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Author Marc Bolaños; Mariella Dimiccoli; Petia Radeva
Title Towards Storytelling from Visual Lifelogging: An Overview Type Journal Article
Year 2017 Publication IEEE Transactions on Human-Machine Systems Abbreviated Journal THMS
Volume 47 Issue 1 Pages 77 - 90
Keywords (up)
Abstract Visual lifelogging consists of acquiring images that capture the daily experiences of the user by wearing a camera over a long period of time. The pictures taken offer considerable potential for knowledge mining concerning how people live their lives, hence, they open up new opportunities for many potential applications in fields including healthcare, security, leisure and
the quantified self. However, automatically building a story from a huge collection of unstructured egocentric data presents major challenges. This paper provides a thorough review of advances made so far in egocentric data analysis, and in view of the current state of the art, indicates new lines of research to move us towards storytelling from visual lifelogging.
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
Notes MILAB; 601.235 Approved no
Call Number Admin @ si @ BDR2017 Serial 2712
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Author Mariella Dimiccoli; Marc Bolaños; Estefania Talavera; Maedeh Aghaei; Stavri G. Nikolov; Petia Radeva
Title SR-Clustering: Semantic Regularized Clustering for Egocentric Photo Streams Segmentation Type Journal Article
Year 2017 Publication Computer Vision and Image Understanding Abbreviated Journal CVIU
Volume 155 Issue Pages 55-69
Keywords (up)
Abstract While wearable cameras are becoming increasingly popular, locating relevant information in large unstructured collections of egocentric images is still a tedious and time consuming processes. This paper addresses the problem of organizing egocentric photo streams acquired by a wearable camera into semantically meaningful segments. First, contextual and semantic information is extracted for each image by employing a Convolutional Neural Networks approach. Later, by integrating language processing, a vocabulary of concepts is defined in a semantic space. Finally, by exploiting the temporal coherence in photo streams, images which share contextual and semantic attributes are grouped together. The resulting temporal segmentation is particularly suited for further analysis, ranging from activity and event recognition to semantic indexing and summarization. Experiments over egocentric sets of nearly 17,000 images, show that the proposed approach outperforms state-of-the-art methods.
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
Notes MILAB; 601.235 Approved no
Call Number Admin @ si @ DBT2017 Serial 2714
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Author Jean-Pascal Jacob; Mariella Dimiccoli; L. Moisan
Title Active skeleton for bacteria modelling Type Journal Article
Year 2017 Publication Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization Abbreviated Journal CMBBE
Volume 5 Issue 4 Pages 274-286
Keywords (up)
Abstract The investigation of spatio-temporal dynamics of bacterial cells and their molecular components requires automated image analysis tools to track cell shape properties and molecular component locations inside the cells. In the study of bacteria aging, the molecular components of interest are protein aggregates accumulated near bacteria boundaries. This particular location makes very ambiguous the correspondence between aggregates and cells, since computing accurately bacteria boundaries in phase-contrast time-lapse imaging is a challenging task. This paper proposes an active skeleton formulation for bacteria modelling which provides several advantages: an easy computation of shape properties (perimeter, length, thickness and orientation), an improved boundary accuracy in noisy images and a natural bacteria-centred coordinate system that permits the intrinsic location of molecular components inside the cell. Starting from an initial skeleton estimate, the medial axis of the bacterium is obtained by minimising an energy function which incorporates bacteria shape constraints. Experimental results on biological images and comparative evaluation of the performances validate the proposed approach for modelling cigar-shaped bacteria like Escherichia coli. The Image-J plugin of the proposed method can be found online at http://fluobactracker.inrialpes.fr.
Address
Corporate Author Thesis
Publisher Taylor & Francis Group 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 MILAB; Approved no
Call Number Admin @ si @JDM2017 Serial 2784
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Author Daniel Hernandez; Lukas Schneider; Antonio Espinosa; David Vazquez; Antonio Lopez; Uwe Franke; Marc Pollefeys; Juan C. Moure
Title Slanted Stixels: Representing San Francisco's Steepest Streets Type Conference Article
Year 2017 Publication 28th British Machine Vision Conference Abbreviated Journal
Volume Issue Pages
Keywords (up)
Abstract In this work we present a novel compact scene representation based on Stixels that infers geometric and semantic information. Our approach overcomes the previous rather restrictive geometric assumptions for Stixels by introducing a novel depth model to account for non-flat roads and slanted objects. Both semantic and depth cues are used jointly to infer the scene representation in a sound global energy minimization formulation. Furthermore, a novel approximation scheme is introduced that uses an extremely efficient over-segmentation. In doing so, the computational complexity of the Stixel inference algorithm is reduced significantly, achieving real-time computation capabilities with only a slight drop in accuracy. We evaluate the proposed approach in terms of semantic and geometric accuracy as well as run-time on four publicly available benchmark datasets. Our approach maintains accuracy on flat road scene datasets while improving substantially on a novel non-flat road dataset.
