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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 Issue Pages (up) 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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ISSN ISBN Medium
Area Expedition Conference DAS
Notes DAG; 600.084; 600.129; 302.065; 600.121 Approved no
Call Number Admin @ si @ AlR2018a Serial 3104
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Author Ciprian Corneanu; Meysam Madadi; Sergio Escalera
Title Deep Structure Inference Network for Facial Action Unit Recognition Type Conference Article
Year 2018 Publication 15th European Conference on Computer Vision Abbreviated Journal
Volume 11216 Issue Pages (up) 309-324
Keywords Computer Vision; Machine Learning; Deep Learning; Facial Expression Analysis; Facial Action Units; Structure Inference
Abstract Facial expressions are combinations of basic components called Action Units (AU). Recognizing AUs is key for general facial expression analysis. Recently, efforts in automatic AU recognition have been dedicated to learning combinations of local features and to exploiting correlations between AUs. We propose a deep neural architecture that tackles both problems by combining learned local and global features in its initial stages and replicating a message passing algorithm between classes similar to a graphical model inference approach in later stages. We show that by training the model end-to-end with increased supervision we improve state-of-the-art by 5.3% and 8.2% performance on BP4D and DISFA datasets, respectively.
Address Munich; September 2018
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 ECCV
Notes HUPBA; no proj Approved no
Call Number Admin @ si @ CME2018 Serial 3205
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Author Yagmur Gucluturk; Umut Guclu; Xavier Baro; Hugo Jair Escalante; Isabelle Guyon; Sergio Escalera; Marcel A. J. van Gerven; Rob van Lier
Title Multimodal First Impression Analysis with Deep Residual Networks Type Journal Article
Year 2018 Publication IEEE Transactions on Affective Computing Abbreviated Journal TAC
Volume 8 Issue 3 Pages (up) 316-329
Keywords
Abstract People form first impressions about the personalities of unfamiliar individuals even after very brief interactions with them. In this study we present and evaluate several models that mimic this automatic social behavior. Specifically, we present several models trained on a large dataset of short YouTube video blog posts for predicting apparent Big Five personality traits of people and whether they seem suitable to be recommended to a job interview. Along with presenting our audiovisual approach and results that won the third place in the ChaLearn First Impressions Challenge, we investigate modeling in different modalities including audio only, visual only, language only, audiovisual, and combination of audiovisual and language. Our results demonstrate that the best performance could be obtained using a fusion of all data modalities. Finally, in order to promote explainability in machine learning and to provide an example for the upcoming ChaLearn challenges, we present a simple approach for explaining the predictions for job interview recommendations
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Notes HUPBA; no proj Approved no
Call Number Admin @ si @ GGB2018 Serial 3210
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Author Joan Serrat; Felipe Lumbreras; Idoia Ruiz
Title Learning to measure for preshipment garment sizing Type Journal Article
Year 2018 Publication Measurement Abbreviated Journal MEASURE
Volume 130 Issue Pages (up) 327-339
Keywords Apparel; Computer vision; Structured prediction; Regression
Abstract Clothing is still manually manufactured for the most part nowadays, resulting in discrepancies between nominal and real dimensions, and potentially ill-fitting garments. Hence, it is common in the apparel industry to manually perform measures at preshipment time. We present an automatic method to obtain such measures from a single image of a garment that speeds up this task. It is generic and extensible in the sense that it does not depend explicitly on the garment shape or type. Instead, it learns through a probabilistic graphical model to identify the different contour parts. Subsequently, a set of Lasso regressors, one per desired measure, can predict the actual values of the measures. We present results on a dataset of 130 images of jackets and 98 of pants, of varying sizes and styles, obtaining 1.17 and 1.22 cm of mean absolute error, respectively.
