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| Author | Pau Rodriguez; Jordi Gonzalez; Jordi Cucurull; Josep M. Gonfaus; Xavier Roca | ||||
| Title | Regularizing CNNs with Locally Constrained Decorrelations | Type | Conference Article | ||
| Year | 2017 | Publication | 5th International Conference on Learning Representations | Abbreviated Journal | |
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| Address | Toulon; France; April 2017 | ||||
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| Area | Expedition | Conference | ICLR | ||
| Notes | ISE; 602.143; 600.119; 600.098 | Approved | no | ||
Call Number  |
Admin @ si @ RGC2017 | Serial | 2927 | ||
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| Author | Adria Rico; Alicia Fornes | ||||
| Title | Camera-based Optical Music Recognition using a Convolutional Neural Network | Type | Conference Article | ||
| Year | 2017 | Publication | 12th IAPR International Workshop on Graphics Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 27-28 | ||
| Keywords | optical music recognition; document analysis; convolutional neural network; deep learning | ||||
| Abstract | Optical Music Recognition (OMR) consists in recognizing images of music scores. Contrary to expectation, the current OMR systems usually fail when recognizing images of scores captured by digital cameras and smartphones. In this work, we propose a camera-based OMR system based on Convolutional Neural Networks, showing promising preliminary results | ||||
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| Area | Expedition | Conference | GREC | ||
| Notes | DAG;600.097; 600.121 | Approved | no | ||
| Call Number |
Admin @ si @ RiF2017 | Serial | 3059 | ||
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| Author | Muhammad Anwer Rao; Fahad Shahbaz Khan; Joost Van de Weijer; Jorma Laaksonen | ||||
| Title | Tex-Nets: Binary Patterns Encoded Convolutional Neural Networks for Texture Recognition | Type | Conference Article | ||
| Year | 2017 | Publication | 19th International Conference on Multimodal Interaction | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | Convolutional Neural Networks; Texture Recognition; Local Binary Paterns | ||||
| Abstract | Recognizing materials and textures in realistic imaging conditions is a challenging computer vision problem. For many years, local features based orderless representations were a dominant approach for texture recognition. Recently deep local features, extracted from the intermediate layers of a Convolutional Neural Network (CNN), are used as filter banks. These dense local descriptors from a deep model, when encoded with Fisher Vectors, have shown to provide excellent results for texture recognition. The CNN models, employed in such approaches, take RGB patches as input and train on a large amount of labeled images. We show that CNN models, which we call TEX-Nets, trained using mapped coded images with explicit texture information provide complementary information to the standard deep models trained on RGB patches. We further investigate two deep architectures, namely early and late fusion, to combine the texture and color information. Experiments on benchmark texture datasets clearly demonstrate that TEX-Nets provide complementary information to standard RGB deep network. Our approach provides a large gain of 4.8%, 3.5%, 2.6% and 4.1% respectively in accuracy on the DTD, KTH-TIPS-2a, KTH-TIPS-2b and Texture-10 datasets, compared to the standard RGB network of the same architecture. Further, our final combination leads to consistent improvements over the state-of-the-art on all four datasets. | ||||
| Address | Glasgow; Scothland; November 2017 | ||||
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| Area | Expedition | Conference | ACM | ||
| Notes | LAMP; 600.109; 600.068; 600.120 | Approved | no | ||
| Call Number |
Admin @ si @ RKW2017 | Serial | 3038 | ||
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| Author | Muhammad Anwer Rao; Fahad Shahbaz Khan; Joost Van de Weijer; Jorma Laaksonen | ||||
| Title | Top-Down Deep Appearance Attention for Action Recognition | Type | Conference Article | ||
| Year | 2017 | Publication | 20th Scandinavian Conference on Image Analysis | Abbreviated Journal | |
| Volume | 10269 | Issue | Pages | 297-309 | |
| Keywords | Action recognition; CNNs; Feature fusion | ||||
