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Author Firat Ismailoglu; Ida G. Sprinkhuizen-Kuyper; Evgueni Smirnov; Sergio Escalera; Ralf Peeters
Title Fractional Programming Weighted Decoding for Error-Correcting Output Codes Type Conference Article
Year 2015 Publication Multiple Classifier Systems, Proceedings of 12th International Workshop , MCS 2015 Abbreviated Journal
Volume Issue Pages 38-50
Keywords
Abstract In order to increase the classification performance obtained using Error-Correcting Output Codes designs (ECOC), introducing weights in the decoding phase of the ECOC has attracted a lot of interest. In this work, we present a method for ECOC designs that focuses on increasing hypothesis margin on the data samples given a base classifier. While achieving this, we implicitly reward the base classifiers with high performance, whereas punish those with low performance. The resulting objective function is of the fractional programming type and we deal with this problem through the Dinkelbach’s Algorithm. The conducted tests over well known UCI datasets show that the presented method is superior to the unweighted decoding and that it outperforms the results of the state-of-the-art weighted decoding methods in most of the performed experiments.
Address Gunzburg; Germany; June 2015
Corporate Author Thesis
Publisher Springer International Publishing Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-3-319-20247-1 Medium
Area Expedition Conference MCS
Notes HuPBA;MILAB Approved no
Call Number Admin @ si @ ISS2015 Serial 2601
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Author Mariella Dimiccoli; Petia Radeva
Title Lifelogging in the era of outstanding digitization Type Conference Article
Year 2015 Publication International Conference on Digital Presentation and Preservation of Cultural and Scientific Heritage Abbreviated Journal
Volume Issue Pages
Keywords
Abstract In this paper, we give an overview on the emerging trend of the digitized self, focusing on visual lifelogging through wearable cameras. This is about continuously recording our life from a first-person view by wearing a camera that passively captures images. On one hand, visual lifelogging has opened the door to a large number of applications, including health. On the other, it has also boosted new challenges in the field of data analysis as well as new ethical concerns. While currently increasing efforts are being devoted to exploit lifelogging data for the improvement of personal well-being, we believe there are still many interesting applications to explore, ranging from tourism to the digitization of human behavior.
Address Verliko Tarmovo; Bulgaria; September 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 Expedition Conference DiPP
Notes MILAB Approved no
Call Number Admin @ si @DiR2016 Serial 2792
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Author J.Poujol; Cristhian A. Aguilera-Carrasco; E.Danos; Boris X. Vintimilla; Ricardo Toledo; Angel Sappa
Title Visible-Thermal Fusion based Monocular Visual Odometry Type Conference Article
Year 2015 Publication 2nd Iberian Robotics Conference ROBOT2015 Abbreviated Journal
Volume 417 Issue Pages 517-528
Keywords Monocular Visual Odometry; LWIR-RGB cross-spectral Imaging; Image Fusion.
Abstract The manuscript evaluates the performance of a monocular visual odometry approach when images from different spectra are considered, both independently and fused. The objective behind this evaluation is to analyze if classical approaches can be improved when the given images, which are from different spectra, are fused and represented in new domains. The images in these new domains should have some of the following properties: i) more robust to noisy data; ii) less sensitive to changes (e.g., lighting); iii) more rich in descriptive information, among other. In particular in the current work two different image fusion strategies are considered. Firstly, images from the visible and thermal spectrum are fused using a Discrete Wavelet Transform (DWT) approach. Secondly, a monochrome threshold strategy is considered. The obtained
representations are evaluated under a visual odometry framework, highlighting
their advantages and disadvantages, using different urban and semi-urban scenarios. Comparisons with both monocular-visible spectrum and monocular-infrared spectrum, are also provided showing the validity of the proposed approach.