Address
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Series Editor Series Title Abbreviated Series Title
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Area Expedition Conference
Notes ADAS; MSIAU; 600.122; 600.118 Approved no
Call Number Admin @ si @ SLR2018 Serial 3128
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Author Carles Sanchez; Miguel Viñas; Coen Antens; Agnes Borras; Debora Gil
Title Back to Front Architecture for Diagnosis as a Service Type Conference Article
Year 2018 Publication 20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing Abbreviated Journal
Volume Issue Pages (up) 343-346
Keywords
Abstract Software as a Service (SaaS) is a cloud computing model in which a provider hosts applications in a server that customers use via internet. Since SaaS does not require to install applications on customers' own computers, it allows the use by multiple users of highly specialized software without extra expenses for hardware acquisition or licensing. A SaaS tailored for clinical needs not only would alleviate licensing costs, but also would facilitate easy access to new methods for diagnosis assistance. This paper presents a SaaS client-server architecture for Diagnosis as a Service (DaaS). The server is based on docker technology in order to allow execution of softwares implemented in different languages with the highest portability and scalability. The client is a content management system allowing the design of websites with multimedia content and interactive visualization of results allowing user editing. We explain a usage case that uses our DaaS as crowdsourcing platform in a multicentric pilot study carried out to evaluate the clinical benefits of a software for assessment of central airway obstruction.
Address Timisoara; Rumania; September 2018
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Series Editor Series Title Abbreviated Series Title
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Area Expedition Conference SYNASC
Notes IAM; 600.145 Approved no
Call Number Admin @ si @ SVA2018 Serial 3360
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Author Mariella Dimiccoli; Cathal Gurrin; David J. Crandall; Xavier Giro; Petia Radeva
Title Introduction to the special issue: Egocentric Vision and Lifelogging Type Journal Article
Year 2018 Publication Journal of Visual Communication and Image Representation Abbreviated Journal JVCIR
Volume 55 Issue Pages (up) 352-353
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Abstract
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Notes MILAB; no proj Approved no
Call Number Admin @ si @ DGC2018 Serial 3187
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Author Patricia Suarez; Angel Sappa; Boris X. Vintimilla
Title Vegetation Index Estimation from Monospectral Images Type Conference Article
Year 2018 Publication 15th International Conference on Images Analysis and Recognition Abbreviated Journal
Volume 10882 Issue Pages (up) 353-362
Keywords
Abstract This paper proposes a novel approach to estimate Normalized Difference Vegetation Index (NDVI) from just the red channel of a RGB image. The NDVI index is defined as the ratio of the difference of the red and infrared radiances over their sum. In other words, information from the red channel of a RGB image and the corresponding infrared spectral band are required for its computation. In the current work the NDVI index is estimated just from the red channel by training a Conditional Generative Adversarial Network (CGAN). The architecture proposed for the generative network consists of a single level structure, which combines at the final layer results from convolutional operations together with the given red channel with Gaussian noise to enhance
details, resulting in a sharp NDVI image. Then, the discriminative model
estimates the probability that the NDVI generated index came from the training dataset, rather than the index automatically generated. Experimental results with a large set of real images are provided showing that a Conditional GAN single level model represents an acceptable approach to estimate NDVI index.
Address Povoa de Varzim; Portugal; June 2018
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 ICIAR
Notes MSIAU; 600.086; 600.130; 600.122 Approved no
Call Number Admin @ si @ SSV2018c Serial 3196
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Author Pau Rodriguez; Josep M. Gonfaus; Guillem Cucurull; Xavier Roca; Jordi Gonzalez
Title Attend and Rectify: A Gated Attention Mechanism for Fine-Grained Recovery Type Conference Article
Year 2018 Publication 15th European Conference on Computer Vision Abbreviated Journal
Volume 11212 Issue Pages (up) 357-372
Keywords Deep Learning; Convolutional Neural Networks; Attention
Abstract We propose a novel attention mechanism to enhance Convolutional Neural Networks for fine-grained recognition. It learns to attend to lower-level feature activations without requiring part annotations and uses these activations to update and rectify the output likelihood distribution. In contrast to other approaches, the proposed mechanism is modular, architecture-independent and efficient both in terms of parameters and computation required. Experiments show that networks augmented with our approach systematically improve their classification accuracy and become more robust to clutter. As a result, Wide Residual Networks augmented with our proposal surpasses the state of the art classification accuracies in CIFAR-10, the Adience gender recognition task, Stanford dogs, and UEC Food-100.