| Abstract | Recognizing human actions in videos is a challenging problem in computer vision. Recently, convolutional neural network based deep features have shown promising results for action recognition. In this paper, we investigate the problem of fusing deep appearance and motion cues for action recognition. We propose a video representation which combines deep appearance and motion based local convolutional features within the bag-of-deep-features framework. Firstly, dense deep appearance and motion based local convolutional features are extracted from spatial (RGB) and temporal (flow) networks, respectively. Both visual cues are processed in parallel by constructing separate visual vocabularies for appearance and motion. A category-specific appearance map is then learned to modulate the weights of the deep motion features. The proposed representation is discriminative and binds the deep local convolutional features to their spatial locations. Experiments are performed on two challenging datasets: JHMDB dataset with 21 action classes and ACT dataset with 43 categories. The results clearly demonstrate that our approach outperforms both standard approaches of early and late feature fusion. Further, our approach is only employing action labels and without exploiting body part information, but achieves competitive performance compared to the state-of-the-art deep features based approaches. | ||||
| Address | Tromso; June 2017 | ||||
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| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| Area | Expedition | Conference | SCIA | ||
| Notes | LAMP; 600.109; 600.068; 600.120 | Approved | no | ||
| Call Number |
Admin @ si @ RKW2017b | Serial | 3039 | ||
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| Author | Pau Riba; Josep Llados; Alicia Fornes | ||||
| Title | Error-tolerant coarse-to-fine matching model for hierarchical graphs | Type | Conference Article | ||
| Year | 2017 | Publication | 11th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition | Abbreviated Journal | |
| Volume | 10310 | Issue | Pages | 107-117 | |
| Keywords | Graph matching; Hierarchical graph; Graph-based representation; Coarse-to-fine matching | ||||
| Abstract | Graph-based representations are effective tools to capture structural information from visual elements. However, retrieving a query graph from a large database of graphs implies a high computational complexity. Moreover, these representations are very sensitive to noise or small changes. In this work, a novel hierarchical graph representation is designed. Using graph clustering techniques adapted from graph-based social media analysis, we propose to generate a hierarchy able to deal with different levels of abstraction while keeping information about the topology. For the proposed representations, a coarse-to-fine matching method is defined. These approaches are validated using real scenarios such as classification of colour images and handwritten word spotting. | ||||
| Address | Anacapri; Italy; May 2017 | ||||
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| Publisher | Springer International Publishing | Place of Publication | Editor | Pasquale Foggia; Cheng-Lin Liu; Mario Vento | |
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| Area | Expedition | Conference | GbRPR | ||
| Notes | DAG; 600.097; 601.302; 600.121 | Approved | no | ||
| Call Number |
Admin @ si @ RLF2017a | Serial | 2951 | ||
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| Author | Marçal Rusiñol; Josep Llados | ||||
| Title | Flowchart Recognition in Patent Information Retrieval | Type | Book Chapter | ||
| Year | 2017 | Publication | Current Challenges in Patent Information Retrieval | Abbreviated Journal | |
| Volume | 37 | Issue | Pages | 351-368 | |
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| Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | M. Lupu; K. Mayer; N. Kando; A.J. Trippe | |
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| Notes | DAG; 600.097; 600.121 | Approved | no | ||
| Call Number |
Admin @ si @ RuL2017 | Serial | 2896 | ||
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| Author | Ivet Rafegas; Javier Vazquez; Robert Benavente; Maria Vanrell; Susana Alvarez | ||||
| Title | Enhancing spatio-chromatic representation with more-than-three color coding for image description | Type | Journal Article | ||
| Year | 2017 | Publication | Journal of the Optical Society of America A | Abbreviated Journal | JOSA A |
| Volume | 34 | Issue | 5 | Pages | 827-837 |
