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Author Jose M. Armingol; Jorge Alfonso; Nourdine Aliane; Miguel Clavijo; Sergio Campos-Cordobes; Arturo de la Escalera; Javier del Ser; Javier Fernandez; Fernando Garcia; Felipe Jimenez; Antonio Lopez; Mario Mata
Title Environmental Perception for Intelligent Vehicles Type Book Chapter
Year 2018 Publication Intelligent Vehicles. Enabling Technologies and Future Developments Abbreviated Journal
Volume (down) Issue Pages 23–101
Keywords Computer vision; laser techniques; data fusion; advanced driver assistance systems; traffic monitoring systems; intelligent vehicles
Abstract Environmental perception represents, because of its complexity, a challenge for Intelligent Transport Systems due to the great variety of situations and different elements that can happen in road environments and that must be faced by these systems. In connection with this, so far there are a variety of solutions as regards sensors and methods, so the results of precision, complexity, cost, or computational load obtained by these works are different. In this chapter some systems based on computer vision and laser techniques are presented. Fusion methods are also introduced in order to provide advanced and reliable perception systems.
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Notes ADAS; 600.118 Approved no
Call Number Admin @ si @AAA2018 Serial 3046
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Author Antonio Lopez; David Vazquez; Gabriel Villalonga
Title Data for Training Models, Domain Adaptation Type Book Chapter
Year 2018 Publication Intelligent Vehicles. Enabling Technologies and Future Developments Abbreviated Journal
Volume (down) Issue Pages 395–436
Keywords Driving simulator; hardware; software; interface; traffic simulation; macroscopic simulation; microscopic simulation; virtual data; training data
Abstract Simulation can enable several developments in the field of intelligent vehicles. This chapter is divided into three main subsections. The first one deals with driving simulators. The continuous improvement of hardware performance is a well-known fact that is allowing the development of more complex driving simulators. The immersion in the simulation scene is increased by high fidelity feedback to the driver. In the second subsection, traffic simulation is explained as well as how it can be used for intelligent transport systems. Finally, it is rather clear that sensor-based perception and action must be based on data-driven algorithms. Simulation could provide data to train and test algorithms that are afterwards implemented in vehicles. These tools are explained in the third subsection.
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Notes ADAS; 600.118 Approved no
Call Number Admin @ si @ LVV2018 Serial 3047
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Author Sounak Dey; Anjan Dutta; Juan Ignacio Toledo; Suman Ghosh; Josep Llados; Umapada Pal
Title SigNet: Convolutional Siamese Network for Writer Independent Offline Signature Verification Type Miscellaneous
Year 2018 Publication Arxiv Abbreviated Journal
Volume (down) Issue Pages
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Abstract Offline signature verification is one of the most challenging tasks in biometrics and document forensics. Unlike other verification problems, it needs to model minute but critical details between genuine and forged signatures, because a skilled falsification might often resembles the real signature with small deformation. This verification task is even harder in writer independent scenarios which is undeniably fiscal for realistic cases. In this paper, we model an offline writer independent signature verification task with a convolutional Siamese network. Siamese networks are twin networks with shared weights, which can be trained to learn a feature space where similar observations are placed in proximity. This is achieved by exposing the network to a pair of similar and dissimilar observations and minimizing the Euclidean distance between similar pairs while simultaneously maximizing it between dissimilar pairs. Experiments conducted on cross-domain datasets emphasize the capability of our network to model forgery in different languages (scripts) and handwriting styles. Moreover, our designed Siamese network, named SigNet, exceeds the state-of-the-art results on most of the benchmark signature datasets, which paves the way for further research in this direction.
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Notes DAG; 600.097; 600.121 Approved no
Call Number Admin @ si @ DDT2018 Serial 3085
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Author Dena Bazazian; Dimosthenis Karatzas; Andrew Bagdanov
Title Soft-PHOC Descriptor for End-to-End Word Spotting in Egocentric Scene Images Type Conference Article
Year 2018 Publication International Workshop on Egocentric Perception, Interaction and Computing at ECCV Abbreviated Journal
Volume (down) Issue Pages
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Abstract Word spotting in natural scene images has many applications in scene understanding and visual assistance. We propose Soft-PHOC, an intermediate representation of images based on character probability maps. Our representation extends the concept of the Pyramidal Histogram Of Characters (PHOC) by exploiting Fully Convolutional Networks to derive a pixel-wise mapping of the character distribution within candidate word regions. We show how to use our descriptors for word spotting tasks in egocentric camera streams through an efficient text line proposal algorithm. This is based on the Hough Transform over character attribute maps followed by scoring using Dynamic Time Warping (DTW). We evaluate our results on ICDAR 2015 Challenge 4 dataset of incidental scene text captured by an egocentric camera.
