona Historical Handwritten Marriages database Type Conference Article
Year 2014 Publication 22nd International Conference on Pattern Recognition Abbreviated Journal (up)
Volume Issue Pages 256 - 261
Keywords
Abstract This paper presents an image database of historical handwritten marriages records stored in the archives of Barcelona cathedral, and the corresponding meta-data addressed to evaluate the performance of document analysis algorithms. The contribution of this paper is twofold. First, it presents a complete ground truth which covers the whole pipeline of handwriting
recognition research, from layout analysis to recognition and understanding. Second, it is the first dataset in the emerging area of genealogical document analysis, where documents are manuscripts pseudo-structured with specific lexicons and the interest is beyond pure transcriptions but context dependent.
Address Creete Island; Grecia; September 2014
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 1051-4651 ISBN Medium
Area Expedition Conference ICPR
Notes DAG; 600.056; 600.061; 602.006; 600.077 Approved no
Call Number Admin @ si @ FAC2014 Serial 2461
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