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| Abstract | Extraction of spatio-chromatic features from color images is usually performed independently on each color channel. Usual 3D color spaces, such as RGB, present a high inter-channel correlation for natural images. This correlation can be reduced using color-opponent representations, but the spatial structure of regions with small color differences is not fully captured in two generic Red-Green and Blue-Yellow channels. To overcome these problems, we propose a new color coding that is adapted to the specific content of each image. Our proposal is based on two steps: (a) setting the number of channels to the number of distinctive colors we find in each image (avoiding the problem of channel correlation), and (b) building a channel representation that maximizes contrast differences within each color channel (avoiding the problem of low local contrast). We call this approach more-than-three color coding (MTT) to enhance the fact that the number of channels is adapted to the image content. The higher color complexity an image has, the more channels can be used to represent it. Here we select distinctive colors as the most predominant in the image, which we call color pivots, and we build the new color coding using these color pivots as a basis. To evaluate the proposed approach we measure its efficiency in an image categorization task. We show how a generic descriptor improves its performance at the description level when applied on the MTT coding. | ||||
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| Notes | CIC; 600.087 | Approved | no | ||
| Call Number |
Admin @ si @ RVB2017 | Serial | 2892 | ||
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| Author | Mireia Sole; Joan Blanco; Debora Gil; Oliver Valero; G. Fonseka; M. Lawrie; Francesca Vidal; Zaida Sarrate | ||||
| Title | Chromosome Territories in Mice Spermatogenesis: A new three-dimensional methodology of study | Type | Conference Article | ||
| Year | 2017 | Publication | 11th European CytoGenesis Conference | Abbreviated Journal | |
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| Address | Florencia; Italia; July 2017 | ||||
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| Area | Expedition | Conference | ECA | ||
| Notes | IAM; 600.096; 600.145 | Approved | no | ||
| Call Number |
Admin @ si @ SBG2017a | Serial | 2936 | ||
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| Author | Mireia Sole; Joan Blanco; Debora Gil; G. Fonseka; Richard Frodsham; Oliver Valero; Francesca Vidal; Zaida Sarrate | ||||
| Title | Unraveling the enigmas of chromosome territoriality during spermatogenesis | Type | Conference Article | ||
| Year | 2017 | Publication | IX Jornada del Departament de Biologia Cel•lular, Fisiologia i Immunologia | Abbreviated Journal | |
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| Address | UAB; Barcelona; June 2017 | ||||
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| Notes | IAM; 600.145 | Approved | no | ||
| Call Number |
Admin @ si @ SBG2017b | Serial | 2959 | ||
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| Author | Mireia Sole; Joan Blanco; Debora Gil; G. Fonseka; Richard Frodsham; Francesca Vidal; Zaida Sarrate | ||||
| Title | Noves perspectives en l estudi de la territorialitat cromosomica de cel·lules germinals masculines: estudis tridimensionals | Type | Journal | ||
| Year | 2017 | Publication | Biologia de la Reproduccio | Abbreviated Journal | JBR |
| Volume | 15 | Issue | Pages | 73-78 | |
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| Abstract | In somatic cells, chromosomes occupy specific nuclear regions called chromosome territories which are involved in the
maintenance and regulation of the genome. Preliminary data in male germ cells also suggest the importance of chromosome territoriality in cell functionality. Nevertheless, the specific characteristics of testicular tissue (presence of different cell types with different morphological characteristics, in different stages of development and with different ploidy) makes difficult to achieve conclusive results. In this study we have developed a methodology to approach the threedimensional study of all chromosome territories in male germ cells from C57BL/6J mice (Mus musculus). The method includes the following steps: i) Optimized cell fixation to obtain an optimal preservation of the three-dimensionality cell morphology, ii) Chromosome identification by FISH (Chromoprobe Multiprobe® OctoChrome™ Murine System; Cytocell) and confocal microscopy (TCS-SP5, Leica Microsystems), iii) Cell type identification by immunofluorescence iv) Image analysis using Matlab scripts, v) Numerical data extraction related to chromosome features, chromosome radial position and chromosome relative position. This methodology allows the unequivocally identification and the analysis of the chromosome territories of all spermatogenic stages. Results will provide information about the features that determine chromosomal position, preferred associations between chromosomes, and the relationship between chromosome positioning and genome regulation. |
