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Author | Maryam Asadi-Aghbolaghi; Hugo Bertiche; Vicent Roig; Shohreh Kasaei; Sergio Escalera | ||||
Title | Action Recognition from RGB-D Data: Comparison and Fusion of Spatio-temporal Handcrafted Features and Deep Strategies | Type | Conference Article | ||
Year | 2017 | Publication | Chalearn Workshop on Action, Gesture, and Emotion Recognition: Large Scale Multimodal Gesture Recognition and Real versus Fake expressed emotions at ICCV | Abbreviated Journal | |
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Address | Venice; Italy; October 2017 | ||||
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Area | Expedition | Conference | ICCVW | ||
Notes | HUPBA; no menciona | Approved | no | ||
Call Number | Admin @ si @ ABR2017 | Serial | 3068 | ||
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Author | Albert Clapes; Tinne Tuytelaars; Sergio Escalera | ||||
Title | Darwintrees for action recognition | Type | Conference Article | ||
Year | 2017 | Publication | Chalearn Workshop on Action, Gesture, and Emotion Recognition: Large Scale Multimodal Gesture Recognition and Real versus Fake expressed emotions at ICCV | Abbreviated Journal | |
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Area | Expedition | Conference | ICCVW | ||
Notes | HUPBA; no menciona | Approved | no | ||
Call Number | Admin @ si @ CTE2017 | Serial | 3069 | ||
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Author | Raul Gomez; Baoguang Shi; Lluis Gomez; Lukas Numann; Andreas Veit; Jiri Matas; Serge Belongie; Dimosthenis Karatzas | ||||
Title | ICDAR2017 Robust Reading Challenge on COCO-Text | Type | Conference Article | ||
Year | 2017 | Publication | 14th International Conference on Document Analysis and Recognition | Abbreviated Journal | |
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Address | Kyoto; Japan; November 2017 | ||||
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Area | Expedition | Conference | ICDAR | ||
Notes | DAG; 600.121 | Approved | no | ||
Call Number | Admin @ si @ GSG2017 | Serial | 3076 | ||
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Author | 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 | |
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Address | Thessaloniki; Greece; October 2017 | ||||
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Area | Expedition | Conference | IMEKOFOODS | ||
Notes | ADAS; 600.118 | Approved | no | ||
Call Number | Admin @ si @ GDM2017 | Serial | 3081 | ||
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Author | Ivet Rafegas | ||||
Title | Color in Visual Recognition: from flat to deep representations and some biological parallelisms | Type | Book Whole | ||
Year | 2017 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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Abstract | Visual recognition is one of the main problems in computer vision that attempts to solve image understanding by deciding what objects are in images. This problem can be computationally solved by using relevant sets of visual features, such as edges, corners, color or more complex object parts. This thesis contributes to how color features have to be represented for recognition tasks.