Address Munich; September 2018
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 ECCV
Notes ISE; 600.098; 602.121; 600.119 Approved no
Call Number Admin @ si @ RGC2018 Serial 3139
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Author Lu Yu; Lichao Zhang; Joost Van de Weijer; Fahad Shahbaz Khan; Yongmei Cheng; C. Alejandro Parraga
Title Beyond Eleven Color Names for Image Understanding Type Journal Article
Year 2018 Publication Machine Vision and Applications Abbreviated Journal MVAP
Volume 29 Issue 2 Pages (up) 361-373
Keywords Color name; Discriminative descriptors; Image classification; Re-identification; Tracking
Abstract Color description is one of the fundamental problems of image understanding. One of the popular ways to represent colors is by means of color names. Most existing work on color names focuses on only the eleven basic color terms of the English language. This could be limiting the discriminative power of these representations, and representations based on more color names are expected to perform better. However, there exists no clear strategy to choose additional color names. We collect a dataset of 28 additional color names. To ensure that the resulting color representation has high discriminative power we propose a method to order the additional color names according to their complementary nature with the basic color names. This allows us to compute color name representations with high discriminative power of arbitrary length. In the experiments we show that these new color name descriptors outperform the existing color name descriptor on the task of visual tracking, person re-identification and image classification.
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Notes LAMP; NEUROBIT; 600.068; 600.109; 600.120 Approved no
Call Number Admin @ si @ YYW2018 Serial 3087
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Author I. Sorodoc; S. Pezzelle; A. Herbelot; Mariella Dimiccoli; R. Bernardi
Title Learning quantification from images: A structured neural architecture Type Journal Article
Year 2018 Publication Natural Language Engineering Abbreviated Journal NLE
Volume 24 Issue 3 Pages (up) 363-392
Keywords
Abstract Major advances have recently been made in merging language and vision representations. Most tasks considered so far have confined themselves to the processing of objects and lexicalised relations amongst objects (content words). We know, however, that humans (even pre-school children) can abstract over raw multimodal data to perform certain types of higher level reasoning, expressed in natural language by function words. A case in point is given by their ability to learn quantifiers, i.e. expressions like few, some and all. From formal semantics and cognitive linguistics, we know that quantifiers are relations over sets which, as a simplification, we can see as proportions. For instance, in most fish are red, most encodes the proportion of fish which are red fish. In this paper, we study how well current neural network strategies model such relations. We propose a task where, given an image and a query expressed by an object–property pair, the system must return a quantifier expressing which proportions of the queried object have the queried property. Our contributions are twofold. First, we show that the best performance on this task involves coupling state-of-the-art attention mechanisms with a network architecture mirroring the logical structure assigned to quantifiers by classic linguistic formalisation. Second, we introduce a new balanced dataset of image scenarios associated with quantification queries, which we hope will foster further research in this area.
