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Author Antonio Lopez; Atsushi Imiya; Tomas Pajdla; Jose Manuel Alvarez
Title Computer Vision in Vehicle Technology: Land, Sea & Air Type Book Whole
Year 2017 Publication (up) Abbreviated Journal
Volume Issue Pages 161-163
Keywords
Abstract Summary This chapter examines different vision-based commercial solutions for real-live problems related to vehicles. It is worth mentioning the recent astonishing performance of deep convolutional neural networks (DCNNs) in difficult visual tasks such as image classification, object recognition/localization/detection, and semantic segmentation. In fact,
different DCNN architectures are already being explored for low-level tasks such as optical flow and disparity computation, and higher level ones such as place recognition.
Address
Corporate Author Thesis
Publisher John Wiley & Sons, Ltd Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-1-118-86807-2 Medium
Area Expedition Conference
Notes ADAS; 600.118 Approved no
Call Number Admin @ si @ LIP2017a Serial 2937
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Author Sergio Escalera; Vassilis Athitsos; Isabelle Guyon
Title Challenges in Multi-modal Gesture Recognition Type Book Chapter
Year 2017 Publication (up) Abbreviated Journal
Volume Issue Pages 1-60
Keywords Gesture recognition; Time series analysis; Multimodal data analysis; Computer vision; Pattern recognition; Wearable sensors; Infrared cameras; Kinect TMTM
Abstract This paper surveys the state of the art on multimodal gesture recognition and introduces the JMLR special topic on gesture recognition 2011–2015. We began right at the start of the Kinect TMTM revolution when inexpensive infrared cameras providing image depth recordings became available. We published papers using this technology and other more conventional methods, including regular video cameras, to record data, thus providing a good overview of uses of machine learning and computer vision using multimodal data in this area of application. Notably, we organized a series of challenges and made available several datasets we recorded for that purpose, including tens of thousands of videos, which are available to conduct further research. We also overview recent state of the art works on gesture recognition based on a proposed taxonomy for gesture recognition, discussing challenges and future lines of research.
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Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes HuPBA; no proj Approved no
Call Number Admin @ si @ EAG2017 Serial 3008
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Author Laura Igual; Santiago Segui
Title Introduction to Data Science – A Python Approach to Concepts, Techniques and Applications. Undergraduate Topics in Computer Science Type Book Whole
Year 2017 Publication (up) Abbreviated Journal
Volume Issue Pages 1-215
Keywords
Abstract
Address
Corporate Author Thesis
Publisher 978-3-319-50016-4 Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-3-319-50016-4 Medium
Area Expedition Conference
Notes MILAB Approved no
Call Number Admin @ si @ IgS2017 Serial 3027
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Author Mireia Sole; Joan Blanco; Debora Gil; Oliver Valero; G. Fonseka; M. Lawrie; Francesca Vidal; Zaida Sarrate
Title Chromosome Territories in Mice Spermatogenesis: A new three-dimensional methodology of study Type Conference Article
Year 2017 Publication (up) 11th European CytoGenesis Conference Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address Florencia; Italia; July 2017
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 ECA
Notes IAM; 600.096; 600.145 Approved no
Call Number Admin @ si @ SBG2017a Serial 2936
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Author Pau Riba; Josep Llados; Alicia Fornes
Title Error-tolerant coarse-to-fine matching model for hierarchical graphs Type Conference Article
Year 2017 Publication (up) 11th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition Abbreviated Journal
Volume 10310 Issue Pages 107-117
Keywords Graph matching; Hierarchical graph; Graph-based representation; Coarse-to-fine matching
Abstract Graph-based representations are effective tools to capture structural information from visual elements. However, retrieving a query graph from a large database of graphs implies a high computational complexity. Moreover, these representations are very sensitive to noise or small changes. In this work, a novel hierarchical graph representation is designed. Using graph clustering techniques adapted from graph-based social media analysis, we propose to generate a hierarchy able to deal with different levels of abstraction while keeping information about the topology. For the proposed representations, a coarse-to-fine matching method is defined. These approaches are validated using real scenarios such as classification of colour images and handwritten word spotting.
