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Author Aura Hernandez-Sabate; Meritxell Joanpere; Nuria Gorgorio; Lluis Albarracin
Title Mathematics learning opportunities when playing a Tower Defense Game Type Journal
Year 2015 Publication International Journal of Serious Games Abbreviated Journal IJSG
Volume (up) 2 Issue 4 Pages 57-71
Keywords Tower Defense game; learning opportunities; mathematics; problem solving; game design
Abstract A qualitative research study is presented herein with the purpose of identifying mathematics learning opportunities in students between 10 and 12 years old while playing a commercial version of a Tower Defense game. These learning opportunities are understood as mathematicisable moments of the game and involve the establishment of relationships between the game and mathematical problem solving. Based on the analysis of these mathematicisable moments, we conclude that the game can promote problem-solving processes and learning opportunities that can be associated with different mathematical contents that appears in mathematics curricula, thought it seems that teacher or new game elements might be needed to facilitate the processes.
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Notes ADAS; 600.076 Approved no
Call Number Admin @ si @ HJG2015 Serial 2730
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Author Frederic Sampedro; Sergio Escalera; Anna Domenech; Ignasi Carrio
Title Automatic Tumor Volume Segmentation in Whole-Body PET/CT Scans: A Supervised Learning Approach Source Type Journal Article
Year 2015 Publication Journal of Medical Imaging and Health Informatics Abbreviated Journal JMIHI
Volume (up) 5 Issue 2 Pages 192-201
Keywords CONTEXTUAL CLASSIFICATION; PET/CT; SUPERVISED LEARNING; TUMOR SEGMENTATION; WHOLE BODY
Abstract Whole-body 3D PET/CT tumoral volume segmentation provides relevant diagnostic and prognostic information in clinical oncology and nuclear medicine. Carrying out this procedure manually by a medical expert is time consuming and suffers from inter- and intra-observer variabilities. In this paper, a completely automatic approach to this task is presented. First, the problem is stated and described both in clinical and technological terms. Then, a novel supervised learning segmentation framework is introduced. The segmentation by learning approach is defined within a Cascade of Adaboost classifiers and a 3D contextual proposal of Multiscale Stacked Sequential Learning. Segmentation accuracy results on 200 Breast Cancer whole body PET/CT volumes show mean 49% sensitivity, 99.993% specificity and 39% Jaccard overlap Index, which represent good performance results both at the clinical and technological level.
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Notes HuPBA;MILAB Approved no
Call Number Admin @ si @ SED2015 Serial 2584
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Author Enric Marti; J.Roncaries; Debora Gil; Aura Hernandez-Sabate; Antoni Gurgui; Ferran Poveda
Title PBL On Line: A proposal for the organization, part-time monitoring and assessment of PBL group activities Type Journal
Year 2015 Publication Journal of Technology and Science Education Abbreviated Journal JOTSE
Volume (up) 5 Issue 2 Pages 87-96
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Abstract
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Notes IAM; ADAS; 600.076; 600.075 Approved no
Call Number Admin @ si @ MRG2015 Serial 2608
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Author Carles Sanchez; Oriol Ramos Terrades; Patricia Marquez; Enric Marti; J.Roncaries; Debora Gil
Title Automatic evaluation of practices in Moodle for Self Learning in Engineering Type Journal
Year 2015 Publication Journal of Technology and Science Education Abbreviated Journal JOTSE
Volume (up) 5 Issue 2 Pages 97-106
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Abstract
Address
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Publisher Place of Publication Editor
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Notes IAM; DAG; 600.075; 600.077 Approved no
Call Number Admin @ si @ SRM2015 Serial 2610
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Author Frederic Sampedro; Sergio Escalera
Title Spatial codification of label predictions in Multi-scale Stacked Sequential Learning: A case study on multi-class medical volume segmentation Type Journal Article
Year 2015 Publication IET Computer Vision Abbreviated Journal IETCV
Volume (up) 9 Issue 3 Pages 439 - 446
Keywords
Abstract In this study, the authors propose the spatial codification of label predictions within the multi-scale stacked sequential learning (MSSL) framework, a successful learning scheme to deal with non-independent identically distributed data entries. After providing a motivation for this objective, they describe its theoretical framework based on the introduction of the blurred shape model as a smart descriptor to codify the spatial distribution of the predicted labels and define the new extended feature set for the second stacked classifier. They then particularise this scheme to be applied in volume segmentation applications. Finally, they test the implementation of the proposed framework in two medical volume segmentation datasets, obtaining significant performance improvements (with a 95% of confidence) in comparison to standard Adaboost classifier and classical MSSL approaches.