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| ISSN | ISBN | 978-84-697-3767-5 | Medium | ||
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| Notes | IAM; 600.096; 600.145 | Approved | no | ||
| Call Number |
Admin @ si @ SBG2017c | Serial | 2961 | ||
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| Author | Mireia Sole; Joan Blanco; Debora Gil; G. Fonseka; Richard Frodsham; Oliver Valero; Francesca Vidal; Zaida Sarrate | ||||
| Title | Análisis 3d de la territorialidad cromosómica en células espermatogénicas: explorando la infertilidad desde un nuevo prisma | Type | Journal | ||
| Year | 2017 | Publication | Revista Asociación para el Estudio de la Biología de la Reproducción | Abbreviated Journal | ASEBIR |
| Volume | 22 | Issue | 2 | Pages | 105 |
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| Notes | IAM; 600.096; 600.145 | Approved | no | ||
| Call Number |
Admin @ si @ SBG2017d | Serial | 3042 | ||
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| Author | F. Javier Sanchez; Jorge Bernal; Cristina Sanchez Montes; Cristina Rodriguez de Miguel; Gloria Fernandez Esparrach | ||||
| Title | Bright spot regions segmentation and classification for specular highlights detection in colonoscopy videos | Type | Journal Article | ||
| Year | 2017 | Publication | Machine Vision and Applications | Abbreviated Journal | MVAP |
| Volume | Issue | Pages | 1-20 | ||
| Keywords | Specular highlights; bright spot regions segmentation; region classification; colonoscopy | ||||
| Abstract | A novel specular highlights detection method in colonoscopy videos is presented. The method is based on a model of appearance de ning specular
highlights as bright spots which are highly contrasted with respect to adjacent regions. Our approach proposes two stages; segmentation, and then classi cation of bright spot regions. The former de nes a set of candidate regions obtained through a region growing process with local maxima as initial region seeds. This process creates a tree structure which keeps track, at each growing iteration, of the region frontier contrast; nal regions provided depend on restrictions over contrast value. Non-specular regions are ltered through a classi cation stage performed by a linear SVM classi er using model-based features from each region. We introduce a new validation database with more than 25; 000 regions along with their corresponding pixel-wise annotations. We perform a comparative study against other approaches. Results show that our method is superior to other approaches, with our segmented regions being closer to actual specular regions in the image. Finally, we also present how our methodology can also be used to obtain an accurate prediction of polyp histology. |
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| Notes | MV; 600.096; 600.175 | Approved | no | ||
| Call Number |
Admin @ si @ SBS2017 | Serial | 2975 | ||
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| Author | Frederic Sampedro; Anna Domenech; Sergio Escalera; Ignasi Carrio | ||||
| Title | Computing quantitative indicators of structural renal damage in pediatric DMSA scans | Type | Journal Article | ||
| Year | 2017 | Publication | Revista Española de Medicina Nuclear e Imagen Molecular | Abbreviated Journal | REMNIM |
| Volume | 36 | Issue | 2 | Pages | 72-77 |
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| Abstract | OBJECTIVES:
The proposal and implementation of a computational framework for the quantification of structural renal damage from 99mTc-dimercaptosuccinic acid (DMSA) scans. The aim of this work is to propose, implement, and validate a computational framework for the quantification of structural renal damage from DMSA scans and in an observer-independent manner. MATERIALS AND METHODS: From a set of 16 pediatric DMSA-positive scans and 16 matched controls and using both expert-guided and automatic approaches, a set of image-derived quantitative indicators was computed based on the relative size, intensity and histogram distribution of the lesion. A correlation analysis was conducted in order to investigate the association of these indicators with other clinical data of interest in this scenario, including C-reactive protein (CRP), white cell count, vesicoureteral reflux, fever, relative perfusion, and the presence of renal sequelae in a 6-month follow-up DMSA scan. RESULTS: A fully automatic lesion detection and segmentation system was able to successfully classify DMSA-positive from negative scans (AUC=0.92, sensitivity=81% and specificity=94%). The image-computed relative size of the lesion correlated with the presence of fever and CRP levels (p<0.05), and a measurement derived from the distribution histogram of the lesion obtained significant performance results in the detection of permanent renal damage (AUC=0.86, sensitivity=100% and specificity=75%). CONCLUSIONS: The proposal and implementation of a computational framework for the quantification of structural renal damage from DMSA scans showed a promising potential to complement visual diagnosis and non-imaging indicators. |