Address Anacapri; Italy; May 2017
Corporate Author Thesis
Publisher Springer International Publishing Place of Publication Editor Pasquale Foggia; Cheng-Lin Liu; Mario Vento
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference GbRPR
Notes DAG; 600.097; 601.302; 600.121 Approved no
Call Number Admin @ si @ RLF2017a Serial 2951
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Author Hana Jarraya; Oriol Ramos Terrades; Josep Llados
Title Learning structural loss parameters on graph embedding applied on symbolic graphs Type Conference Article
Year 2017 Publication (up) 12th IAPR International Workshop on Graphics Recognition Abbreviated Journal
Volume Issue Pages
Keywords
Abstract We propose an amelioration of proposed Graph Embedding (GEM) method in previous work that takes advantages of structural pattern representation and the structured distortion. it models an Attributed Graph (AG) as a Probabilistic Graphical Model (PGM). Then, it learns the parameters of this PGM presented by a vector, as new signature of AG in a lower dimensional vectorial space. We focus to adapt the structured learning algorithm via 1_slack formulation with a suitable risk function, called Graph Edit Distance (GED). It defines the dissimilarity of the ground truth and predicted graph labels. It determines by the error tolerant graph matching using bipartite graph matching algorithm. We apply Structured Support Vector Machines (SSVM) to process classification task. During our experiments, we got our results on the GREC dataset.
Address Kyoto; Japan; November 2017
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 GREC
Notes DAG; 600.097; 600.121 Approved no
Call Number Admin @ si @ JRL2017b Serial 3073
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Author Sounak Dey; Anjan Dutta; Josep Llados; Alicia Fornes; Umapada Pal
Title Shallow Neural Network Model for Hand-drawn Symbol Recognition in Multi-Writer Scenario Type Conference Article
Year 2017 Publication (up) 12th IAPR International Workshop on Graphics Recognition Abbreviated Journal
Volume Issue Pages 31-32
Keywords
Abstract One of the main challenges in hand drawn symbol recognition is the variability among symbols because of the different writer styles. In this paper, we present and discuss some results recognizing hand-drawn symbols with a shallow neural network. A neural network model inspired from the LeNet architecture has been used to achieve state-of-the-art results with
very less training data, which is very unlikely to the data hungry deep neural network. From the results, it has become evident that the neural network architectures can efficiently describe and recognize hand drawn symbols from different writers and can model the inter author aberration
Address
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Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference GREC
Notes DAG; 600.097; 600.121 Approved no
Call Number Admin @ si @ DDL2017 Serial 3057
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Author Pau Riba; Anjan Dutta; Josep Llados; Alicia Fornes
Title Graph-based deep learning for graphics classification Type Conference Article
Year 2017 Publication (up) 12th IAPR International Workshop on Graphics Recognition Abbreviated Journal
Volume Issue Pages 29-30
Keywords
Abstract Graph-based representations are a common way to deal with graphics recognition problems. However, previous works were mainly focused on developing learning-free techniques. The success of deep learning frameworks have proved that learning is a powerful tool to solve many problems, however it is not straightforward to extend these methodologies to non euclidean data such as graphs. On the other hand, graphs are a good representational structure for graphical entities. In this work, we present some deep learning techniques that have been proposed in the literature for graph-based representations and
we show how they can be used in graphics recognition problems
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 GREC
Notes DAG; 600.097; 601.302; 600.121 Approved no
Call Number Admin @ si @ RDL2017b Serial 3058
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Author Adria Rico; Alicia Fornes
Title Camera-based Optical Music Recognition using a Convolutional Neural Network Type Conference Article
Year 2017 Publication (up) 12th IAPR International Workshop on Graphics Recognition Abbreviated Journal
Volume Issue Pages 27-28
Keywords optical music recognition; document analysis; convolutional neural network; deep learning
Abstract Optical Music Recognition (OMR) consists in recognizing images of music scores. Contrary to expectation, the current OMR systems usually fail when recognizing images of scores captured by digital cameras and smartphones. In this work, we propose a camera-based OMR system based on Convolutional Neural Networks, showing promising preliminary results
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 GREC
Notes DAG;600.097; 600.121 Approved no