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Notes MILAB; no menciona Approved no
Call Number Admin @ si @ SPH2018 Serial 3021
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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 Issue Pages (up) 365-372
Keywords
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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Series Editor Series Title Abbreviated Series Title
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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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Author Santi Puch; Irina Sanchez; Aura Hernandez-Sabate; Gemma Piella; Vesna Prckovska
Title Global Planar Convolutions for Improved Context Aggregation in Brain Tumor Segmentation Type Conference Article
Year 2018 Publication International MICCAI Brainlesion Workshop Abbreviated Journal
Volume 11384 Issue Pages (up) 393-405
Keywords Brain tumors; 3D fully-convolutional CNN; Magnetic resonance imaging; Global planar convolution
Abstract In this work, we introduce the Global Planar Convolution module as a building-block for fully-convolutional networks that aggregates global information and, therefore, enhances the context perception capabilities of segmentation networks in the context of brain tumor segmentation. We implement two baseline architectures (3D UNet and a residual version of 3D UNet, ResUNet) and present a novel architecture based on these two architectures, ContextNet, that includes the proposed Global Planar Convolution module. We show that the addition of such module eliminates the need of building networks with several representation levels, which tend to be over-parametrized and to showcase slow rates of convergence. Furthermore, we provide a visual demonstration of the behavior of GPC modules via visualization of intermediate representations. We finally participate in the 2018 edition of the BraTS challenge with our best performing models, that are based on ContextNet, and report the evaluation scores on the validation and the test sets of the challenge.
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Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference MICCAIW
Notes ADAS; 600.118 Approved no
Call Number Admin @ si @ PSH2018 Serial 3251
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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 Issue Pages (up) 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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Area Expedition Conference
Notes ADAS; 600.118 Approved no
Call Number Admin @ si @ LVV2018 Serial 3047
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Author Manuel Carbonell; Mauricio Villegas; Alicia Fornes; Josep Llados
Title Joint Recognition of Handwritten Text and Named Entities with a Neural End-to-end Model Type Conference Article
Year 2018 Publication 13th IAPR International Workshop on Document Analysis Systems Abbreviated Journal
Volume Issue Pages (up) 399-404
Keywords Named entity recognition; Handwritten Text Recognition; neural networks
Abstract When extracting information from handwritten documents, text transcription and named entity recognition are usually faced as separate subsequent tasks. This has the disadvantage that errors in the first module affect heavily the
performance of the second module. In this work we propose to do both tasks jointly, using a single neural network with a common architecture used for plain text recognition. Experimentally, the work has been tested on a collection of historical marriage records. Results of experiments are presented to show the effect on the performance for different
configurations: different ways of encoding the information, doing or not transfer learning and processing at text line or multi-line region level. The results are comparable to state of the art reported in the ICDAR 2017 Information Extraction competition, even though the proposed technique does not use any dictionaries, language modeling or post processing.
Address Vienna; Austria; April 2018
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ISSN ISBN Medium
Area Expedition Conference DAS
Notes DAG; 600.097; 603.057; 601.311; 600.121 Approved no
Call Number Admin @ si @ CVF2018 Serial 3170
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Author Md. Mostafa Kamal Sarker; Hatem A. Rashwan; Hatem A. Rashwan; Estefania Talavera; Syeda Furruka Banu; Petia Radeva; Domenec Puig
Title MACNet: Multi-scale Atrous Convolution Networks for Food Places Classification in Egocentric Photo-streams Type Conference Article
Year 2018 Publication European Conference on Computer Vision workshops Abbreviated Journal
Volume Issue Pages (up) 423-433
Keywords
Abstract First-person (wearable) camera continually captures unscripted interactions of the camera user with objects, people, and scenes reflecting his personal and relational tendencies. One of the preferences of people is their interaction with food events. The regulation of food intake and its duration has a great importance to protect against diseases. Consequently, this work aims to develop a smart model that is able to determine the recurrences of a person on food places during a day. This model is based on a deep end-to-end model for automatic food places recognition by analyzing egocentric photo-streams. In this paper, we apply multi-scale Atrous convolution networks to extract the key features related to food places of the input images. The proposed model is evaluated on an in-house private dataset called “EgoFoodPlaces”. Experimental results shows promising results of food places classification recognition in egocentric photo-streams.
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Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LCNS
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference ECCVW
Notes MILAB; no menciona Approved no
Call Number Admin @ si @ SRR2018b Serial 3185
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