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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 1751-9632 ISBN Medium
Area Expedition Conference
Notes HuPBA;MILAB Approved no
Call Number Admin @ si @ SaE2015 Serial 2551
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Author Carles Sanchez; Jorge Bernal; F. Javier Sanchez; Antoni Rosell; Marta Diez-Ferrer; Debora Gil
Title Towards On-line Quantification of Tracheal Stenosis from Videobronchoscopy Type Journal Article
Year 2015 Publication International Journal of Computer Assisted Radiology and Surgery Abbreviated Journal IJCAR
Volume (up) 10 Issue 6 Pages 935-945
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Abstract
Address
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Notes IAM; MV; 600.075 Approved no
Call Number Admin @ si @ SBS2015a Serial 2611
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Author Carles Sanchez; Jorge Bernal; F. Javier Sanchez; Marta Diez-Ferrer; Antoni Rosell; Debora Gil
Title Towards On-line Quantification of Tracheal Stenosis from Videobronchoscopy Type Conference Article
Year 2015 Publication 6th International Conference on Information Processing in Computer-Assisted Interventions IPCAI2015 Abbreviated Journal
Volume (up) 10 Issue 6 Pages 935-945
Keywords
Abstract PURPOSE:
Lack of objective measurement of tracheal obstruction degree has a negative impact on the chosen treatment prone to lead to unnecessary repeated explorations and other scanners. Accurate computation of tracheal stenosis in videobronchoscopy would constitute a breakthrough for this noninvasive technique and a reduction in operation cost for the public health service.
METHODS:
Stenosis calculation is based on the comparison of the region delimited by the lumen in an obstructed frame and the region delimited by the first visible ring in a healthy frame. We propose a parametric strategy for the extraction of lumen and tracheal ring regions based on models of their geometry and appearance that guide a deformable model. To ensure a systematic applicability, we present a statistical framework to choose optimal parametric values and a strategy to choose the frames that minimize the impact of scope optical distortion.
RESULTS:
Our method has been tested in 40 cases covering different stenosed tracheas. Experiments report a non- clinically relevant [Formula: see text] of discrepancy in the calculated stenotic area and a computational time allowing online implementation in the operating room.
CONCLUSIONS:
Our methodology allows reliable measurements of airway narrowing in the operating room. To fully assess its clinical impact, a prospective clinical trial should be done.
Address Barcelona; Spain; June 2015
Corporate Author Thesis
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ISSN ISBN Medium
Area Expedition Conference IPCAI
Notes IAM; MV; 600.075 Approved no
Call Number Admin @ si @ SBS2015b Serial 2613
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Author Sergio Vera; Miguel Angel Gonzalez Ballester; Debora Gil
Title A Novel Cochlear Reference Frame Based On The Laplace Equation Type Conference Article
Year 2015 Publication 29th international Congress and Exhibition on Computer Assisted Radiology and Surgery Abbreviated Journal
Volume (up) 10 Issue 1 Pages 1-312
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Abstract Poster
Address Barcelona; Spain; June 2015
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ISSN ISBN Medium
Area Expedition Conference CARS
Notes IAM; 600.075 Approved no
Call Number Admin @ si @ VGG2015 Serial 2615
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Author Xavier Otazu; Olivier Penacchio; Xim Cerda-Company
Title Brightness and colour induction through contextual influences in V1 Type Conference Article
Year 2015 Publication Scottish Vision Group 2015 SGV2015 Abbreviated Journal
Volume (up) 12 Issue 9 Pages 1208-2012
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Abstract
Address Carnoustie; Scotland; March 2015
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Area Expedition Conference SGV
Notes NEUROBIT; Approved no
Call Number Admin @ si @ OPC2015a Serial 2632
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Author Miguel Oliveira; Angel Sappa; Victor Santos
Title A probabilistic approach for color correction in image mosaicking applications Type Journal Article
Year 2015 Publication IEEE Transactions on Image Processing Abbreviated Journal TIP
Volume (up) 14 Issue 2 Pages 508 - 523
Keywords Color correction; image mosaicking; color transfer; color palette mapping functions
Abstract Image mosaicking applications require both geometrical and photometrical registrations between the images that compose the mosaic. This paper proposes a probabilistic color correction algorithm for correcting the photometrical disparities. First, the image to be color corrected is segmented into several regions using mean shift. Then, connected regions are extracted using a region fusion algorithm. Local joint image histograms of each region are modeled as collections of truncated Gaussians using a maximum likelihood estimation procedure. Then, local color palette mapping functions are computed using these sets of Gaussians. The color correction is performed by applying those functions to all the regions of the image. An extensive comparison with ten other state of the art color correction algorithms is presented, using two different image pair data sets. Results show that the proposed approach obtains the best average scores in both data sets and evaluation metrics and is also the most robust to failures.