Call Number Admin @ si @ RiF2017 Serial 3059
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Author Julio C. S. Jacques Junior; Xavier Baro; Sergio Escalera
Title Exploiting feature representations through similarity learning and ranking aggregation for person re-identification Type Conference Article
Year 2017 Publication (up) 12th IEEE International Conference on Automatic Face and Gesture Recognition Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Person re-identification has received special attentionby the human analysis community in the last few years.To address the challenges in this field, many researchers haveproposed different strategies, which basically exploit eithercross-view invariant features or cross-view robust metrics. Inthis work we propose to combine different feature representationsthrough ranking aggregation. Spatial information, whichpotentially benefits the person matching, is represented usinga 2D body model, from which color and texture informationare extracted and combined. We also consider contextualinformation (background and foreground data), automaticallyextracted via Deep Decompositional Network, and the usage ofConvolutional Neural Network (CNN) features. To describe thematching between images we use the polynomial feature map,also taking into account local and global information. Finally,the Stuart ranking aggregation method is employed to combinecomplementary ranking lists obtained from different featurerepresentations. Experimental results demonstrated that weimprove the state-of-the-art on VIPeR and PRID450s datasets,achieving 58.77% and 71.56% on top-1 rank recognitionrate, respectively, as well as obtaining competitive results onCUHK01 dataset.
Address Washington; DC; USA; May 2017
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 FG
Notes HUPBA; 602.143 Approved no
Call Number Admin @ si @ JBE2017 Serial 2923
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Author Iiris Lusi; Julio C. S. Jacques Junior; Jelena Gorbova; Xavier Baro; Sergio Escalera; Hasan Demirel; Juri Allik; Cagri Ozcinar; Gholamreza Anbarjafari
Title Joint Challenge on Dominant and Complementary Emotion Recognition Using Micro Emotion Features and Head-Pose Estimation: Databases Type Conference Article
Year 2017 Publication (up) 12th IEEE International Conference on Automatic Face and Gesture Recognition Abbreviated Journal
Volume Issue Pages
Keywords
Abstract In this work two databases for the Joint Challenge on Dominant and Complementary Emotion Recognition Using Micro Emotion Features and Head-Pose Estimation1 are introduced. Head pose estimation paired with and detailed emotion recognition have become very important in relation to human-computer interaction. The 3D head pose database, SASE, is a 3D database acquired with Microsoft Kinect 2 camera, including RGB and depth information of different head poses which is composed by a total of 30000 frames with annotated markers, including 32 male and 18 female subjects. For the dominant and complementary emotion database, iCVMEFED, includes 31250 images with different emotions of 115 subjects whose gender distribution is almost uniform. For each subject there are 5 samples. The emotions are composed by 7 basic emotions plus neutral, being defined as complementary and dominant pairs. The emotion associated to the images were labeled with the support of psychologists.
Address Washington; DC; USA; May 2017
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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Area Expedition Conference FG
Notes HUPBA; no menciona Approved no
Call Number Admin @ si @ LJG2017 Serial 2924
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Author Chirster Loob; Pejman Rasti; Iiris Lusi; Julio C. S. Jacques Junior; Xavier Baro; Sergio Escalera; Tomasz Sapinski; Dorota Kaminska; Gholamreza Anbarjafari
Title Dominant and Complementary Multi-Emotional Facial Expression Recognition Using C-Support Vector Classification Type Conference Article
Year 2017 Publication (up) 12th IEEE International Conference on Automatic Face and Gesture Recognition Abbreviated Journal
Volume Issue Pages
Keywords
Abstract We are proposing a new facial expression recognition model which introduces 30+ detailed facial expressions recognisable by any artificial intelligence interacting with a human. Throughout this research, we introduce two categories for the emotions, namely, dominant emotions and complementary emotions. In this research paper the complementary emotion is recognised by using the eye region if the dominant emotion is angry, fearful or sad, and if the dominant emotion is disgust or happiness the complementary emotion is mainly conveyed by the mouth. In order to verify the tagged dominant and complementary emotions, randomly chosen people voted for the recognised multi-emotional facial expressions. The average results of voting are showing that 73.88% of the voters agree on the correctness of the recognised multi-emotional facial expressions.