Address London; uk; September 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 BMVC
Notes ADAS; 600.118 Approved no
Call Number ADAS @ adas @ HSE2017a Serial 2945
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Author Ozan Caglayan; Walid Aransa; Adrien Bardet; Mercedes Garcia-Martinez; Fethi Bougares; Loic Barrault; Marc Masana; Luis Herranz; Joost Van de Weijer
Title LIUM-CVC Submissions for WMT17 Multimodal Translation Task Type Conference Article
Year 2017 Publication 2nd Conference on Machine Translation Abbreviated Journal
Volume Issue Pages
Keywords (up)
Abstract This paper describes the monomodal and multimodal Neural Machine Translation systems developed by LIUM and CVC for WMT17 Shared Task on Multimodal Translation. We mainly explored two multimodal architectures where either global visual features or convolutional feature maps are integrated in order to benefit from visual context. Our final systems ranked first for both En-De and En-Fr language pairs according to the automatic evaluation metrics METEOR and BLEU.
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 WMT
Notes LAMP; 600.106; 600.120 Approved no
Call Number Admin @ si @ CAB2017 Serial 3035
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Author Xavier Perez Sala; Fernando De la Torre; Laura Igual; Sergio Escalera; Cecilio Angulo
Title Subspace Procrustes Analysis Type Journal Article
Year 2017 Publication International Journal of Computer Vision Abbreviated Journal IJCV
Volume 121 Issue 3 Pages 327–343
Keywords (up)
Abstract Procrustes Analysis (PA) has been a popular technique to align and build 2-D statistical models of shapes. Given a set of 2-D shapes PA is applied to remove rigid transformations. Then, a non-rigid 2-D model is computed by modeling (e.g., PCA) the residual. Although PA has been widely used, it has several limitations for modeling 2-D shapes: occluded landmarks and missing data can result in local minima solutions, and there is no guarantee that the 2-D shapes provide a uniform sampling of the 3-D space of rotations for the object. To address previous issues, this paper proposes Subspace PA (SPA). Given several
instances of a 3-D object, SPA computes the mean and a 2-D subspace that can simultaneously model all rigid and non-rigid deformations of the 3-D object. We propose a discrete (DSPA) and continuous (CSPA) formulation for SPA, assuming that 3-D samples of an object are provided. DSPA extends the traditional PA, and produces unbiased 2-D models by uniformly sampling different views of the 3-D object. CSPA provides a continuous approach to uniformly sample the space of 3-D rotations, being more efficient in space and time. Experiments using SPA to learn 2-D models of bodies from motion capture data illustrate the benefits of our approach.
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
Notes MILAB; HuPBA; no proj Approved no
Call Number Admin @ si @ PTI2017 Serial 2841
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Author Frederic Sampedro; Anna Domenech; Sergio Escalera; Ignasi Carrio
Title Computing quantitative indicators of structural renal damage in pediatric DMSA scans Type Journal Article
Year 2017 Publication Revista Española de Medicina Nuclear e Imagen Molecular Abbreviated Journal REMNIM
Volume 36 Issue 2 Pages 72-77
Keywords (up)
Abstract OBJECTIVES:
The proposal and implementation of a computational framework for the quantification of structural renal damage from 99mTc-dimercaptosuccinic acid (DMSA) scans. The aim of this work is to propose, implement, and validate a computational framework for the quantification of structural renal damage from DMSA scans and in an observer-independent manner.
MATERIALS AND METHODS:
From a set of 16 pediatric DMSA-positive scans and 16 matched controls and using both expert-guided and automatic approaches, a set of image-derived quantitative indicators was computed based on the relative size, intensity and histogram distribution of the lesion. A correlation analysis was conducted in order to investigate the association of these indicators with other clinical data of interest in this scenario, including C-reactive protein (CRP), white cell count, vesicoureteral reflux, fever, relative perfusion, and the presence of renal sequelae in a 6-month follow-up DMSA scan.
