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Author | Hana Jarraya; Muhammad Muzzamil Luqman; Jean-Yves Ramel | ||||
Title | Improving Fuzzy Multilevel Graph Embedding Technique by Employing Topological Node Features: An Application to Graphics Recognition | Type | Book Chapter | ||
Year | 2017 | Publication | Graphics Recognition. Current Trends and Challenges | Abbreviated Journal | |
Volume | 9657 | Issue | Pages | ||
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Publisher | Springer | Place of Publication | Editor | B. Lamiroy; R Dueire Lins | |
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Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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Area | Expedition | Conference | GREC | ||
Notes | DAG; 600.097; 600.121 | Approved | no | ||
Call Number | Admin @ si @ JLR2017 | Serial | 2928 | ||
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Author | Umut Guclu; Yagmur Gucluturk; Meysam Madadi; Sergio Escalera; Xavier Baro; Jordi Gonzalez; Rob van Lier; Marcel A. J. van Gerven | ||||
Title | End-to-end semantic face segmentation with conditional random fields as convolutional, recurrent and adversarial networks | Type | Miscellaneous | ||
Year | 2017 | Publication | Arxiv | Abbreviated Journal | |
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Abstract | arXiv:1703.03305
Recent years have seen a sharp increase in the number of related yet distinct advances in semantic segmentation. Here, we tackle this problem by leveraging the respective strengths of these advances. That is, we formulate a conditional random field over a four-connected graph as end-to-end trainable convolutional and recurrent networks, and estimate them via an adversarial process. Importantly, our model learns not only unary potentials but also pairwise potentials, while aggregating multi-scale contexts and controlling higher-order inconsistencies. We evaluate our model on two standard benchmark datasets for semantic face segmentation, achieving state-of-the-art results on both of them. |
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Notes | HuPBA; ISE; 600.098; 600.119 | Approved | no | ||
Call Number | Admin @ si @ GGM2017 | Serial | 2932 | ||
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Author | H. Martin Kjer; Jens Fagertun; Sergio Vera; Debora Gil | ||||
Title | Medial structure generation for registration of anatomical structures | Type | Book Chapter | ||
Year | 2017 | Publication | Skeletonization, Theory, Methods and Applications | Abbreviated Journal | |
Volume | 11 | Issue | Pages | ||
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Notes | IAM; 600.096; 600.075; 600.145 | Approved | no | ||
Call Number | Admin @ si @ MFV2017a | Serial | 2935 | ||
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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 | Antonio Lopez; Atsushi Imiya; Tomas Pajdla; Jose Manuel Alvarez | ||||
Title | Computer Vision in Vehicle Technology: Land, Sea & Air | Type | Book Whole | ||
Year | 2017 | Publication | Abbreviated Journal | ||
Volume | Issue | Pages | 161-163 | ||
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Abstract | Summary This chapter examines different vision-based commercial solutions for real-live problems related to vehicles. It is worth mentioning the recent astonishing performance of deep convolutional neural networks (DCNNs) in difficult visual tasks such as image classification, object recognition/localization/detection, and semantic segmentation. In fact,
different DCNN architectures are already being explored for low-level tasks such as optical flow and disparity computation, and higher level ones such as place recognition. |
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Publisher | John Wiley & Sons, Ltd | Place of Publication | Editor | ||
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ISSN | ISBN | 978-1-118-86807-2 | Medium | ||
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Notes | ADAS; 600.118 | Approved | no | ||
Call Number | Admin @ si @ LIP2017a | Serial | 2937 | ||
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Author | Marc Bolaños; Alvaro Peris; Francisco Casacuberta; Petia Radeva | ||||
Title | VIBIKNet: Visual Bidirectional Kernelized Network for Visual Question Answering | Type | Conference Article | ||
Year | 2017 | Publication | 8th Iberian Conference on Pattern Recognition and Image Analysis | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | Visual Qestion Aswering; Convolutional Neural Networks; Long short-term memory networks | ||||
Abstract | In this paper, we address the problem of visual question answering by proposing a novel model, called VIBIKNet. Our model is based on integrating Kernelized Convolutional Neural Networks and Long-Short Term Memory units to generate an answer given a question about an image. We prove that VIBIKNet is an optimal trade-off between accuracy and computational load, in terms of memory and time consumption. We validate our method on the VQA challenge dataset and compare it to the top performing methods in order to illustrate its performance and speed. | ||||
Address | Faro; Portugal; June 2017 | ||||