Address Munich; Alemanya; September 2018
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Area Expedition Conference ECCVW
Notes DAG; 600.129; 600.121; Approved no
Call Number Admin @ si @ BKB2018b Serial 3174
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Author Jorge Bernal; Aymeric Histace; Marc Masana; Quentin Angermann; Cristina Sanchez Montes; Cristina Rodriguez de Miguel; Maroua Hammami; Ana Garcia Rodriguez; Henry Cordova; Olivier Romain; Gloria Fernandez Esparrach; Xavier Dray; F. Javier Sanchez
Title Polyp Detection Benchmark in Colonoscopy Videos using GTCreator: A Novel Fully Configurable Tool for Easy and Fast Annotation of Image Databases Type Conference Article
Year 2018 Publication 32nd International Congress and Exhibition on Computer Assisted Radiology & Surgery Abbreviated Journal
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Notes ISE; MV; 600.119 Approved no
Call Number Admin @ si @ BHM2018 Serial 3089
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Author Hugo Jair Escalante; Heysem Kaya; Albert Ali Salah; Sergio Escalera; Yagmur Gucluturk; Umut Guclu; Xavier Baro; Isabelle Guyon; Julio C. S. Jacques Junior; Meysam Madadi; Stephane Ayache; Evelyne Viegas; Furkan Gurpinar; Achmadnoer Sukma Wicaksana; Cynthia C. S. Liem; Marcel A. J. van Gerven; Rob van Lier
Title Explaining First Impressions: Modeling, Recognizing, and Explaining Apparent Personality from Videos Type Miscellaneous
Year 2018 Publication Arxiv Abbreviated Journal
Volume (down) Issue Pages
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Abstract Explainability and interpretability are two critical aspects of decision support systems. Within computer vision, they are critical in certain tasks related to human behavior analysis such as in health care applications. Despite their importance, it is only recently that researchers are starting to explore these aspects. This paper provides an introduction to explainability and interpretability in the context of computer vision with an emphasis on looking at people tasks. Specifically, we review and study those mechanisms in the context of first impressions analysis. To the best of our knowledge, this is the first effort in this direction. Additionally, we describe a challenge we organized on explainability in first impressions analysis from video. We analyze in detail the newly introduced data set, the evaluation protocol, and summarize the results of the challenge. Finally, derived from our study, we outline research opportunities that we foresee will be decisive in the near future for the development of the explainable computer vision field.
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Notes HUPBA Approved no
Call Number Admin @ si @ JKS2018 Serial 3095
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Author Lluis Gomez; Marçal Rusiñol; Ali Furkan Biten; Dimosthenis Karatzas
Title Subtitulació automàtica d'imatges. Estat de l'art i limitacions en el context arxivístic Type Conference Article
Year 2018 Publication Jornades Imatge i Recerca Abbreviated Journal
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Notes DAG; 600.084; 600.135; 601.338; 600.121; 600.129 Approved no
Call Number Admin @ si @ GRB2018 Serial 3173
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Author Lluis Gomez; Marçal Rusiñol; Dimosthenis Karatzas
Title Cutting Sayre's Knot: Reading Scene Text without Segmentation. Application to Utility Meters Type Conference Article
Year 2018 Publication 13th IAPR International Workshop on Document Analysis Systems Abbreviated Journal
Volume (down) Issue Pages 97-102
Keywords Robust Reading; End-to-end Systems; CNN; Utility Meters
Abstract In this paper we present a segmentation-free system for reading text in natural scenes. A CNN architecture is trained in an end-to-end manner, and is able to directly output readings without any explicit text localization step. In order to validate our proposal, we focus on the specific case of reading utility meters. We present our results in a large dataset of images acquired by different users and devices, so text appears in any location, with different sizes, fonts and lengths, and the images present several distortions such as
dirt, illumination highlights or blur.
