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Author | R. Clariso; David Masip; A. Rius | ||||
Title | Student projects empowering mobile learning in higher education | Type | Journal | ||
Year | 2014 | Publication | Revista de Universidad y Sociedad del Conocimiento | Abbreviated Journal | RUSC |
Volume | 11 | Issue | Pages | 192-207 | |
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ISSN | 1698-580X | ISBN | Medium | ||
Area | Expedition | Conference | |||
Notes | OR;MV | Approved | no | ||
Call Number | Admin @ si @ CMR2014 | Serial | 2619 | ||
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Author | Joan Arnedo-Moreno; D. Bañeres; Xavier Baro; S. Caballe; S. Guerrero; L. Porta; J. Prieto | ||||
Title | Va-ID: A trust-based virtual assessment system | Type | Conference Article | ||
Year | 2014 | Publication | 6th International Conference on Intelligent Networking and Collaborative Systems | Abbreviated Journal | |
Volume | Issue | Pages | 328 - 335 | ||
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Abstract | Even though online education is a very important pillar of lifelong education, institutions are still reluctant to wager for a fully online educational model. At the end, they keep relying on on-site assessment systems, mainly because fully virtual alternatives do not have the deserved social recognition or credibility. Thus, the design of virtual assessment systems that are able to provide effective proof of student authenticity and authorship and the integrity of the activities in a scalable and cost efficient manner would be very helpful. This paper presents ValID, a virtual assessment approach based on a continuous trust level evaluation between students and the institution. The current trust level serves as the main mechanism to dynamically decide which kind of controls a given student should be subjected to, across different courses in a degree. The main goal is providing a fair trade-off between security, scalability and cost, while maintaining the perceived quality of the educational model. | ||||
Address | Salerna; Italy; September 2014 | ||||
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ISSN | ISBN | 978-1-4799-6386-7 | Medium | ||
Area | Expedition | Conference | INCOS | ||
Notes | OR; HuPBA;MV | Approved | no | ||
Call Number | Admin @ si @ ABB2014 | Serial | 2620 | ||
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Author | B. Zhou; Agata Lapedriza; J. Xiao; A. Torralba; A. Oliva | ||||
Title | Learning Deep Features for Scene Recognition using Places Database | Type | Conference Article | ||
Year | 2014 | Publication | 28th Annual Conference on Neural Information Processing Systems | Abbreviated Journal | |
Volume | Issue | Pages | 487-495 | ||
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Address | Montreal; Canada; December 2014 | ||||
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Area | Expedition | Conference | NIPS | ||
Notes | OR;MV | Approved | no | ||
Call Number | Admin @ si @ ZLX2014 | Serial | 2621 | ||
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Author | Agata Lapedriza; David Masip; David Sanchez | ||||
Title | Emotions Classification using Facial Action Units Recognition | Type | Conference Article | ||
Year | 2014 | Publication | 17th International Conference of the Catalan Association for Artificial Intelligence | Abbreviated Journal | |
Volume | 269 | Issue | Pages | 55-64 | |
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Abstract | In this work we build a system for automatic emotion classification from image sequences. We analyze subtle changes in facial expressions by detecting a subset of 12 representative facial action units (AUs). Then, we classify emotions based on the output of these AUs classifiers, i.e. the presence/absence of AUs. We base the AUs classification upon a set of spatio-temporal geometric and appearance features for facial representation, fusing them within the emotion classifier. A decision tree is trained for emotion classifying, making the resulting model easy to interpret by capturing the combination of AUs activation that lead to a particular emotion. For Cohn-Kanade database, the proposed system classifies 7 emotions with a mean accuracy of near 90%, attaining a similar recognition accuracy in comparison with non-interpretable models that are not based in AUs detection. | ||||
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ISSN | ISBN | 978-1-61499-451-0 | Medium | ||
Area | Expedition | Conference | CCIA | ||
Notes | OR;MV | Approved | no | ||
Call Number | Admin @ si @ LMS2014 | Serial | 2622 | ||
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Author | Bogdan Raducanu; Alireza Bosaghzadeh; Fadi Dornaika | ||||
Title | Multi-observation Face Recognition in Videos based on Label Propagation | Type | Conference Article | ||
Year | 2015 | Publication | 6th Workshop on Analysis and Modeling of Faces and Gestures AMFG2015 | Abbreviated Journal | |
Volume | Issue | Pages | 10-17 | ||