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Area | Expedition | Conference | IbPRIA | ||
Notes | MILAB; no proj | Approved | no | ||
Call Number | Admin @ si @ BPC2017 | Serial | 2939 | ||
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Author | David Vazquez; Jorge Bernal; F. Javier Sanchez; Gloria Fernandez Esparrach; Antonio Lopez; Adriana Romero; Michal Drozdzal; Aaron Courville | ||||
Title | A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images | Type | Journal Article | ||
Year | 2017 | Publication | Journal of Healthcare Engineering | Abbreviated Journal | JHCE |
Volume | Issue | Pages | 2040-2295 | ||
Keywords | Colonoscopy images; Deep Learning; Semantic Segmentation | ||||
Abstract | Colorectal cancer (CRC) is the third cause of cancer death world-wide. Currently, the standard approach to reduce CRC-related mortality is to perform regular screening in search for polyps and colonoscopy is the screening tool of choice. The main limitations of this screening procedure are polyp miss- rate and inability to perform visual assessment of polyp malignancy. These drawbacks can be reduced by designing Decision Support Systems (DSS) aim- ing to help clinicians in the different stages of the procedure by providing endoluminal scene segmentation. Thus, in this paper, we introduce an extended benchmark of colonoscopy image segmentation, with the hope of establishing a new strong benchmark for colonoscopy image analysis research. The proposed dataset consists of 4 relevant classes to inspect the endolumninal scene, tar- geting different clinical needs. Together with the dataset and taking advantage of advances in semantic segmentation literature, we provide new baselines by training standard fully convolutional networks (FCN). We perform a compar- ative study to show that FCN significantly outperform, without any further post-processing, prior results in endoluminal scene segmentation, especially with respect to polyp segmentation and localization. | ||||
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Notes | ADAS; MV; 600.075; 600.085; 600.076; 601.281; 600.118 | Approved | no | ||
Call Number | VBS2017b | Serial | 2940 | ||
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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 | Marta Diez-Ferrer; Debora Gil; Elena Carreño; Susana Padrones; Samantha Aso; Vanesa Vicens; Noelia Cubero de Frutos; Rosa Lopez Lisbona; Carles Sanchez; Agnes Borras; Antoni Rosell | ||||
Title | Positive Airway Pressure-Enhanced CT to Improve Virtual Bronchoscopic Navigation | Type | Journal Article | ||
Year | 2017 | Publication | European Respiratory Journal | Abbreviated Journal | ERJ |
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Notes | IAM | Approved | no | ||
Call Number | Admin @ si @ DGC2017b | Serial | 3632 | ||
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Author | Quentin Angermann; Jorge Bernal; Cristina Sanchez Montes; Maroua Hammami; Gloria Fernandez Esparrach; Xavier Dray; Olivier Romain; F. Javier Sanchez; Aymeric Histace | ||||
Title | Real-Time Polyp Detection in Colonoscopy Videos: A Preliminary Study For Adapting Still Frame-based Methodology To Video Sequences Analysis | Type | Conference Article | ||
Year | 2017 | Publication | 31st International Congress and Exhibition on Computer Assisted Radiology and Surgery | Abbreviated Journal | |
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Address | Barcelona; Spain; June 2017 | ||||
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Area | Expedition | Conference | CARS | ||
Notes | MV; no menciona | Approved | no | ||
Call Number | Admin @ si @ ABS2017 | Serial | 2947 | ||
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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 | Lasse Martensson; Anders Hast; Alicia Fornes | ||||
Title | Word Spotting as a Tool for Scribal Attribution | Type | Conference Article | ||
Year | 2017 | Publication | 2nd Conference of the association of Digital Humanities in the Nordic Countries | Abbreviated Journal | |
Volume | Issue | Pages | 87-89 | ||
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Address | Gothenburg; Suecia; March 2017 | ||||
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ISSN | ISBN | 978-91-88348-83-8 | Medium | ||
Area | Expedition | Conference | DHN | ||
Notes | DAG; 600.097; 600.121 | Approved | no | ||
Call Number | Admin @ si @ MHF2017 | Serial | 2954 | ||
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Author | Pau Riba; Alicia Fornes; Josep Llados | ||||
Title | Towards the Alignment of Handwritten Music Scores | Type | Book Chapter | ||
Year | 2017 | Publication | International Workshop on Graphics Recognition. GREC 2015.Graphic Recognition. Current Trends and Challenges | Abbreviated Journal | |
Volume | 9657 | Issue | Pages | 103-116 | |
Keywords | Optical Music Recognition; Handwritten Music Scores; Dynamic Time Warping alignment | ||||
Abstract | It is very common to nd dierent versions of the same music work in archives of Opera Theaters. These dierences correspond to modications and annotations from the musicians. From the musicologist point of view, these variations are very interesting and deserve study.