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Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1057-7149 ISBN Medium
Area Expedition Conference
Notes ADAS; 600.076 Approved no
Call Number Admin @ si @ OSS2015b Serial 2554
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Author T. Mouats; N. Aouf; Angel Sappa; Cristhian A. Aguilera-Carrasco; Ricardo Toledo
Title Multi-Spectral Stereo Odometry Type Journal Article
Year 2015 Publication IEEE Transactions on Intelligent Transportation Systems Abbreviated Journal TITS
Volume (up) 16 Issue 3 Pages 1210-1224
Keywords Egomotion estimation; feature matching; multispectral odometry (MO); optical flow; stereo odometry; thermal imagery
Abstract In this paper, we investigate the problem of visual odometry for ground vehicles based on the simultaneous utilization of multispectral cameras. It encompasses a stereo rig composed of an optical (visible) and thermal sensors. The novelty resides in the localization of the cameras as a stereo setup rather
than two monocular cameras of different spectrums. To the best of our knowledge, this is the first time such task is attempted. Log-Gabor wavelets at different orientations and scales are used to extract interest points from both images. These are then described using a combination of frequency and spatial information within the local neighborhood. Matches between the pairs of multimodal images are computed using the cosine similarity function based
on the descriptors. Pyramidal Lucas–Kanade tracker is also introduced to tackle temporal feature matching within challenging sequences of the data sets. The vehicle egomotion is computed from the triangulated 3-D points corresponding to the matched features. A windowed version of bundle adjustment incorporating
Gauss–Newton optimization is utilized for motion estimation. An outlier removal scheme is also included within the framework to deal with outliers. Multispectral data sets were generated and used as test bed. They correspond to real outdoor scenarios captured using our multimodal setup. Finally, detailed results validating the proposed strategy are illustrated.
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Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1524-9050 ISBN Medium
Area Expedition Conference
Notes ADAS; 600.055; 600.076 Approved no
Call Number Admin @ si @ MAS2015a Serial 2533
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Author Eloi Puertas; Sergio Escalera; Oriol Pujol
Title Generalized Multi-scale Stacked Sequential Learning for Multi-class Classification Type Journal Article
Year 2015 Publication Pattern Analysis and Applications Abbreviated Journal PAA
Volume (up) 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
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1433-7541 ISBN Medium
Area Expedition Conference
Notes HuPBA;MILAB Approved no
Call Number Admin @ si @ PEP2013 Serial 2251
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Author Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera
Title Combining Local and Global Learners in the Pairwise Multiclass Classification Type Journal Article
Year 2015 Publication Pattern Analysis and Applications Abbreviated Journal PAA
Volume (up) 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
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1433-7541 ISBN Medium
Area Expedition Conference
Notes HuPBA;MILAB Approved no
Call Number Admin @ si @ BGE2014 Serial 2441
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Author Lluis Pere de las Heras; Oriol Ramos Terrades; Sergi Robles; Gemma Sanchez
Title 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 (up) 18 Issue 1 Pages 15-30
Keywords
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
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; ADAS; 600.061; 600.076; 600.077 Approved no
Call Number Admin @ si @ HRR2015 Serial 2567
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Author Christophe Rigaud; Clement Guerin; Dimosthenis Karatzas; Jean-Christophe Burie; Jean-Marc Ogier
Title Knowledge-driven understanding of images in comic books Type Journal Article
Year 2015 Publication International Journal on Document Analysis and Recognition Abbreviated Journal IJDAR
Volume (up) 18 Issue 3 Pages 199-221
Keywords Document Understanding; comics analysis; expert system
Abstract Document analysis is an active field of research, which can attain a complete understanding of the semantics of a given document. One example of the document understanding process is enabling a computer to identify the key elements of a comic book story and arrange them according to a predefined domain knowledge. In this study, we propose a knowledge-driven system that can interact with bottom-up and top-down information to progressively understand the content of a document. We model the comic book’s and the image processing domains knowledge for information consistency analysis. In addition, different image processing methods are improved or developed to extract panels, balloons, tails, texts, comic characters and their semantic relations in an unsupervised way.
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; 600.056; 600.077 Approved no
Call Number RGK2015 Serial 2595
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