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Abstract | In order to deal with the huge amount of content generated by social media, especially for indexing and retrieval purposes, the focus shifted from single object recognition to multi-observation object recognition. Of particular interest is the problem of face recognition (used as primary cue for persons’ identity assessment), since it is highly required by popular social media search engines like Facebook and Youtube. Recently, several approaches for graph-based label propagation were proposed. However, the associated graphs were constructed in an ad-hoc manner (e.g., using the KNN graph) that cannot cope properly with the rapid and frequent changes in data appearance, a phenomenon intrinsically related with video sequences. In this paper, we
propose a novel approach for efficient and adaptive graph construction, based on a two-phase scheme: (i) the first phase is used to adaptively find the neighbors of a sample and also to find the adequate weights for the minimization function of the second phase; (ii) in the second phase, the selected neighbors along with their corresponding weights are used to locally and collaboratively estimate the sparse affinity matrix weights. Experimental results performed on Honda Video Database (HVDB) and a subset of video sequences extracted from the popular TV-series ’Friends’ show a distinct advantage of the proposed method over the existing standard graph construction methods. |
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Address | Boston; USA; June 2015 | ||||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | CVPRW | ||
Notes | LAMP; 600.068; 600.072; | Approved | no | ||
Call Number | Admin @ si @ RBD2015 | Serial | 2627 | ||
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Author | Marco Pedersoli; Andrea Vedaldi; Jordi Gonzalez; Xavier Roca | ||||
Title | A coarse-to-fine approach for fast deformable object detection | Type | Journal Article | ||
Year | 2015 | Publication | Pattern Recognition | Abbreviated Journal | PR |
Volume | 48 | Issue | 5 | Pages | 1844-1853 |
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Abstract | We present a method that can dramatically accelerate object detection with part based models. The method is based on the observation that the cost of detection is likely to be dominated by the cost of matching each part to the image, and not by the cost of computing the optimal configuration of the parts as commonly assumed. Therefore accelerating detection requires minimizing the number of
part-to-image comparisons. To this end we propose a multiple-resolutions hierarchical part based model and a corresponding coarse-to-fine inference procedure that recursively eliminates from the search space unpromising part placements. The method yields a ten-fold speedup over the standard dynamic programming approach and is complementary to the cascade-of-parts approach of [9]. Compared to the latter, our method does not have parameters to be determined empirically, which simplifies its use during the training of the model. Most importantly, the two techniques can be combined to obtain a very significant speedup, of two orders of magnitude in some cases. We evaluate our method extensively on the PASCAL VOC and INRIA datasets, demonstrating a very high increase in the detection speed with little degradation of the accuracy. |
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Area | Expedition | Conference | |||
Notes | ISE; 600.078; 602.005; 605.001; 302.012 | Approved | no | ||
Call Number | Admin @ si @ PVG2015 | Serial | 2628 | ||
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Author | Cristhian A. Aguilera-Carrasco; Angel Sappa; Ricardo Toledo | ||||
Title | LGHD: a Feature Descriptor for Matching Across Non-Linear Intensity Variations | Type | Conference Article | ||
Year | 2015 | Publication | 22th IEEE International Conference on Image Processing | Abbreviated Journal | |
Volume | Issue | Pages | 178 - 181 | ||
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Address | Quebec; Canada; September 2015 | ||||
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Area | Expedition | Conference | ICIP | ||
Notes | ADAS; 600.076 | Approved | no | ||
Call Number | Admin @ si @ AST2015 | Serial | 2630 | ||
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Author | Jiaolong Xu | ||||
Title | Domain Adaptation of Deformable Part-based Models | Type | Book Whole | ||
Year | 2015 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
Volume | Issue | Pages | |||
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Abstract | On-board pedestrian detection is crucial for Advanced Driver Assistance Systems