RESULTS:
A fully automatic lesion detection and segmentation system was able to successfully classify DMSA-positive from negative scans (AUC=0.92, sensitivity=81% and specificity=94%). The image-computed relative size of the lesion correlated with the presence of fever and CRP levels (p<0.05), and a measurement derived from the distribution histogram of the lesion obtained significant performance results in the detection of permanent renal damage (AUC=0.86, sensitivity=100% and specificity=75%).
CONCLUSIONS:
The proposal and implementation of a computational framework for the quantification of structural renal damage from DMSA scans showed a promising potential to complement visual diagnosis and non-imaging indicators.
Address
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
Notes HuPBA;MILAB; no menciona Approved no
Call Number Admin @ si @ SDE2017 Serial 2842
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Author Jose Garcia-Rodriguez; Isabelle Guyon; Sergio Escalera; Alexandra Psarrou; Andrew Lewis; Miguel Cazorla
Title Editorial: Special Issue on Computational Intelligence for Vision and Robotics Type Journal Article
Year 2017 Publication Neural Computing and Applications Abbreviated Journal Neural Computing and Applications
Volume 28 Issue 5 Pages 853–854
Keywords (up)
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
Notes HuPBA;MILAB; no menciona Approved no
Call Number Admin @ si @ GGE2017 Serial 2845
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Author Pau Riba; Josep Llados; Alicia Fornes; Anjan Dutta
Title Large-scale graph indexing using binary embeddings of node contexts for information spotting in document image databases Type Journal Article
Year 2017 Publication Pattern Recognition Letters Abbreviated Journal PRL
Volume 87 Issue Pages 203-211
Keywords (up)
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. However, retrieving a query graph from a large dataset of graphs implies a high computational complexity. The most important property for a large-scale retrieval is the search time complexity to be sub-linear in the number of database examples. With this aim, in this paper we propose a graph indexation formalism applied to visual 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. Then, 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 different real scenarios such as handwritten word spotting in images of historical documents or symbol spotting in architectural floor plans.
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
Notes DAG; 600.097; 602.006; 603.053; 600.121 Approved no
Call Number RLF2017b Serial 2873
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Author Lluis Gomez; Dimosthenis Karatzas
Title TextProposals: a Text‐specific Selective Search Algorithm for Word Spotting in the Wild Type Journal Article
Year 2017 Publication Pattern Recognition Abbreviated Journal PR
Volume 70 Issue Pages 60-74
Keywords (up)
Abstract Motivated by the success of powerful while expensive techniques to recognize words in a holistic way (Goel et al., 2013; Almazán et al., 2014; Jaderberg et al., 2016) object proposals techniques emerge as an alternative to the traditional text detectors. In this paper we introduce a novel object proposals method that is specifically designed for text. We rely on a similarity based region grouping algorithm that generates a hierarchy of word hypotheses. Over the nodes of this hierarchy it is possible to apply a holistic word recognition method in an efficient way.

Our experiments demonstrate that the presented method is superior in its ability of producing good quality word proposals when compared with class-independent algorithms. We show impressive recall rates with a few thousand proposals in different standard benchmarks, including focused or incidental text datasets, and multi-language scenarios. Moreover, the combination of our object proposals with existing whole-word recognizers (Almazán et al., 2014; Jaderberg et al., 2016) shows competitive performance in end-to-end word spotting, and, in some benchmarks, outperforms previously published results. Concretely, in the challenging ICDAR2015 Incidental Text dataset, we overcome in more than 10% F-score the best-performing method in the last ICDAR Robust Reading Competition (Karatzas, 2015). Source code of the complete end-to-end system is available at https://github.com/lluisgomez/TextProposals.
