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Author | Katerine Diaz; Jesus Martinez del Rincon; Aura Hernandez-Sabate | ||||
Title | Decremental generalized discriminative common vectors applied to images classification | Type | Journal Article | ||
Year | 2017 | Publication | Knowledge-Based Systems | Abbreviated Journal | KBS |
Volume | 131 | Issue | Pages | 46-57 | |
Keywords | Decremental learning; Generalized Discriminative Common Vectors; Feature extraction; Linear subspace methods; Classification | ||||
Abstract | In this paper, a novel decremental subspace-based learning method called Decremental Generalized Discriminative Common Vectors method (DGDCV) is presented. The method makes use of the concept of decremental learning, which we introduce in the field of supervised feature extraction and classification. By efficiently removing unnecessary data and/or classes for a knowledge base, our methodology is able to update the model without recalculating the full projection or accessing to the previously processed training data, while retaining the previously acquired knowledge. The proposed method has been validated in 6 standard face recognition datasets, showing a considerable computational gain without compromising the accuracy of the model. | ||||
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Notes | ADAS; 600.118; 600.121 | Approved | no | ||
Call Number | Admin @ si @ DMH2017a | Serial | 3003 | ||
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Author | Marta Diez-Ferrer; Debora Gil; Elena Carreño; Susana Padrones; Samantha Aso | ||||
Title | Positive Airway Pressure-Enhanced CT to Improve Virtual Bronchoscopic Navigation | Type | Journal Article | ||
Year | 2017 | Publication | Journal of Thoracic Oncology | Abbreviated Journal | JTO |
Volume | 12 | Issue | 1S | Pages | S596-S597 |
Keywords | Thorax CT; diagnosis; Peripheral Pulmonary Nodule | ||||
Abstract | A main weakness of virtual bronchoscopic navigation (VBN) is unsuccessful segmentation of distal branches approaching peripheral pulmonary nodules (PPN). CT scan acquisition protocol is pivotal for segmentation covering the utmost periphery. We hypothesize that application of continuous positive airway pressure (CPAP) during CT acquisition could improve visualization and segmentation of peripheral bronchi. The purpose of the present pilot study is to compare quality of segmentations under 4 CT acquisition modes: inspiration (INSP), expiration (EXP) and both with CPAP (INSP-CPAP and EXP-CPAP). | ||||
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Notes | IAM; 600.096; 600.075; 600.145 | Approved | no | ||
Call Number | Admin @ si @ DGC2017a | Serial | 2883 | ||
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Author | Antonio Lopez; Gabriel Villalonga; Laura Sellart; German Ros; David Vazquez; Jiaolong Xu; Javier Marin; Azadeh S. Mozafari | ||||
Title | Training my car to see using virtual worlds | Type | Journal Article | ||
Year | 2017 | Publication | Image and Vision Computing | Abbreviated Journal | IMAVIS |
Volume | 38 | Issue | Pages | 102-118 | |
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Abstract | Computer vision technologies are at the core of different advanced driver assistance systems (ADAS) and will play a key role in oncoming autonomous vehicles too. One of the main challenges for such technologies is to perceive the driving environment, i.e. to detect and track relevant driving information in a reliable manner (e.g. pedestrians in the vehicle route, free space to drive through). Nowadays it is clear that machine learning techniques are essential for developing such a visual perception for driving. In particular, the standard working pipeline consists of collecting data (i.e. on-board images), manually annotating the data (e.g. drawing bounding boxes around pedestrians), learning a discriminative data representation taking advantage of such annotations (e.g. a deformable part-based model, a deep convolutional neural network), and then assessing the reliability of such representation with the acquired data. In the last two decades most of the research efforts focused on representation learning (first, designing descriptors and learning classifiers; later doing it end-to-end). Hence, collecting data and, especially, annotating it, is essential for learning good representations. While this has been the case from the very beginning, only after the disruptive appearance of deep convolutional neural networks that it became a serious issue due to their data hungry nature. In this context, the problem is that manual data annotation is a tiresome work prone to errors. Accordingly, in the late 00’s we initiated a research line consisting of training visual models using photo-realistic computer graphics, especially focusing on assisted and autonomous driving. In this paper, we summarize such a work and show how it has become a new tendency with increasing acceptance. | ||||
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Notes | ADAS; 600.118 | Approved | no | ||
Call Number | Admin @ si @ LVS2017 | Serial | 2985 | ||
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Author | Joan Serrat; Felipe Lumbreras; Francisco Blanco; Manuel Valiente; Montserrat Lopez-Mesas | ||||