(ADAS). An accurate classication is fundamental for vision-based pedestrian detection. The underlying assumption for learning classiers is that the training set and the deployment environment (testing) follow the same probability distribution regarding the features used by the classiers. However, in practice, there are dierent reasons that can break this constancy assumption. Accordingly, reusing existing classiers 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 thesis we focus on the domain adaptation of deformable part-based models (DPMs) for pedestrian detection. As a prof of concept, we use a computer graphic based synthetic dataset, i.e. a virtual world, as the source domain, and adapt the virtual-world trained DPM detector to various real-world dataset. We start by exploiting the maximum detection accuracy of the virtual-world trained DPM. Even though, when operating in various real-world datasets, the virtualworld trained detector still suer from accuracy degradation due to the domain gap of virtual and real worlds. We then focus on domain adaptation of DPM. At the rst step, we consider single source and single target domain adaptation and propose two batch learning methods, namely A-SSVM and SA-SSVM. Later, we further consider leveraging multiple target (sub-)domains for progressive domain adaptation and propose a hierarchical adaptive structured SVM (HA-SSVM) for optimization. Finally, we extend HA-SSVM for the challenging online domain adaptation problem, aiming at making the detector to automatically adapt to the target domain online, without any human intervention. All of the proposed methods in this thesis do not require revisiting source domain data. The evaluations are done on the Caltech pedestrian detection benchmark. Results show that SA-SSVM slightly outperforms A-SSVM and avoids accuracy drops as high as 15 points when comparing with a non-adapted detector. The hierarchical model learned by HA-SSVM further boosts the domain adaptation performance. Finally, the online domain adaptation method has demonstrated that it can achieve comparable accuracy to the batch learned models while not requiring manually label target domain examples. Domain adaptation for pedestrian detection is of paramount importance and a relatively unexplored area. We humbly hope the work in this thesis could provide foundations for future work in this area. |
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Address | April 2015 | ||||
Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | Place of Publication | Editor | Antonio Lopez | ||
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ISSN | ISBN | 978-84-943427-1-4 | Medium | ||
Area | Expedition | Conference | |||
Notes | ADAS; 600.076 | Approved | no | ||
Call Number | Admin @ si @ Xu2015 | Serial | 2631 | ||
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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 | 12 | Issue | 9 | Pages | 1208-2012 |
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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 | Olivier Penacchio; Xavier Otazu; A. wilkins; J. Harris | ||||
Title | Uncomfortable images prevent lateral interactions in the cortex from providing a sparse code | Type | Conference Article | ||
Year | 2015 | Publication | European Conference on Visual Perception ECVP2015 | Abbreviated Journal | |
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Address | Liverpool; uk; August 2015 | ||||
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Area | Expedition | Conference | ECVP | ||
Notes | NEUROBIT; | Approved | no | ||
Call Number | Admin @ si @ POW2015 | Serial | 2633 | ||
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Author | Xavier Otazu; Olivier Penacchio; Xim Cerda-Company | ||||
Title | An excitatory-inhibitory firing rate model accounts for brightness induction, colour induction and visual discomfort | Type | Conference Article | ||
Year | 2015 | Publication | Barcelona Computational, Cognitive and Systems Neuroscience | Abbreviated Journal | |
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Address | Barcelona; June 2015 | ||||
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Area | Expedition | Conference | BARCCSYN | ||
Notes | NEUROBIT; | Approved | no | ||
Call Number | Admin @ si @ OPC2015b | Serial | 2634 | ||
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Author | Santiago Segui; Oriol Pujol; Jordi Vitria | ||||
Title | Learning to count with deep object features | Type | Conference Article | ||
Year | 2015 | Publication | Deep Vision: Deep Learning in Computer Vision, CVPR 2015 Workshop | Abbreviated Journal | |
Volume | Issue | Pages | 90-96 | ||
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Abstract | Learning to count is a learning strategy that has been recently proposed in the literature for dealing with problems where estimating the number of object instances in a scene is the final objective. In this framework, the task of learning to detect and localize individual object instances is seen as a harder task that can be evaded by casting the problem as that of computing a regression value from hand-crafted image features. In this paper we explore the features that are learned when training a counting convolutional neural
network in order to understand their underlying representation. To this end we define a counting problem for MNIST data and show that the internal representation of the network is able to classify digits in spite of the fact that no direct supervision was provided for them during training. We also present preliminary results about a deep network that is able to count the number of pedestrians in a scene. |