Address Viena; Austria; April 2018
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Area Expedition Conference DAS
Notes DAG; 600.084; 600.121; 600.129 Approved no
Call Number Admin @ si @ GRK2018 Serial 3102
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Author Dimosthenis Karatzas; Lluis Gomez; Marçal Rusiñol; Anguelos Nicolaou
Title The Robust Reading Competition Annotation and Evaluation Platform Type Conference Article
Year 2018 Publication 13th IAPR International Workshop on Document Analysis Systems Abbreviated Journal
Volume (down) Issue Pages 61-66
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Abstract The ICDAR Robust Reading Competition (RRC), initiated in 2003 and reestablished in 2011, has become the defacto evaluation standard for the international community. Concurrent with its second incarnation in 2011, a continuous
effort started to develop an online framework to facilitate the hosting and management of competitions. This short paper briefly outlines the Robust Reading Competition Annotation and Evaluation Platform, the backbone of the
Robust Reading Competition, comprising a collection of tools and processes that aim to simplify the management and annotation of data, and to provide online and offline performance evaluation and analysis services.
Address Viena; Austria; April 2018
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Notes DAG; 600.084; 600.121 Approved no
Call Number KGR2018 Serial 3103
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Author David Aldavert; Marçal Rusiñol
Title Manuscript text line detection and segmentation using second-order derivatives analysis Type Conference Article
Year 2018 Publication 13th IAPR International Workshop on Document Analysis Systems Abbreviated Journal
Volume (down) Issue Pages 293 - 298
Keywords text line detection; text line segmentation; text region detection; second-order derivatives
Abstract In this paper, we explore the use of second-order derivatives to detect text lines on handwritten document images. Taking advantage that the second derivative gives a minimum response when a dark linear element over a
bright background has the same orientation as the filter, we use this operator to create a map with the local orientation and strength of putative text lines in the document. Then, we detect line segments by selecting and merging the filter responses that have a similar orientation and scale. Finally, text lines are found by merging the segments that are within the same text region. The proposed segmentation algorithm, is learning-free while showing a performance similar to the state of the art methods in publicly available datasets.
Address Viena; Austria; April 2018
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Notes DAG; 600.084; 600.129; 302.065; 600.121 Approved no
Call Number Admin @ si @ AlR2018a Serial 3104
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Author David Aldavert; Marçal Rusiñol
Title Synthetically generated semantic codebook for Bag-of-Visual-Words based word spotting Type Conference Article
Year 2018 Publication 13th IAPR International Workshop on Document Analysis Systems Abbreviated Journal
Volume (down) Issue Pages 223 - 228
Keywords Word Spotting; Bag of Visual Words; Synthetic Codebook; Semantic Information
Abstract Word-spotting methods based on the Bag-ofVisual-Words framework have demonstrated a good retrieval performance even when used in a completely unsupervised manner. Although unsupervised approaches are suitable for
large document collections due to the cost of acquiring labeled data, these methods also present some drawbacks. For instance, having to train a suitable “codebook” for a certain dataset has a high computational cost. Therefore, in
this paper we present a database agnostic codebook which is trained from synthetic data. The aim of the proposed approach is to generate a codebook where the only information required is the type of script used in the document. The use of synthetic data also allows to easily incorporate semantic
information in the codebook generation. So, the proposed method is able to determine which set of codewords have a semantic representation of the descriptor feature space. Experimental results show that the resulting codebook attains a state-of-the-art performance while having a more compact representation.
Address Viena; Austria; April 2018
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Notes DAG; 600.084; 600.129; 600.121 Approved no
Call Number Admin @ si @ AlR2018b Serial 3105
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Author V. Poulain d'Andecy; Emmanuel Hartmann; Marçal Rusiñol
Title Field Extraction by hybrid incremental and a-priori structural templates Type Conference Article
Year 2018 Publication 13th IAPR International Workshop on Document Analysis Systems Abbreviated Journal
Volume (down) Issue Pages 251 - 256
Keywords Layout Analysis; information extraction; incremental learning
Abstract In this paper, we present an incremental framework for extracting information fields from administrative documents. First, we demonstrate some limits of the existing state-of-the-art methods such as the delay of the system efficiency. This is a concern in industrial context when we have only few samples of each document class. Based on this analysis, we propose a hybrid system combining incremental learning by means of itf-df statistics and a-priori generic
models. We report in the experimental section our results obtained with a dataset of real invoices.