Image features can be extracted following two different approaches. A first approach is defining handcrafted descriptors of images which is then followed by a learning scheme to classify the content (named flat schemes in Kruger et al. (2013). In this approach, perceptual considerations are habitually used to define efficient color features. Here we propose a new flat color descriptor based on the extension of color channels to boost the representation of spatio-chromatic contrast that surpasses state-of-the-art approaches. However, flat schemes present a lack of generality far away from the capabilities of biological systems. A second approach proposes evolving these flat schemes into a hierarchical process, like in the visual cortex. This includes an automatic process to learn optimal features. These deep schemes, and more specifically Convolutional Neural Networks (CNNs), have shown an impressive performance to solve various vision problems. However, there is a lack of understanding about the internal representation obtained, as a result of automatic learning. In this thesis we propose a new methodology to explore the internal representation of trained CNNs by defining the Neuron Feature as a visualization of the intrinsic features encoded in each individual neuron. Additionally, and inspired by physiological techniques, we propose to compute different neuron selectivity indexes (e.g., color, class, orientation or symmetry, amongst others) to label and classify the full CNN neuron population to understand learned representations. Finally, using the proposed methodology, we show an in-depth study on how color is represented on a specific CNN, trained for object recognition, that competes with primate representational abilities (Cadieu et al (2014)). We found several parallelisms with biological visual systems: (a) a significant number of color selectivity neurons throughout all the layers; (b) an opponent and low frequency representation of color oriented edges and a higher sampling of frequency selectivity in brightness than in color in 1st layer like in V1; (c) a higher sampling of color hue in the second layer aligned to observed hue maps in V2; (d) a strong color and shape entanglement in all layers from basic features in shallower layers (V1 and V2) to object and background shapes in deeper layers (V4 and IT); and (e) a strong correlation between neuron color selectivities and color dataset bias. |
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Address | November 2017 | ||||
Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Maria Vanrell | |
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ISSN | ISBN | 978-84-945373-7-0 | Medium | ||
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Notes | CIC | Approved | no | ||
Call Number | Admin @ si @ Raf2017 | Serial | 3100 | ||
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Author | Ishaan Gulrajani; Kundan Kumar; Faruk Ahmed; Adrien Ali Taiga; Francesco Visin; David Vazquez; Aaron Courville | ||||
Title | PixelVAE: A Latent Variable Model for Natural Images | Type | Conference Article | ||
Year | 2017 | Publication | 5th International Conference on Learning Representations | Abbreviated Journal | |
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Keywords | Deep Learning; Unsupervised Learning | ||||
Abstract | Natural image modeling is a landmark challenge of unsupervised learning. Variational Autoencoders (VAEs) learn a useful latent representation and generate samples that preserve global structure but tend to suffer from image blurriness. PixelCNNs model sharp contours and details very well, but lack an explicit latent representation and have difficulty modeling large-scale structure in a computationally efficient way. In this paper, we present PixelVAE, a VAE model with an autoregressive decoder based on PixelCNN. The resulting architecture achieves state-of-the-art log-likelihood on binarized MNIST. We extend PixelVAE to a hierarchy of multiple latent variables at different scales; this hierarchical model achieves competitive likelihood on 64x64 ImageNet and generates high-quality samples on LSUN bedrooms. | ||||
Address | Toulon; France; April 2017 | ||||
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Area | Expedition | Conference | ICLR | ||
Notes | ADAS; 600.085; 600.076; 601.281; 600.118 | Approved | no | ||
Call Number | ADAS @ adas @ GKA2017 | Serial | 2815 | ||
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Author | Alejandro Cartas; Mariella Dimiccoli; Petia Radeva | ||||
Title | Batch-based activity recognition from egocentric photo-streams | Type | Conference Article | ||
Year | 2017 | Publication | 1st International workshop on Egocentric Perception, Interaction and Computing | Abbreviated Journal | |
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Abstract | Activity recognition from long unstructured egocentric photo-streams has several applications in assistive technology such as health monitoring and frailty detection, just to name a few. However, one of its main technical challenges is to deal with the low frame rate of wearable photo-cameras, which causes abrupt appearance changes between consecutive frames. In consequence, important discriminatory low-level features from motion such as optical flow cannot be estimated. In this paper, we present a batch-driven approach for training a deep learning architecture that strongly rely on Long short-term units to tackle this problem. We propose two different implementations of the same approach that process a photo-stream sequence using batches of fixed size with the goal of capturing the temporal evolution of high-level features. The main difference between these implementations is that one explicitly models consecutive batches by overlapping them. Experimental results over a public dataset acquired by three users demonstrate the validity of the proposed architectures to exploit the temporal evolution of convolutional features over time without relying on event boundaries. | ||||
Address | Venice; Italy; October 2017; | ||||
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Area | Expedition | Conference | ICCV - EPIC | ||
Notes | MILAB; no menciona | Approved | no | ||
Call Number | Admin @ si @ CDR2017 | Serial | 3023 | ||
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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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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 | 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 | 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 | 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 | 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 | 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 | |
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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 | 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 | |
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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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