Address Lisboa; Portugal; November 2015
Corporate Author Thesis
Publisher Springer International Publishing Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2194-5357 ISBN 978-3-319-27145-3 Medium
Area Expedition Conference ROBOT
Notes ADAS; 600.076; 600.086 Approved no
Call Number Admin @ si @ PAD2015 Serial 2663
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Author Youssef El Rhabi; Simon Loic; Brun Luc
Title Estimation de la pose d’une caméra à partir d’un flux vidéo en s’approchant du temps réel Type Conference Article
Year 2015 Publication 15ème édition d'ORASIS, journées francophones des jeunes chercheurs en vision par ordinateur ORASIS2015 Abbreviated Journal
Volume Issue Pages
Keywords Augmented Reality; SFM; SLAM; real time pose computation; 2D/3D registration
Abstract Finding a way to estimate quickly and robustly the pose of an image is essential in augmented reality. Here we will discuss the approach we chose in order to get closer to real time by using SIFT points [4]. We propose a method based on filtering both SIFT points and images on which to focus on. Hence we will focus on relevant data.
Address Amiens; France; 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 Expedition Conference ORASIS
Notes DAG; 600.077 Approved no
Call Number Admin @ si @ RLL2015 Serial 2626
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Author Olivier Lefebvre; Pau Riba; Charles Fournier; Alicia Fornes; Josep Llados; Rejean Plamondon; Jules Gagnon-Marchand
Title Monitoring neuromotricity on-line: a cloud computing approach Type Conference Article
Year 2015 Publication 17th Conference of the International Graphonomics Society IGS2015 Abbreviated Journal
Volume Issue Pages
Keywords
Abstract The goal of our experiment is to develop a useful and accessible tool that can be used to evaluate a patient's health by analyzing handwritten strokes. We use a cloud computing approach to analyze stroke data sampled on a commercial tablet working on the Android platform and a distant server to perform complex calculations using the Delta and Sigma lognormal algorithms. A Google Drive account is used to store the data and to ease the development of the project. The communication between the tablet, the cloud and the server is encrypted to ensure biomedical information confidentiality. Highly parameterized biomedical tests are implemented on the tablet as well as a free drawing test to evaluate the validity of the data acquired by the first test compared to the second one. A blurred shape model descriptor pattern recognition algorithm is used to classify the data obtained by the free drawing test. The functions presented in this paper are still currently under development and other improvements are needed before launching the application in the public domain.
Address Pointe-à-Pitre; Guadeloupe; 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 Expedition Conference IGS
Notes DAG; 600.077 Approved no
Call Number Admin @ si @ LRF2015 Serial 2617
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Author Gloria Fernandez Esparrach; Jorge Bernal; Cristina Rodriguez de Miguel; Debora Gil; Fernando Vilariño; Henry Cordova; Cristina Sanchez Montes; I.Araujo ; Maria Lopez Ceron; J.Llach; F. Javier Sanchez
Title Colonic polyps are correctly identified by a computer vision method using wm-dova energy maps Type Conference Article
Year 2015 Publication Proceedings of 23 United European- UEG Week 2015 Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
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 Medium
Area Expedition Conference UEG
Notes MV; IAM; 600.075;SIAI Approved no
Call Number Admin @ si @ FBR2015 Serial 2732
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Author Juan Ignacio Toledo; Jordi Cucurull; Jordi Puiggali; Alicia Fornes; Josep Llados
Title Document Analysis Techniques for Automatic Electoral Document Processing: A Survey Type Conference Article
Year 2015 Publication E-Voting and Identity, Proceedings of 5th international conference, VoteID 2015 Abbreviated Journal
Volume Issue Pages 139-141
Keywords Document image analysis; Computer vision; Paper ballots; Paper based elections; Optical scan; Tally
Abstract In this paper, we will discuss the most common challenges in electoral document processing and study the different solutions from the document analysis community that can be applied in each case. We will cover Optical Mark Recognition techniques to detect voter selections in the Australian Ballot, handwritten number recognition for preferential elections and handwriting recognition for write-in areas. We will also propose some particular adjustments that can be made to those general techniques in the specific context of electoral documents.
