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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  |
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| Volume | 9657 | Issue | Pages | 103-116 | |
| Keywords | Optical Music Recognition; Handwritten Music Scores; Dynamic Time Warping alignment | ||||
| Abstract | It is very common to nd di erent versions of the same music work in archives of Opera Theaters. These di erences correspond to modi cations 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 di erences. 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 | Thanh Ha Do; Salvatore Tabbone; Oriol Ramos Terrades | ||||
| Title | Spotting Symbol over Graphical Documents Via Sparsity in Visual Vocabulary | Type | Book Chapter | ||
| Year | 2016 | Publication | Recent Trends in Image Processing and Pattern Recognition | Abbreviated Journal |
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| Volume | 709 | Issue | Pages | ||
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| Area | Expedition | Conference | RTIP2R | ||
| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ HTR2016 | Serial | 2956 | ||
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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 |
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| 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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| Author | Mireia Sole; Joan Blanco; Debora Gil; G. Fonseka; Richard Frodsham; Oliver Valero; Francesca Vidal; Zaida Sarrate | ||||
| Title | Is there a pattern of Chromosome territoriality along mice spermatogenesis? | Type | Conference Article | ||
| Year | 2017 | Publication | 3rd Spanish MeioNet Meeting Abstract Book | Abbreviated Journal |
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| Volume | Issue | Pages | 55-56 | ||
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| Address | Miraflores de la Sierra; Madrid; June 2017 | ||||
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| Area | Expedition | Conference | MEIONET | ||
| Notes | IAM; 600.096; 600.145 | Approved | no | ||
| Call Number | Admin @ si @ | Serial | 2958 | ||
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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 | Xinhang Song; Shuqiang Jiang; Luis Herranz | ||||
| Title | Combining Models from Multiple Sources for RGB-D Scene Recognition | Type | Conference Article | ||
| Year | 2017 | Publication | 26th International Joint Conference on Artificial Intelligence | Abbreviated Journal |
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| Volume | Issue | Pages | 4523-4529 | ||
| Keywords | Robotics and Vision; Vision and Perception | ||||
| Abstract | Depth can complement RGB with useful cues about object volumes and scene layout. However, RGB-D image datasets are still too small for directly training deep convolutional neural networks (CNNs), in contrast to the massive monomodal RGB datasets. Previous works in RGB-D recognition typically combine two separate networks for RGB and depth data, pretrained with a large RGB dataset and then fine tuned to the respective target RGB and depth datasets. These approaches have several limitations: 1) only use low-level filters learned from RGB data, thus not being able to exploit properly depth-specific patterns, and 2) RGB and depth features are only combined at high-levels but rarely at lower-levels. In this paper, we propose a framework that leverages both knowledge acquired from large RGB datasets together with depth-specific cues learned from the limited depth data, obtaining more effective multi-source and multi-modal representations. We propose a multi-modal combination method that selects discriminative combinations of layers from the different source models and target modalities, capturing both high-level properties of the task and intrinsic low-level properties of both modalities. | ||||
| Address | Melbourne; Australia; August 2017 | ||||
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| Area | Expedition | Conference | IJCAI | ||
| Notes | LAMP; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ SJH2017b | Serial | 2966 | ||
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| Author | Xinhang Song; Luis Herranz; Shuqiang Jiang | ||||
| Title | Depth CNNs for RGB-D Scene Recognition: Learning from Scratch Better than Transferring from RGB-CNNs | Type | Conference Article | ||
| Year | 2017 | Publication | 31st AAAI Conference on Artificial Intelligence | Abbreviated Journal |
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| Volume | Issue | Pages | |||
| Keywords | RGB-D scene recognition; weakly supervised; fine tune; CNN | ||||
| Abstract | Scene recognition with RGB images has been extensively studied and has reached very remarkable recognition levels, thanks to convolutional neural networks (CNN) and large scene datasets. In contrast, current RGB-D scene data is much more limited, so often leverages RGB large datasets, by transferring pretrained RGB CNN models and fine-tuning with the target RGB-D dataset. However, we show that this approach has the limitation of hardly reaching bottom layers, which is key to learn modality-specific features. In contrast, we focus on the bottom layers, and propose an alternative strategy to learn depth features combining local weakly supervised training from patches followed by global fine tuning with images. This strategy is capable of learning very discriminative depth-specific features with limited depth images, without resorting to Places-CNN. In addition we propose a modified CNN architecture to further match the complexity of the model and the amount of data available. For RGB-D scene recognition, depth and RGB features are combined by projecting them in a common space and further leaning a multilayer classifier, which is jointly optimized in an end-to-end network. Our framework achieves state-of-the-art accuracy on NYU2 and SUN RGB-D in both depth only and combined RGB-D data. | ||||
| Address | San Francisco CA; February 2017 | ||||
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| Area | Expedition | Conference | AAAI | ||
| Notes | LAMP; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ SHJ2017 | Serial | 2967 | ||
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| Author | Simone Balocco; Francesco Ciompi; Juan Rigla; Xavier Carrillo; Josefina Mauri; Petia Radeva | ||||
| Title | Intra-Coronary Stent localization In Intravascular Ultrasound Sequences, A Preliminary Study | Type | Conference Article | ||
| Year | 2017 | Publication | International workshop on Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting (CVII-STENT) | Abbreviated Journal |
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| Abstract | An intraluminal coronary stent is a metal scaold deployed in a stenotic artery during Percutaneous Coronary Intervention (PCI).