This paper explores the alignment of music scores as a tool for automatically detecting the passages that contain such dierences. Given the diculties in the recognition of handwritten music scores, our goal is to align the music scores and at the same time, avoid the recognition of music elements as much as possible. After removing the sta lines, braces and ties, the bar lines are detected. Then, the bar units are described as a whole using the Blurred Shape Model. The bar units alignment is performed by using Dynamic Time Warping. The analysis of the alignment path is used to detect the variations in the music scores. The method has been evaluated on a subset of the CVC-MUSCIMA dataset, showing encouraging results. |
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Publisher | Place of Publication | Editor | Bart Lamiroy; R Dueire Lins | ||
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Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | 978-3-319-52158-9 | Medium | ||
Area | Expedition | Conference | |||
Notes | DAG; 600.097; 602.006; 600.121 | Approved | no | ||
Call Number | Admin @ si @ RFL2017 | Serial | 2955 | ||
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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 | Debora Gil; Oriol Ramos Terrades; Elisa Minchole; Carles Sanchez; Noelia Cubero de Frutos; Marta Diez-Ferrer; Rosa Maria Ortiz; Antoni Rosell | ||||
Title | Classification of Confocal Endomicroscopy Patterns for Diagnosis of Lung Cancer | Type | Conference Article | ||
Year | 2017 | Publication | 6th Workshop on Clinical Image-based Procedures: Translational Research in Medical Imaging | Abbreviated Journal | |
Volume | 10550 | Issue | Pages | 151-159 | |
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Abstract | Confocal Laser Endomicroscopy (CLE) is an emerging imaging technique that allows the in-vivo acquisition of cell patterns of potentially malignant lesions. Such patterns could discriminate between inflammatory and neoplastic lesions and, thus, serve as a first in-vivo biopsy to discard cases that do not actually require a cell biopsy.
The goal of this work is to explore whether CLE images obtained during videobronchoscopy contain enough visual information to discriminate between benign and malign peripheral lesions for lung cancer diagnosis. To do so, we have performed a pilot comparative study with 12 patients (6 adenocarcinoma and 6 benign-inflammatory) using 2 different methods for CLE pattern analysis: visual analysis by 3 experts and a novel methodology that uses graph methods to find patterns in pre-trained feature spaces. Our preliminary results indicate that although visual analysis can only achieve a 60.2% of accuracy, the accuracy of the proposed unsupervised image pattern classification raises to 84.6%. We conclude that CLE images visual information allow in-vivo detection of neoplastic lesions and graph structural analysis applied to deep-learning feature spaces can achieve competitive results. |
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Address | Quebec; Canada; September 2017 | ||||
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Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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Area | Expedition | Conference | CLIP | ||
Notes | IAM; 600.096; 600.075; 600.145 | Approved | no | ||
Call Number | Admin @ si @ GRM2017 | Serial | 2957 | ||
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