Address Bern; Switzerland; September 2015
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference VoteID
Notes DAG; 600.061; 602.006; 600.077 Approved no
Call Number Admin @ si @ TCP2015 Serial 2641
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Author Pau Riba; Josep Llados; Alicia Fornes; Anjan Dutta
Title Large-scale Graph Indexing using Binary Embeddings of Node Contexts Type Conference Article
Year 2015 Publication 10th IAPR-TC15 Workshop on Graph-based Representations in Pattern Recognition Abbreviated Journal
Volume 9069 Issue Pages 208-217
Keywords Graph matching; Graph indexing; Application in document analysis; Word spotting; Binary embedding
Abstract Graph-based representations are experiencing a growing usage in visual recognition and retrieval due to their representational power in front of classical appearance-based representations in terms of feature vectors. Retrieving a query graph from a large dataset of graphs has the drawback of the high computational complexity required to compare the query and the target graphs. The most important property for a large-scale retrieval is the search time complexity to be sub-linear in the number of database examples. In this paper we propose a fast indexation formalism for graph retrieval. A binary embedding is defined as hashing keys for graph nodes. Given a database of labeled graphs, graph nodes are complemented with vectors of attributes representing their local context. Hence, each attribute counts the length of a walk of order k originated in a vertex with label l. Each attribute vector is converted to a binary code applying a binary-valued hash function. Therefore, graph retrieval is formulated in terms of finding target graphs in the database whose nodes have a small Hamming distance from the query nodes, easily computed with bitwise logical operators. As an application example, we validate the performance of the proposed methods in a handwritten word spotting scenario in images of historical documents.
Address Beijing; China; May 2015
Corporate Author Thesis
Publisher Springer International Publishing Place of Publication Editor C.-L.Liu; B.Luo; W.G.Kropatsch; J.Cheng
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 978-3-319-18223-0 Medium
Area Expedition Conference GbRPR
Notes DAG; 600.061; 602.006; 600.077 Approved no
Call Number Admin @ si @ RLF2015a Serial 2618
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Author Dennis G.Romero; Anselmo Frizera; Angel Sappa; Boris X. Vintimilla; Teodiano F.Bastos
Title A predictive model for human activity recognition by observing actions and context Type Conference Article
Year 2015 Publication Advanced Concepts for Intelligent Vision Systems, Proceedings of 16th International Conference, ACIVS 2015 Abbreviated Journal
Volume 9386 Issue Pages 323-333
Keywords
Abstract This paper presents a novel model to estimate human activities — a human activity is defined by a set of human actions. The proposed approach is based on the usage of Recurrent Neural Networks (RNN) and Bayesian inference through the continuous monitoring of human actions and its surrounding environment. In the current work human activities are inferred considering not only visual analysis but also additional resources; external sources of information, such as context information, are incorporated to contribute to the activity estimation. The novelty of the proposed approach lies in the way the information is encoded, so that it can be later associated according to a predefined semantic structure. Hence, a pattern representing a given activity can be defined by a set of actions, plus contextual information or other kind of information that could be relevant to describe the activity. Experimental results with real data are provided showing the validity of the proposed approach.
Address Catania; Italy; October 2015
Corporate Author Thesis
Publisher Springer International Publishing 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 978-3-319-25902-4 Medium
Area Expedition Conference ACIVS
Notes ADAS; 600.076 Approved no
Call Number Admin @ si @ RFS2015 Serial 2661
Permanent link to this record
 
 
Author Fahad Shahbaz Khan; Muhammad Anwer Rao; Joost Van de Weijer; Michael Felsberg; J.Laaksonen
Title Deep semantic pyramids for human attributes and action recognition Type Conference Article
Year 2015 Publication Image Analysis, Proceedings of 19th Scandinavian Conference , SCIA 2015 Abbreviated Journal
Volume 9127 Issue Pages 341-353
Keywords Action recognition; Human attributes; Semantic pyramids
Abstract Describing persons and their actions is a challenging problem due to variations in pose, scale and viewpoint in real-world images. Recently, semantic pyramids approach [1] for pose normalization has shown to provide excellent results for gender and action recognition. The performance of semantic pyramids approach relies on robust image description and is therefore limited due to the use of shallow local features. In the context of object recognition [2] and object detection [3], convolutional neural networks (CNNs) or deep features have shown to improve the performance over the conventional shallow features.
