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Author (up) Konstantia Georgouli; Katerine Diaz; Jesus Martinez del Rincon; Anastasios Koidis
Title Building generic, easily-updatable chemometric models with harmonisation and augmentation features: The case of FTIR vegetable oils classification Type Conference Article
Year 2017 Publication 3rd Ιnternational Conference Metrology Promoting Standardization and Harmonization in Food and Nutrition Abbreviated Journal
Volume Issue Pages
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Abstract
Address Thessaloniki; Greece; October 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 IMEKOFOODS
Notes ADAS; 600.118 Approved no
Call Number Admin @ si @ GDM2017 Serial 3081
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Author (up) Lasse Martensson; Anders Hast; Alicia Fornes
Title Word Spotting as a Tool for Scribal Attribution Type Conference Article
Year 2017 Publication 2nd Conference of the association of Digital Humanities in the Nordic Countries Abbreviated Journal
Volume Issue Pages 87-89
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Abstract
Address Gothenburg; Suecia; March 2017
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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 978-91-88348-83-8 Medium
Area Expedition Conference DHN
Notes DAG; 600.097; 600.121 Approved no
Call Number Admin @ si @ MHF2017 Serial 2954
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Author (up) 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 Abbreviated Journal
Volume Issue Pages 1-215
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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 (up) Laura Lopez-Fuentes; Andrew Bagdanov; Joost Van de Weijer; Harald Skinnemoen
Title Bandwidth Limited Object Recognition in High Resolution Imagery Type Conference Article
Year 2017 Publication IEEE Winter conference on Applications of Computer Vision Abbreviated Journal
Volume Issue Pages
Keywords
Abstract This paper proposes a novel method to optimize bandwidth usage for object detection in critical communication scenarios. We develop two operating models of active information seeking. The first model identifies promising regions in low resolution imagery and progressively requests higher resolution regions on which to perform recognition of higher semantic quality. The second model identifies promising regions in low resolution imagery while simultaneously predicting the approximate location of the object of higher semantic quality. From this general framework, we develop a car recognition system via identification of its license plate and evaluate the performance of both models on a car dataset that we introduce. Results are compared with traditional JPEG compression and demonstrate that our system saves up to one order of magnitude of bandwidth while sacrificing little in terms of recognition performance.
Address Santa Rosa; CA; USA; March 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 WACV
Notes LAMP; 600.068; 600.109; 600.084; 600.106; 600.079; 600.120 Approved no
Call Number Admin @ si @ LBW2017 Serial 2973
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Author (up) Laura Lopez-Fuentes; Claudio Rossi; Harald Skinnemoen
Title River segmentation for flood monitoring Type Conference Article
Year 2017 Publication Data Science for Emergency Management at Big Data 2017 Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Floods are major natural disasters which cause deaths and material damages every year. Monitoring these events is crucial in order to reduce both the affected people and the economic losses. In this work we train and test three different Deep Learning segmentation algorithms to estimate the water area from river images, and compare their performances. We discuss the implementation of a novel data chain aimed to monitor river water levels by automatically process data collected from surveillance cameras, and to give alerts in case of high increases of the water level or flooding. We also create and openly publish the first image dataset for river water segmentation.
Address
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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
Notes LAMP; 600.084; 600.120 Approved no
Call Number Admin @ si @ LRS2017 Serial 3078
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Author (up) Laura Lopez-Fuentes; Joost Van de Weijer; Marc Bolaños; Harald Skinnemoen
Title Multi-modal Deep Learning Approach for Flood Detection Type Conference Article
Year 2017 Publication MediaEval Benchmarking Initiative for Multimedia Evaluation Abbreviated Journal
Volume Issue Pages
Keywords
Abstract In this paper we propose a multi-modal deep learning approach to detect floods in social media posts. Social media posts normally contain some metadata and/or visual information, therefore in order to detect the floods we use this information. The model is based on a Convolutional Neural Network which extracts the visual features and a bidirectional Long Short-Term Memory network to extract the semantic features from the textual metadata. We validate the
method on images extracted from Flickr which contain both visual information and metadata and compare the results when using both, visual information only or metadata only. This work has been done in the context of the MediaEval Multimedia Satellite Task.
Address Dublin; Ireland; September 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 MediaEval
Notes LAMP; 600.084; 600.109; 600.120 Approved no
Call Number Admin @ si @ LWB2017a Serial 2974
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Author (up) Laura Lopez-Fuentes; Sebastia Massanet; Manuel Gonzalez-Hidalgo
Title Image vignetting reduction via a maximization of fuzzy entropy Type Conference Article
Year 2017 Publication IEEE International Conference on Fuzzy Systems Abbreviated Journal
Volume Issue Pages
Keywords
Abstract In many computer vision applications, vignetting is an undesirable effect which must be removed in a pre-processing step. Recently, an algorithm for image vignetting correction has been presented by means of a minimization of log-intensity entropy. This method relies on an increase of the entropy of the image when it is affected with vignetting. In this paper, we propose a novel algorithm to reduce image vignetting via a maximization of the fuzzy entropy of the image. Fuzzy entropy quantifies the fuzziness degree of a fuzzy set and its value is also modified by the presence of vignetting. The experimental results show that this novel algorithm outperforms in most cases the algorithm based on the minimization of log-intensity entropy both from the qualitative and the quantitative point of view.