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
Notes DAG; 600.084; 601.197; 600.121; 600.129 Approved no
Call Number Admin @ si @ GoK2017 Serial 2886
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Author Lluis Gomez; Anguelos Nicolaou; Dimosthenis Karatzas
Title Improving patch‐based scene text script identification with ensembles of conjoined networks Type Journal Article
Year 2017 Publication Pattern Recognition Abbreviated Journal PR
Volume 67 Issue Pages 85-96
Keywords (up)
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
Notes DAG; 600.084; 600.121; 600.129 Approved no
Call Number Admin @ si @ GNK2017 Serial 2887
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Author Lluis Gomez; Y. Patel; Marçal Rusiñol; C.V. Jawahar; Dimosthenis Karatzas
Title Self‐supervised learning of visual features through embedding images into text topic spaces Type Conference Article
Year 2017 Publication 30th IEEE Conference on Computer Vision and Pattern Recognition Abbreviated Journal
Volume Issue Pages
Keywords (up)
Abstract End-to-end training from scratch of current deep architectures for new computer vision problems would require Imagenet-scale datasets, and this is not always possible. In this paper we present a method that is able to take advantage of freely available multi-modal content to train computer vision algorithms without human supervision. We put forward the idea of performing self-supervised learning of visual features by mining a large scale corpus of multi-modal (text and image) documents. We show that discriminative visual features can be learnt efficiently by training a CNN to predict the semantic context in which a particular image is more probable to appear as an illustration. For this we leverage the hidden semantic structures discovered in the text corpus with a well-known topic modeling technique. Our experiments demonstrate state of the art performance in image classification, object detection, and multi-modal retrieval compared to recent self-supervised or natural-supervised approaches.
Address Honolulu; Hawaii; 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 CVPR
Notes DAG; 600.084; 600.121 Approved no
Call Number Admin @ si @ GPR2017 Serial 2889
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Author Ivet Rafegas; Javier Vazquez; Robert Benavente; Maria Vanrell; Susana Alvarez
Title Enhancing spatio-chromatic representation with more-than-three color coding for image description Type Journal Article
Year 2017 Publication Journal of the Optical Society of America A Abbreviated Journal JOSA A
Volume 34 Issue 5 Pages 827-837
Keywords (up)
Abstract Extraction of spatio-chromatic features from color images is usually performed independently on each color channel. Usual 3D color spaces, such as RGB, present a high inter-channel correlation for natural images. This correlation can be reduced using color-opponent representations, but the spatial structure of regions with small color differences is not fully captured in two generic Red-Green and Blue-Yellow channels. To overcome these problems, we propose a new color coding that is adapted to the specific content of each image. Our proposal is based on two steps: (a) setting the number of channels to the number of distinctive colors we find in each image (avoiding the problem of channel correlation), and (b) building a channel representation that maximizes contrast differences within each color channel (avoiding the problem of low local contrast). We call this approach more-than-three color coding (MTT) to enhance the fact that the number of channels is adapted to the image content. The higher color complexity an image has, the more channels can be used to represent it. Here we select distinctive colors as the most predominant in the image, which we call color pivots, and we build the new color coding using these color pivots as a basis. To evaluate the proposed approach we measure its efficiency in an image categorization task. We show how a generic descriptor improves its performance at the description level when applied on the MTT coding.
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
Notes CIC; 600.087 Approved no
Call Number Admin @ si @ RVB2017 Serial 2892
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Author Marçal Rusiñol; Josep Llados
Title Flowchart Recognition in Patent Information Retrieval Type Book Chapter
Year 2017 Publication Current Challenges in Patent Information Retrieval Abbreviated Journal
Volume 37 Issue Pages 351-368
Keywords (up)
Abstract
Address
Corporate Author Thesis
Publisher Springer Berlin Heidelberg Place of Publication Editor M. Lupu; K. Mayer; N. Kando; A.J. Trippe
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes DAG; 600.097; 600.121 Approved no
Call Number Admin @ si @ RuL2017 Serial 2896
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Author Victor Vaquero; German Ros; Francesc Moreno-Noguer; Antonio Lopez; Alberto Sanfeliu
Title Joint coarse-and-fine reasoning for deep optical flow Type Conference Article
Year 2017 Publication 24th International Conference on Image Processing Abbreviated Journal
Volume Issue Pages 2558-2562
Keywords (up)
Abstract We propose a novel representation for dense pixel-wise estimation tasks using CNNs that boosts accuracy and reduces training time, by explicitly exploiting joint coarse-and-fine reasoning. The coarse reasoning is performed over a discrete classification space to obtain a general rough solution, while the fine details of the solution are obtained over a continuous regression space. In our approach both components are jointly estimated, which proved to be beneficial for improving estimation accuracy. Additionally, we propose a new network architecture, which combines coarse and fine components by treating the fine estimation as a refinement built on top of the coarse solution, and therefore adding details to the general prediction. We apply our approach to the challenging problem of optical flow estimation and empirically validate it against state-of-the-art CNN-based solutions trained from scratch and tested on large optical flow datasets.
Address Beijing; China; September 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 ICIP
Notes ADAS; 600.118 Approved no
Call Number Admin @ si @ VRM2017 Serial 2898
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