Title | myStone: A system for automatic kidney stone classification | Type | Journal Article | ||
Year | 2017 | Publication | Expert Systems with Applications | Abbreviated Journal | ESA |
Volume | 89 | Issue | Pages | 41-51 | |
Keywords | Kidney stone; Optical device; Computer vision; Image classification | ||||
Abstract | Kidney stone formation is a common disease and the incidence rate is constantly increasing worldwide. It has been shown that the classification of kidney stones can lead to an important reduction of the recurrence rate. The classification of kidney stones by human experts on the basis of certain visual color and texture features is one of the most employed techniques. However, the knowledge of how to analyze kidney stones is not widespread, and the experts learn only after being trained on a large number of samples of the different classes. In this paper we describe a new device specifically designed for capturing images of expelled kidney stones, and a method to learn and apply the experts knowledge with regard to their classification. We show that with off the shelf components, a carefully selected set of features and a state of the art classifier it is possible to automate this difficult task to a good degree. We report results on a collection of 454 kidney stones, achieving an overall accuracy of 63% for a set of eight classes covering almost all of the kidney stones taxonomy. Moreover, for more than 80% of samples the real class is the first or the second most probable class according to the system, being then the patient recommendations for the two top classes similar. This is the first attempt towards the automatic visual classification of kidney stones, and based on the current results we foresee better accuracies with the increase of the dataset size. | ||||
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Notes | ADAS; MSIAU; 603.046; 600.122; 600.118 | Approved | no | ||
Call Number | Admin @ si @ SLB2017 | Serial | 3026 | ||
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Author | Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera; Huamin Ren; Thomas B. Moeslund; Elham Etemad | ||||
Title | Locality Regularized Group Sparse Coding for Action Recognition | Type | Journal Article | ||
Year | 2017 | Publication | Computer Vision and Image Understanding | Abbreviated Journal | CVIU |
Volume | 158 | Issue | Pages | 106-114 | |
Keywords | Bag of words; Feature encoding; Locality constrained coding; Group sparse coding; Alternating direction method of multipliers; Action recognition | ||||
Abstract | Bag of visual words (BoVW) models are widely utilized in image/ video representation and recognition. The cornerstone of these models is the encoding stage, in which local features are decomposed over a codebook in order to obtain a representation of features. In this paper, we propose a new encoding algorithm by jointly encoding the set of local descriptors of each sample and considering the locality structure of descriptors. The proposed method takes advantages of locality coding such as its stability and robustness to noise in descriptors, as well as the strengths of the group coding strategy by taking into account the potential relation among descriptors of a sample. To efficiently implement our proposed method, we consider the Alternating Direction Method of Multipliers (ADMM) framework, which results in quadratic complexity in the problem size. The method is employed for a challenging classification problem: action recognition by depth cameras. Experimental results demonstrate the outperformance of our methodology compared to the state-of-the-art on the considered datasets. | ||||
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Notes | HuPBA; no proj | Approved | no | ||
Call Number | Admin @ si @ BGE2017 | Serial | 3014 | ||
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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 | |
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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. | ||||
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Notes | MILAB; 601.235 | Approved | no | ||
Call Number | Admin @ si @ DBT2017 | Serial | 2714 | ||
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Author | Katerine Diaz; Konstantia Georgouli; Anastasios Koidis; Jesus Martinez del Rincon | ||||
Title | Incremental model learning for spectroscopy-based food analysis | Type | Journal Article | ||
Year | 2017 | Publication | Chemometrics and Intelligent Laboratory Systems | Abbreviated Journal | CILS |
Volume | 167 | Issue | Pages | 123-131 | |
Keywords | Incremental model learning; IGDCV technique; Subspace based learning; IdentificationVegetable oils; FT-IR spectroscopy | ||||
Abstract | In this paper we propose the use of incremental learning for creating and improving multivariate analysis models in the field of chemometrics of spectral data. As main advantages, our proposed incremental subspace-based learning allows creating models faster, progressively improving previously created models and sharing them between laboratories and institutions without requiring transferring or disclosing individual spectra samples. In particular, our approach allows to improve the generalization and adaptability of previously generated models with a few new spectral samples to be applicable to real-world situations. The potential of our approach is demonstrated using vegetable oil type identification based on spectroscopic data as case study. Results show how incremental models maintain the accuracy of batch learning methodologies while reducing their computational cost and handicaps. | ||||