Address Viena; Austria; April 2018
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Notes DAG; 600.084; 600.129; 600.121 Approved no
Call Number Admin @ si @ PHR2018 Serial 3106
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Author Felipe Codevilla; Matthias Muller; Antonio Lopez; Vladlen Koltun; Alexey Dosovitskiy
Title End-to-end Driving via Conditional Imitation Learning Type Conference Article
Year 2018 Publication IEEE International Conference on Robotics and Automation Abbreviated Journal
Volume (down) Issue Pages 4693 - 4700
Keywords
Abstract Deep networks trained on demonstrations of human driving have learned to follow roads and avoid obstacles. However, driving policies trained via imitation learning cannot be controlled at test time. A vehicle trained end-to-end to imitate an expert cannot be guided to take a specific turn at an upcoming intersection. This limits the utility of such systems. We propose to condition imitation learning on high-level command input. At test time, the learned driving policy functions as a chauffeur that handles sensorimotor coordination but continues to respond to navigational commands. We evaluate different architectures for conditional imitation learning in vision-based driving. We conduct experiments in realistic three-dimensional simulations of urban driving and on a 1/5 scale robotic truck that is trained to drive in a residential area. Both systems drive based on visual input yet remain responsive to high-level navigational commands. The supplementary video can be viewed at this https URL
Address Brisbane; Australia; May 2018
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Area Expedition Conference ICRA
Notes ADAS; 600.116; 600.124; 600.118 Approved no
Call Number Admin @ si @ CML2018 Serial 3108
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Author Stefan Lonn; Petia Radeva; Mariella Dimiccoli
Title A picture is worth a thousand words but how to organize thousands of pictures? Type Miscellaneous
Year 2018 Publication Arxiv Abbreviated Journal
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Abstract We live in a society where the large majority of the population has a camera-equipped smartphone. In addition, hard drives and cloud storage are getting cheaper and cheaper, leading to a tremendous growth in stored personal photos. Unlike photo collections captured by a digital camera, which typically are pre-processed by the user who organizes them into event-related folders, smartphone pictures are automatically stored in the cloud. As a consequence, photo collections captured by a smartphone are highly unstructured and because smartphones are ubiquitous, they present a larger variability compared to pictures captured by a digital camera. To solve the need of organizing large smartphone photo collections automatically, we propose here a new methodology for hierarchical photo organization into topics and topic-related categories. Our approach successfully estimates latent topics in the pictures by applying probabilistic Latent Semantic Analysis, and automatically assigns a name to each topic by relying on a lexical database. Topic-related categories are then estimated by using a set of topic-specific Convolutional Neuronal Networks. To validate our approach, we ensemble and make public a large dataset of more than 8,000 smartphone pictures from 10 persons. Experimental results demonstrate better user satisfaction with respect to state of the art solutions in terms of organization.
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Notes MILAB; no proj Approved no
Call Number Admin @ si @ LRD2018 Serial 3111
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Author Md. Mostafa Kamal Sarker; Mohammed Jabreel; Hatem A. Rashwan; Syeda Furruka Banu; Petia Radeva; Domenec Puig
Title CuisineNet: Food Attributes Classification using Multi-scale Convolution Network Type Conference Article
Year 2018 Publication 21st International Conference of the Catalan Association for Artificial Intelligence Abbreviated Journal
Volume (down) Issue Pages 365-372
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Abstract Diversity of food and its attributes represents the culinary habits of peoples from different countries. Thus, this paper addresses the problem of identifying food culture of people around the world and its flavor by classifying two main food attributes, cuisine and flavor. A deep learning model based on multi-scale convotuional networks is proposed for extracting more accurate features from input images. The aggregation of multi-scale convolution layers with different kernel size is also used for weighting the features results from different scales. In addition, a joint loss function based on Negative Log Likelihood (NLL) is used to fit the model probability to multi labeled classes for multi-modal classification task. Furthermore, this work provides a new dataset for food attributes, so-called Yummly48K, extracted from the popular food website, Yummly. Our model is assessed on the constructed Yummly48K dataset. The experimental results show that our proposed method yields 65% and 62% average F1 score on validation and test set which outperforming the state-of-the-art models.
Address Roses; catalonia; October 2018
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Area Expedition Conference CCIA
Notes MILAB; no menciona Approved no
Call Number Admin @ si @ SJR2018 Serial 3113
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