Intravascular Ultrasound (IVUS) is a catheter-based imaging technique generally used for assessing the correct placement of the stent. All the approaches proposed so far for the stent analysis only focused on the struts detection, while this paper proposes a novel approach to detect the boundaries and the position of the stent along the pullback. The pipeline of the method requires the identi cation of the stable frames of the sequence and the reliable detection of stent struts. Using this data, a measure of likelihood for a frame to contain a stent is computed. Then, a robust binary representation of the presence of the stent in the pullback is obtained applying an iterative and multi-scale approximation of the signal to symbols using the SAX algorithm. Results obtained comparing the automatic results versus the manual annotation of two observers on 80 IVUS in-vivo sequences shows that the method approaches the inter-observer variability scores. |
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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 | MICCAIW | ||
| Notes | MILAB; no proj | Approved | no | ||
| Call Number | Admin @ si @ BCR2017 | Serial | 2968 | ||
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| Author | Aitor Alvarez-Gila; Joost Van de Weijer; Estibaliz Garrote | ||||
| Title | Adversarial Networks for Spatial Context-Aware Spectral Image Reconstruction from RGB | Type | Conference Article | ||
| Year | 2017 | Publication | 1st International Workshop on Physics Based Vision meets Deep Learning | Abbreviated Journal |
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| Abstract | Hyperspectral signal reconstruction aims at recovering the original spectral input that produced a certain trichromatic (RGB) response from a capturing device or observer.
Given the heavily underconstrained, non-linear nature of the problem, traditional techniques leverage different statistical properties of the spectral signal in order to build informative priors from real world object reflectances for constructing such RGB to spectral signal mapping. However, most of them treat each sample independently, and thus do not benefit from the contextual information that the spatial dimensions can provide. We pose hyperspectral natural image reconstruction as an image to image mapping learning problem, and apply a conditional generative adversarial framework to help capture spatial semantics. This is the first time Convolutional Neural Networks -and, particularly, Generative Adversarial Networks- are used to solve this task. Quantitative evaluation shows a Root Mean Squared Error (RMSE) drop of 44:7% and a Relative RMSE drop of 47:0% on the ICVL natural hyperspectral image dataset. |
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| Address | Venice; Italy; October 2017 | ||||
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| Area | Expedition | Conference | ICCV-PBDL | ||
| Notes | LAMP; 600.109; 600.106; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ AWG2017 | Serial | 2969 | ||
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| Author | Meysam Madadi; Sergio Escalera; Alex Carruesco; Carlos Andujar; Xavier Baro; Jordi Gonzalez | ||||
| Title | Occlusion Aware Hand Pose Recovery from Sequences of Depth Images | Type | Conference Article | ||
| Year | 2017 | Publication | 12th IEEE International Conference on Automatic Face and Gesture Recognition | Abbreviated Journal |
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| Abstract | State-of-the-art approaches on hand pose estimation from depth images have reported promising results under quite controlled considerations. In this paper we propose a two-step pipeline for recovering the hand pose from a sequence of depth images. The pipeline has been designed to deal with images taken from any viewpoint and exhibiting a high degree of finger occlusion. In a first step we initialize the hand pose using a part-based model, fitting a set of hand components in the depth images. In a second step we consider temporal data and estimate the parameters of a trained bilinear model consisting of shape and trajectory bases. Results on a synthetic, highly-occluded dataset demonstrate that the proposed method outperforms most recent pose recovering approaches, including those based on CNNs. | ||||
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| Area | Expedition | Conference | FG | ||
| Notes | HUPBA; ISE; 602.143; 600.098; 600.119 | Approved | no | ||
| Call Number | Admin @ si @ MEC2017 | Serial | 2970 | ||
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| Author | Laura Lopez-Fuentes; Sebastia Massanet; Manuel Gonzalez-Hidalgo | ||||
| Title | Image vignetting reduction via a maximization of fuzzy entropy | Type | Conference Article | ||
| Year | 2017 | Publication | IEEE International Conference on Fuzzy Systems | Abbreviated Journal |
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| Abstract | In many computer vision applications, vignetting is an undesirable effect which must be removed in a pre-processing step. Recently, an algorithm for image vignetting correction has been presented by means of a minimization of log-intensity entropy. This method relies on an increase of the entropy of the image when it is affected with vignetting. In this paper, we propose a novel algorithm to reduce image vignetting via a maximization of the fuzzy entropy of the image. Fuzzy entropy quantifies the fuzziness degree of a fuzzy set and its value is also modified by the presence of vignetting. The experimental results show that this novel algorithm outperforms in most cases the algorithm based on the minimization of log-intensity entropy both from the qualitative and the quantitative point of view. | ||||
| Address | Napoles; Italia; July 2017 | ||||
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| Area | Expedition | Conference | FUZZ-IEEE | ||