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| Notes | HuPBA;MILAB; no menciona | Approved | no | ||
| Call Number |
Admin @ si @ SDE2017 | Serial | 2842 | ||
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| Author | Carles Sanchez; Antonio Esteban Lansaque; Agnes Borras; Marta Diez-Ferrer; Antoni Rosell; Debora Gil | ||||
| Title | Towards a Videobronchoscopy Localization System from Airway Centre Tracking | Type | Conference Article | ||
| Year | 2017 | Publication | 12th International Conference on Computer Vision Theory and Applications | Abbreviated Journal | |
| Volume | Issue | Pages | 352-359 | ||
| Keywords | Video-bronchoscopy; Lung cancer diagnosis; Airway lumen detection; Region tracking; Guided bronchoscopy navigation | ||||
| Abstract | Bronchoscopists use fluoroscopy to guide flexible bronchoscopy to the lesion to be biopsied without any kind of incision. Being fluoroscopy an imaging technique based on X-rays, the risk of developmental problems and cancer is increased in those subjects exposed to its application, so minimizing radiation is crucial. Alternative guiding systems such as electromagnetic navigation require specific equipment, increase the cost of the clinical procedure and still require fluoroscopy. In this paper we propose an image based guiding system based on the extraction of airway centres from intra-operative videos. Such anatomical landmarks are matched to the airway centreline extracted from a pre-planned CT to indicate the best path to the nodule. We present a
feasibility study of our navigation system using simulated bronchoscopic videos and a multi-expert validation of landmarks extraction in 3 intra-operative ultrathin explorations. |
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| Address | Porto; Portugal; February 2017 | ||||
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| Area | Expedition | Conference | VISAPP | ||
| Notes | IAM; 600.096; 600.075; 600.145 | Approved | no | ||
| Call Number |
Admin @ si @ SEB2017 | Serial | 2943 | ||
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| Author | Cesar de Souza; Adrien Gaidon; Yohann Cabon; Antonio Lopez | ||||
| Title | Procedural Generation of Videos to Train Deep Action Recognition Networks | Type | Conference Article | ||
| Year | 2017 | Publication | 30th IEEE Conference on Computer Vision and Pattern Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 2594-2604 | ||
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| Abstract | Deep learning for human action recognition in videos is making significant progress, but is slowed down by its dependency on expensive manual labeling of large video collections. In this work, we investigate the generation of synthetic training data for action recognition, as it has recently shown promising results for a variety of other computer vision tasks. We propose an interpretable parametric generative model of human action videos that relies on procedural generation and other computer graphics techniques of modern game engines. We generate a diverse, realistic, and physically plausible dataset of human action videos, called PHAV for ”Procedural Human Action Videos”. It contains a total of 39, 982 videos, with more than 1, 000 examples for each action of 35 categories. Our approach is not limited to existing motion capture sequences, and we procedurally define 14 synthetic actions. We introduce a deep multi-task representation learning architecture to mix synthetic and real videos, even if the action categories differ. Our experiments on the UCF101 and HMDB51 benchmarks suggest that combining our large set of synthetic videos with small real-world datasets can boost recognition performance, significantly
outperforming fine-tuning state-of-the-art unsupervised generative models of videos. |
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| Address | Honolulu; Hawaii; July 2017 | ||||
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| Area | Expedition | Conference | CVPR | ||
| Notes | ADAS; 600.076; 600.085; 600.118 | Approved | no | ||
| Call Number |
Admin @ si @ SGC2017 | Serial | 3051 | ||
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