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Notes | ADAS; 600.118 | Approved | no | ||
Call Number | Admin @ si @ DGK2017 | Serial | 3002 | ||
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Author | Patricia Suarez; Angel Sappa; Boris X. Vintimilla | ||||
Title | Learning to Colorize Infrared Images | Type | Conference Article | ||
Year | 2017 | Publication | 15th International Conference on Practical Applications of Agents and Multi-Agent System | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | CNN in multispectral imaging; Image colorization | ||||
Abstract | This paper focuses on near infrared (NIR) image colorization by using a Generative Adversarial Network (GAN) architecture model. The proposed architecture consists of two stages. Firstly, it learns to colorize the given input, resulting in a RGB image. Then, in the second stage, a discriminative model is used to estimate the probability that the generated image came from the training dataset, rather than the image automatically generated. The proposed model starts the learning process from scratch, because our set of images is very dierent from the dataset used in existing pre-trained models, so transfer learning strategies cannot be used. Infrared image colorization is an important problem when human perception need to be considered, e.g, in remote sensing applications. Experimental results with a large set of real images are provided showing the validity of the proposed approach. | ||||
Address | Porto; Portugal; June 2017 | ||||
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Area | Expedition | Conference | PAAMS | ||
Notes | ADAS; MSIAU; 600.086; 600.122; 600.118 | Approved | no | ||
Call Number | Admin @ si @ | Serial | 2919 | ||
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Author | Hana Jarraya; Oriol Ramos Terrades; Josep Llados | ||||
Title | Graph Embedding through Probabilistic Graphical Model applied to Symbolic Graphs | Type | Conference Article | ||
Year | 2017 | Publication | 8th Iberian Conference on Pattern Recognition and Image Analysis | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | Attributed Graph; Probabilistic Graphical Model; Graph Embedding; Structured Support Vector Machines | ||||
Abstract | We propose a new Graph Embedding (GEM) method that takes advantages of structural pattern representation. It models an Attributed Graph (AG) as a Probabilistic Graphical Model (PGM). Then, it learns the parameters of this PGM presented by a vector. This vector is a signature of AG in a lower dimensional vectorial space. We apply Structured Support Vector Machines (SSVM) to process classification task. As first tentative, results on the GREC dataset are encouraging enough to go further on this direction. | ||||
Address | Faro; Portugal; June 2017 | ||||
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Area | Expedition | Conference | IbPRIA | ||
Notes | DAG; 600.097; 600.121 | Approved | no | ||
Call Number | Admin @ si @ JRL2017a | Serial | 2953 | ||
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Author | German Ros; Laura Sellart; Gabriel Villalonga; Elias Maidanik; Francisco Molero; Marc Garcia; Adriana Cedeño; Francisco Perez; Didier Ramirez; Eduardo Escobar; Jose Luis Gomez; David Vazquez; Antonio Lopez | ||||
Title | Semantic Segmentation of Urban Scenes via Domain Adaptation of SYNTHIA | Type | Book Chapter | ||
Year | 2017 | Publication | Domain Adaptation in Computer Vision Applications | Abbreviated Journal | |
Volume | 12 | Issue | Pages | 227-241 | |
Keywords | SYNTHIA; Virtual worlds; Autonomous Driving | ||||
Abstract | Vision-based semantic segmentation in urban scenarios is a key functionality for autonomous driving. Recent revolutionary results of deep convolutional neural networks (DCNNs) foreshadow the advent of reliable classifiers to perform such visual tasks. However, DCNNs require learning of many parameters from raw images; thus, having a sufficient amount of diverse images with class annotations is needed. These annotations are obtained via cumbersome, human labour which is particularly challenging for semantic segmentation since pixel-level annotations are required. In this chapter, we propose to use a combination of a virtual world to automatically generate realistic synthetic images with pixel-level annotations, and domain adaptation to transfer the models learnt to correctly operate in real scenarios. We address the question of how useful synthetic data can be for semantic segmentation – in particular, when using a DCNN paradigm. In order to answer this question we have generated a synthetic collection of diverse urban images, named SYNTHIA, with automatically generated class annotations and object identifiers. We use SYNTHIA in combination with publicly available real-world urban images with manually provided annotations. Then, we conduct experiments with DCNNs that show that combining SYNTHIA with simple domain adaptation techniques in the training stage significantly improves performance on semantic segmentation. | ||||