Address Napoles; Italia; July 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 FUZZ-IEEE
Notes LAMP; 600.120 Approved no
Call Number Admin @ si @ LMG2017 Serial 2972
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Author (up) Leonardo Galteri; Dena Bazazian; Lorenzo Seidenari; Marco Bertini; Andrew Bagdanov; Anguelos Nicolaou; Dimosthenis Karatzas; Alberto del Bimbo
Title Reading Text in the Wild from Compressed Images Type Conference Article
Year 2017 Publication 1st International workshop on Egocentric Perception, Interaction and Computing Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Reading text in the wild is gaining attention in the computer vision community. Images captured in the wild are almost always compressed to varying degrees, depending on application context, and this compression introduces artifacts
that distort image content into the captured images. In this paper we investigate the impact these compression artifacts have on text localization and recognition in the wild. We also propose a deep Convolutional Neural Network (CNN) that can eliminate text-specific compression artifacts and which leads to an improvement in text recognition. Experimental results on the ICDAR-Challenge4 dataset demonstrate that compression artifacts have a significant
impact on text localization and recognition and that our approach yields an improvement in both – especially at high compression rates.
Address Venice; Italy; October 2017
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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 ICCV - EPIC
Notes DAG; 600.084; 600.121 Approved no
Call Number Admin @ si @ GBS2017 Serial 3006
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Author (up) 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
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Address
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Notes DAG; 600.084; 600.121; 600.129 Approved no
Call Number Admin @ si @ GNK2017 Serial 2887
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Author (up) 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
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
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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 (up) Lluis Gomez; Marçal Rusiñol; Dimosthenis Karatzas
Title LSDE: Levenshtein Space Deep Embedding for Query-by-string Word Spotting Type Conference Article
Year 2017 Publication 14th International Conference on Document Analysis and Recognition Abbreviated Journal
Volume Issue Pages
Keywords
Abstract n this paper we present the LSDE string representation and its application to handwritten word spotting. LSDE is a novel embedding approach for representing strings that learns a space in which distances between projected points are correlated with the Levenshtein edit distance between the original strings.
We show how such a representation produces a more semantically interpretable retrieval from the user’s perspective than other state of the art ones such as PHOC and DCToW. We also conduct a preliminary handwritten word spotting experiment on the George Washington 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 ICDAR
Notes DAG; 600.084; 600.121 Approved no
Call Number Admin @ si @ GRK2017 Serial 2999
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Author (up) 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
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 (up) Lluis Pere de las Heras; Oriol Ramos Terrades; Josep Llados
Title Ontology-Based Understanding of Architectural Drawings Type Book Chapter
Year 2017 Publication International Workshop on Graphics Recognition. GREC 2015.Graphic Recognition. Current Trends and Challenges Abbreviated Journal
Volume 9657 Issue Pages 75-85
Keywords Graphics recognition; Floor plan analysi; Domain ontology
Abstract In this paper we present a knowledge base of architectural documents aiming at improving existing methods of floor plan classification and understanding. It consists of an ontological definition of the domain and the inclusion of real instances coming from both, automatically interpreted and manually labeled documents. The knowledge base has proven to be an effective tool to structure our knowledge and to easily maintain and upgrade it. Moreover, it is an appropriate means to automatically check the consistency of relational data and a convenient complement of hard-coded knowledge interpretation systems.
Address
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Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
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Area Expedition Conference
Notes DAG; 600.121 Approved no
Call Number Admin @ si @ HRL2017 Serial 3086
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Author (up) Luis Herranz; Shuqiang Jiang; Ruihan Xu
Title Modeling Restaurant Context for Food Recognition Type Journal Article
Year 2017 Publication IEEE Transactions on Multimedia Abbreviated Journal TMM
Volume 19 Issue 2 Pages 430 - 440
Keywords
Abstract Food photos are widely used in food logs for diet monitoring and in social networks to share social and gastronomic experiences. A large number of these images are taken in restaurants. Dish recognition in general is very challenging, due to different cuisines, cooking styles, and the intrinsic difficulty of modeling food from its visual appearance. However, contextual knowledge can be crucial to improve recognition in such scenario. In particular, geocontext has been widely exploited for outdoor landmark recognition. Similarly, we exploit knowledge about menus and location of restaurants and test images. We first adapt a framework based on discarding unlikely categories located far from the test image. Then, we reformulate the problem using a probabilistic model connecting dishes, restaurants, and locations. We apply that model in three different tasks: dish recognition, restaurant recognition, and location refinement. Experiments on six datasets show that by integrating multiple evidences (visual, location, and external knowledge) our system can boost the performance in all tasks.
Address
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Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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Area Expedition Conference
Notes LAMP; 600.120 Approved no
Call Number Admin @ si @ HJX2017 Serial 2965
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Author (up) Maedeh Aghaei; Mariella Dimiccoli; Petia Radeva
Title All the people around me: face clustering in egocentric photo streams Type Conference Article
Year 2017 Publication 24th International Conference on Image Processing Abbreviated Journal
Volume Issue Pages
Keywords face discovery; face clustering; deepmatching; bag-of-tracklets; egocentric photo-streams
Abstract arxiv1703.01790
Given an unconstrained stream of images captured by a wearable photo-camera (2fpm), we propose an unsupervised bottom-up approach for automatic clustering appearing faces into the individual identities present in these data. The problem is challenging since images are acquired under real world conditions; hence the visible appearance of the people in the images undergoes intensive variations. Our proposed pipeline consists of first arranging the photo-stream into events, later, localizing the appearance of multiple people in them, and
finally, grouping various appearances of the same person across different events. Experimental results performed on a dataset acquired by wearing a photo-camera during one month, demonstrate the effectiveness of the proposed approach for the considered purpose.
Address Beijing; China; September 2017
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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 ICIP
Notes MILAB; no menciona Approved no
Call Number Admin @ si @ EDR2017 Serial 3025
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