We propose deep semantic pyramids for human attributes and action recognition. The method works by constructing spatial pyramids based on CNNs of different part locations. These pyramids are then combined to obtain a single semantic representation. We validate our approach on the Berkeley and 27 Human Attributes datasets for attributes classification. For action recognition, we perform experiments on two challenging datasets: Willow and PASCAL VOC 2010. The proposed deep semantic pyramids provide a significant gain of 17.2%, 13.9%, 24.3% and 22.6% compared to the standard shallow semantic pyramids on Berkeley, 27 Human Attributes, Willow and PASCAL VOC 2010 datasets respectively. Our results also show that deep semantic pyramids outperform conventional CNNs based on the full bounding box of the person. Finally, we compare our approach with state-of-the-art methods and show a gain in performance compared to best methods in literature.
Address Denmark; Copenhagen; June 2015
Corporate Author Thesis
Publisher Springer International Publishing Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 978-3-319-19664-0 Medium
Area Expedition Conference SCIA
Notes LAMP; 600.068; 600.079;ADAS Approved no
Call Number Admin @ si @ KRW2015b Serial 2672
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Author J.Kuhn; A.Nussbaumer; J.Pirker; Dimosthenis Karatzas; A. Pagani; O.Conlan; M.Memmel; C.M.Steiner; C.Gutl; D.Albert; Andreas Dengel
Title Advancing Physics Learning Through Traversing a Multi-Modal Experimentation Space Type Conference Article
Year 2015 Publication Workshop Proceedings on the 11th International Conference on Intelligent Environments Abbreviated Journal
Volume 19 Issue Pages 373-380
Keywords
Abstract Translating conceptual knowledge into real world experiences presents a significant educational challenge. This position paper presents an approach that supports learners in moving seamlessly between conceptual learning and their application in the real world by bringing physical and virtual experiments into everyday settings. Learners are empowered in conducting these situated experiments in a variety of physical settings by leveraging state of the art mobile, augmented reality, and virtual reality technology. A blend of mobile-based multi-sensory physical experiments, augmented reality and enabling virtual environments can allow learners to bridge their conceptual learning with tangible experiences in a completely novel manner. This approach focuses on the learner by applying self-regulated personalised learning techniques, underpinned by innovative pedagogical approaches and adaptation techniques, to ensure that the needs and preferences of each learner are catered for individually.
Address Praga; Chzech Republic; July 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 Expedition Conference IE
Notes DAG; 600.077 Approved no
Call Number Admin @ si @ KNP2015 Serial 2694
Permanent link to this record
 
 
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
Keywords
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.
Address Boston; USA; 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 Expedition Conference CVPRW
Notes MILAB; HuPBA; OR;MV Approved no
Call Number Admin @ si @ SPV2015 Serial 2636
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Author Victor Ponce; Hugo Jair Escalante; Sergio Escalera; Xavier Baro
Title Gesture and Action Recognition by Evolved Dynamic Subgestures Type Conference Article
Year 2015 Publication 26th British Machine Vision Conference Abbreviated Journal
Volume Issue Pages 129.1-129.13
Keywords
Abstract This paper introduces a framework for gesture and action recognition based on the evolution of temporal gesture primitives, or subgestures. Our work is inspired on the principle of producing genetic variations within a population of gesture subsequences, with the goal of obtaining a set of gesture units that enhance the generalization capability of standard gesture recognition approaches. In our context, gesture primitives are evolved over time using dynamic programming and generative models in order to recognize complex actions. In few generations, the proposed subgesture-based representation
of actions and gestures outperforms the state of the art results on the MSRDaily3D and MSRAction3D datasets.
Address Swansea; uk; September 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 Expedition Conference BMVC
Notes HuPBA;MV Approved no
Call Number Admin @ si @ PEE2015 Serial 2657
Permanent link to this record
 
 
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 10 Issue 1 Pages 1-312
Keywords
Abstract Poster
Address Barcelona; Spain; 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 Expedition Conference CARS
Notes IAM; 600.075 Approved no
Call Number Admin @ si @ VGG2015 Serial 2615
Permanent link to this record
 
 
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 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.
Address
Corporate Author Thesis
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
Permanent link to this record