| Notes | LAMP; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ LMG2017 | Serial | 2972 | ||
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| Author | Laura Lopez-Fuentes; Andrew Bagdanov; Joost Van de Weijer; Harald Skinnemoen | ||||
| Title | Bandwidth Limited Object Recognition in High Resolution Imagery | Type | Conference Article | ||
| Year | 2017 | Publication | IEEE Winter conference on Applications of Computer Vision | Abbreviated Journal |
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| Abstract | This paper proposes a novel method to optimize bandwidth usage for object detection in critical communication scenarios. We develop two operating models of active information seeking. The first model identifies promising regions in low resolution imagery and progressively requests higher resolution regions on which to perform recognition of higher semantic quality. The second model identifies promising regions in low resolution imagery while simultaneously predicting the approximate location of the object of higher semantic quality. From this general framework, we develop a car recognition system via identification of its license plate and evaluate the performance of both models on a car dataset that we introduce. Results are compared with traditional JPEG compression and demonstrate that our system saves up to one order of magnitude of bandwidth while sacrificing little in terms of recognition performance. | ||||
| Address | Santa Rosa; CA; USA; March 2017 | ||||
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| Area | Expedition | Conference | WACV | ||
| Notes | LAMP; 600.068; 600.109; 600.084; 600.106; 600.079; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ LBW2017 | Serial | 2973 | ||
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| Author | Laura Lopez-Fuentes; Joost Van de Weijer; Marc Bolaños; Harald Skinnemoen | ||||
| Title | Multi-modal Deep Learning Approach for Flood Detection | Type | Conference Article | ||
| Year | 2017 | Publication | MediaEval Benchmarking Initiative for Multimedia Evaluation | Abbreviated Journal |
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| Abstract | In this paper we propose a multi-modal deep learning approach to detect floods in social media posts. Social media posts normally contain some metadata and/or visual information, therefore in order to detect the floods we use this information. The model is based on a Convolutional Neural Network which extracts the visual features and a bidirectional Long Short-Term Memory network to extract the semantic features from the textual metadata. We validate the
method on images extracted from Flickr which contain both visual information and metadata and compare the results when using both, visual information only or metadata only. This work has been done in the context of the MediaEval Multimedia Satellite Task. |
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| Address | Dublin; Ireland; September 2017 | ||||
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| Area | Expedition | Conference | MediaEval | ||
| Notes | LAMP; 600.084; 600.109; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ LWB2017a | Serial | 2974 | ||
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| Author | Quentin Angermann; Jorge Bernal; Cristina Sanchez Montes; Gloria Fernandez Esparrach; Xavier Gray; Olivier Romain; F. Javier Sanchez; Aymeric Histace | ||||
| Title | Towards Real-Time Polyp Detection in Colonoscopy Videos: Adapting Still Frame-Based Methodologies for Video Sequences Analysis | Type | Conference Article | ||
| Year | 2017 | Publication | 4th International Workshop on Computer Assisted and Robotic Endoscopy | Abbreviated Journal |
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| Volume | Issue | Pages | 29-41 | ||
| Keywords | Polyp detection; colonoscopy; real time; spatio temporal coherence | ||||
| Abstract | Colorectal cancer is the second cause of cancer death in United States: precursor lesions (polyps) detection is key for patient survival. Though colonoscopy is the gold standard screening tool, some polyps are still missed. Several computational systems have been proposed but none of them are used in the clinical room mainly due to computational constraints. Besides, most of them are built over still frame databases, decreasing their performance on video analysis due to the lack of output stability and not coping with associated variability on image quality and polyp appearance. We propose a strategy to adapt these methods to video analysis by adding a spatio-temporal stability module and studying a combination of features to capture polyp appearance variability. We validate our strategy, incorporated on a real-time detection method, on a public video database. Resulting method detects all
polyps under real time constraints, increasing its performance due to our adaptation strategy. |
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| Address | Quebec; Canada; September 2017 | ||||
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| Area | Expedition | Conference | CARE | ||
| Notes | MV; 600.096; 600.075 | Approved | no | ||
| Call Number | Admin @ si @ ABS2017b | Serial | 2977 | ||
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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 | Clinical Usability Quantification Of a Real-Time Polyp Detection Method In Videocolonoscopy | Type | Conference Article | ||
| Year | 2017 | Publication | 25th United European Gastroenterology Week | Abbreviated Journal |
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| Address | Barcelona, October 2017 | ||||
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| Area | Expedition | Conference | ESGE | ||
| Notes | MV; no menciona | Approved | no | ||
| Call Number | Admin @ si @ ABS2017c | Serial | 2978 | ||
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