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Publisher | Springer | Place of Publication | Editor | Gabriela Csurka | |
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Notes | ADAS; 600.085; 600.082; 600.076; 600.118 | Approved | no | ||
Call Number | ADAS @ adas @ RSV2017 | Serial | 2882 | ||
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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 | |
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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 | ||||
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Area | Expedition | Conference | CVPR | ||
Notes | DAG; 600.084; 600.121 | Approved | no | ||
Call Number | Admin @ si @ GPR2017 | Serial | 2889 | ||
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Author | David Geronimo; David Vazquez; Arturo de la Escalera | ||||
Title | Vision-Based Advanced Driver Assistance Systems | Type | Book Chapter | ||
Year | 2017 | Publication | Computer Vision in Vehicle Technology: Land, Sea, and Air | Abbreviated Journal | |
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Keywords | ADAS; Autonomous Driving | ||||
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Notes | ADAS; 600.118 | Approved | no | ||
Call Number | ADAS @ adas @ GVE2017 | Serial | 2881 | ||
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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 |
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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. | ||||
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Publisher | Taylor & Francis Group | Place of Publication | Editor | ||
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Notes | MILAB; | Approved | no | ||
Call Number | Admin @ si @JDM2017 | Serial | 2784 | ||
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Author | Mireia Forns-Nadal; Federico Sem; Anna Mane; Laura Igual; Dani Guinart; Oscar Vilarroya | ||||
Title | Increased Nucleus Accumbens Volume in First-Episode Psychosis | Type | Journal Article | ||
Year | 2017 | Publication | Psychiatry Research-Neuroimaging | Abbreviated Journal | PRN |
Volume | 263 | Issue | Pages | 57-60 | |
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Abstract | Nucleus accumbens has been reported as a key structure in the neurobiology of schizophrenia. Studies analyzing structural abnormalities have shown conflicting results, possibly related to confounding factors. We investigated the nucleus accumbens volume using manual delimitation in first-episode psychosis (FEP) controlling for age, cannabis use and medication. Thirty-one FEP subjects who were naive or minimally exposed to antipsychotics and a control group were MRI scanned and clinically assessed from baseline to 6 months of follow-up. FEP showed increased relative and total accumbens volumes. Clinical correlations with negative symptoms, duration of untreated psychosis and cannabis use were not significant. | ||||
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Notes | MILAB; no menciona | Approved | no | ||
Call Number | Admin @ si @ FSM2017 | Serial | 3028 | ||
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Author | Sergio Alloza; Flavio Escribano; Sergi Delgado; Ciprian Corneanu; Sergio Escalera | ||||
Title | XBadges. Identifying and training soft skills with commercial video games Improving persistence, risk taking & spatial reasoning with commercial video games and facial and emotional recognition system | Type | Conference Article | ||
Year | 2017 | Publication | 4th Congreso de la Sociedad Española para las Ciencias del Videojuego | Abbreviated Journal | |
Volume | 1957 | Issue | Pages | 13-28 | |
Keywords | Video Games; Soft Skills; Training; Skilling Development; Emotions; Cognitive Abilities; Flappy Bird; Pacman; Tetris | ||||
Abstract | XBadges is a research project based on the hypothesis that commercial video games (nonserious games) can train soft skills. We measure persistence, patial reasoning and risk taking before and after subjects paticipate in controlled game playing sessions.
In addition, we have developed an automatic facial expression recognition system capable of inferring their emotions while playing, allowing us to study the role of emotions in soft skills acquisition. We have used Flappy Bird, Pacman and Tetris for assessing changes in persistence, risk taking and spatial reasoning respectively. Results show how playing Tetris significantly improves spatial reasoning and how playing Pacman significantly improves prudence in certain areas of behavior. As for emotions, they reveal that being concentrated helps to improve performance and skills acquisition. Frustration is also shown as a key element. With the results obtained we are able to glimpse multiple applications in areas which need soft skills development. |
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Address | Barcelona; June 2017 | ||||
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Area | Expedition | Conference | COSECIVI; CEUR-WS | ||
Notes | HUPBA; no menciona | Approved | no | ||
Call Number | Admin @ si @ AED2017 | Serial | 3065 | ||
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