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Address | Boston; USA; June 2015 | ||||
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Area | Expedition | Conference | CVPRW | ||
Notes | MILAB; HuPBA; OR;MV | Approved | no | ||
Call Number | Admin @ si @ SPV2015 | Serial | 2636 | ||
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Author | Marc Bolaños; Maite Garolera; Petia Radeva | ||||
Title | Active labeling application applied to food-related object recognition | Type | Conference Article | ||
Year | 2013 | Publication | 5th International Workshop on Multimedia for Cooking & Eating Activities | Abbreviated Journal | |
Volume | Issue | Pages | 45-50 | ||
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Abstract | Every day, lifelogging devices, available for recording different aspects of our daily life, increase in number, quality and functions, just like the multiple applications that we give to them. Applying wearable devices to analyse the nutritional habits of people is a challenging application based on acquiring and analyzing life records in long periods of time. However, to extract the information of interest related to the eating patterns of people, we need automatic methods to process large amount of life-logging data (e.g. recognition of food-related objects). Creating a rich set of manually labeled samples to train the algorithms is slow, tedious and subjective. To address this problem, we propose a novel method in the framework of Active Labeling for construct- ing a training set of thousands of images. Inspired by the hierarchical sampling method for active learning [6], we propose an Active forest that organizes hierarchically the data for easy and fast labeling. Moreover, introducing a classifier into the hierarchical structures, as well as transforming the feature space for better data clustering, additionally im- prove the algorithm. Our method is successfully tested to label 89.700 food-related objects and achieves significant reduction in expert time labelling.
Active labeling application applied to food-related object recognition ResearchGate. Available from: http://www.researchgate.net/publication/262252017Activelabelingapplicationappliedtofood-relatedobjectrecognition [accessed Jul 14, 2015]. |
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Address | Barcelona; October 2013 | ||||
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Area | Expedition | Conference | ACM-CEA | ||
Notes | MILAB | Approved | no | ||
Call Number | Admin @ si @ BGR2013b | Serial | 2637 | ||
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Author | Nuria Cirera; Alicia Fornes; Josep Llados | ||||
Title | Hidden Markov model topology optimization for handwriting recognition | Type | Conference Article | ||
Year | 2015 | Publication | 13th International Conference on Document Analysis and Recognition ICDAR2015 | Abbreviated Journal | |
Volume | Issue | Pages | 626-630 | ||
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Abstract | In this paper we present a method to optimize the topology of linear left-to-right hidden Markov models. These models are very popular for sequential signals modeling on tasks such as handwriting recognition. Many topology definition methods select the number of states for a character model based
on character length. This can be a drawback when characters are shorter than the minimum allowed by the model, since they can not be properly trained nor recognized. The proposed method optimizes the number of states per model by automatically including convenient skip-state transitions and therefore it avoids the aforementioned problem.We discuss and compare our method with other character length-based methods such the Fixed, Bakis and Quantile methods. Our proposal performs well on off-line handwriting recognition task. |
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Address | Nancy; France; August 2015 | ||||
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Area | Expedition | Conference | ICDAR | ||
Notes | DAG; 600.061; 602.006; 600.077 | Approved | no | ||
Call Number | Admin @ si @ CFL2015 | Serial | 2639 | ||
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Author | Tadashi Araki; Nobutaka Ikeda; Nilanjan Dey; Sayan Chakraborty; Luca Saba; Dinesh Kumar; Elisa Cuadrado Godia; Xiaoyi Jiang; Ajay Gupta; Petia Radeva; John R. Laird; Andrew Nicolaides; Jasjit S. Suri | ||||
Title | A comparative approach of four different image registration techniques for quantitative assessment of coronary artery calcium lesions using intravascular ultrasound | Type | Journal Article | ||
Year | 2015 | Publication | Computer Methods and Programs in Biomedicine | Abbreviated Journal | CMPB |
Volume | 118 | Issue | 2 | Pages | 158-172 |
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Notes | MILAB | Approved | no | ||
Call Number | Admin @ si @ AID2015 | Serial | 2640 | ||
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