Address Washington; DC; USA; May 2017
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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ISSN ISBN Medium
Area Expedition Conference FG
Notes HUPBA; no menciona Approved no
Call Number Admin @ si @ LRL2017 Serial 2925
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Author Meysam Madadi; Sergio Escalera; Alex Carruesco; Carlos Andujar; Xavier Baro; Jordi Gonzalez
Title Occlusion Aware Hand Pose Recovery from Sequences of Depth Images Type Conference Article
Year 2017 Publication (up) 12th IEEE International Conference on Automatic Face and Gesture Recognition Abbreviated Journal
Volume Issue Pages
Keywords
Abstract State-of-the-art approaches on hand pose estimation from depth images have reported promising results under quite controlled considerations. In this paper we propose a two-step pipeline for recovering the hand pose from a sequence of depth images. The pipeline has been designed to deal with images taken from any viewpoint and exhibiting a high degree of finger occlusion. In a first step we initialize the hand pose using a part-based model, fitting a set of hand components in the depth images. In a second step we consider temporal data and estimate the parameters of a trained bilinear model consisting of shape and trajectory bases. Results on a synthetic, highly-occluded dataset demonstrate that the proposed method outperforms most recent pose recovering approaches, including those based on CNNs.
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Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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Area Expedition Conference FG
Notes HUPBA; ISE; 602.143; 600.098; 600.119 Approved no
Call Number Admin @ si @ MEC2017 Serial 2970
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Author Maryam Asadi-Aghbolaghi; Albert Clapes; Marco Bellantonio; Hugo Jair Escalante; Victor Ponce; Xavier Baro; Isabelle Guyon; Shohreh Kasaei; Sergio Escalera
Title A survey on deep learning based approaches for action and gesture recognition in image sequences Type Conference Article
Year 2017 Publication (up) 12th IEEE International Conference on Automatic Face and Gesture Recognition Abbreviated Journal
Volume Issue Pages
Keywords
Abstract The interest in action and gesture recognition has grown considerably in the last years. In this paper, we present a survey on current deep learning methodologies for action and gesture recognition in image sequences. We introduce a taxonomy that summarizes important aspects of deep learning
for approaching both tasks. We review the details of the proposed architectures, fusion strategies, main datasets, and competitions.
We summarize and discuss the main works proposed so far with particular interest on how they treat the temporal dimension of data, discussing their main features and identify opportunities and challenges for future research.
Address Washington; USA; May 2017
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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ISSN ISBN Medium
Area Expedition Conference FG
Notes HUPBA; no proj Approved no
Call Number Admin @ si @ ACB2017b Serial 2982
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Author Eirikur Agustsson; Radu Timofte; Sergio Escalera; Xavier Baro; Isabelle Guyon; Rasmus Rothe
Title Apparent and real age estimation in still images with deep residual regressors on APPA-REAL database Type Conference Article
Year 2017 Publication (up) 12th IEEE International Conference on Automatic Face and Gesture Recognition Abbreviated Journal
Volume Issue Pages
Keywords
Abstract After decades of research, the real (biological) age estimation from a single face image reached maturity thanks to the availability of large public face databases and impressive accuracies achieved by recently proposed methods.
The estimation of “apparent age” is a related task concerning the age perceived by human observers. Significant advances have been also made in this new research direction with the recent Looking At People challenges. In this paper we make several contributions to age estimation research. (i) We introduce APPA-REAL, a large face image database with both real and apparent age annotations. (ii) We study the relationship between real and apparent age. (iii) We develop a residual age regression method to further improve the performance. (iv) We show that real age estimation can be successfully tackled as an apparent age estimation followed by an apparent to real age residual regression. (v) We graphically reveal the facial regions on which the CNN focuses in order to perform apparent and real age estimation tasks.
Address Washington;USA; May 2017
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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ISSN ISBN Medium
Area Expedition Conference FG
Notes HUPBA; no menciona Approved no
Call Number Admin @ si @ ATE2017 Serial 3013
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