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Author |
Antonio Lopez; Atsushi Imiya; Tomas Pajdla; Jose Manuel Alvarez |
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Title |
Computer Vision in Vehicle Technology: Land, Sea & Air |
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Year |
2017 |
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161-163 |
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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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John Wiley & Sons, Ltd |
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978-1-118-86807-2 |
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ADAS; 600.118 |
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Admin @ si @ LIP2017a |
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2937 |
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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 |
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Title |
Positive Airway Pressure-Enhanced CT to Improve Virtual Bronchoscopic Navigation |
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Journal Article |
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2017 |
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European Respiratory Journal |
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ERJ |
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IAM |
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no |
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Admin @ si @ DGC2017b |
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3632 |
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Quentin Angermann; Jorge Bernal; Cristina Sanchez Montes; Maroua Hammami; Gloria Fernandez Esparrach; Xavier Dray; Olivier Romain; F. Javier Sanchez; Aymeric Histace |
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Title |
Real-Time Polyp Detection in Colonoscopy Videos: A Preliminary Study For Adapting Still Frame-based Methodology To Video Sequences Analysis |
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Conference Article |
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2017 |
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31st International Congress and Exhibition on Computer Assisted Radiology and Surgery |
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Barcelona; Spain; June 2017 |
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CARS |
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MV; no menciona |
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Admin @ si @ ABS2017 |
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2947 |
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Author |
Lasse Martensson; Anders Hast; Alicia Fornes |
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Title |
Word Spotting as a Tool for Scribal Attribution |
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Conference Article |
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2017 |
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2nd Conference of the association of Digital Humanities in the Nordic Countries |
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87-89 |
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Gothenburg; Suecia; March 2017 |
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978-91-88348-83-8 |
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DAG; 600.097; 600.121 |
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no |
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Admin @ si @ MHF2017 |
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2954 |
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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 |
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Title |
Classification of Confocal Endomicroscopy Patterns for Diagnosis of Lung Cancer |
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Conference Article |
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2017 |
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6th Workshop on Clinical Image-based Procedures: Translational Research in Medical Imaging |
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10550 |
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151-159 |
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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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Quebec; Canada; September 2017 |
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CLIP |
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Notes |
IAM; 600.096; 600.075; 600.145 |
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Admin @ si @ GRM2017 |
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2957 |
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Author |
Mireia Sole; Joan Blanco; Debora Gil; G. Fonseka; Richard Frodsham; Oliver Valero; Francesca Vidal; Zaida Sarrate |
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Title |
Is there a pattern of Chromosome territoriality along mice spermatogenesis? |
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Conference Article |
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2017 |
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3rd Spanish MeioNet Meeting Abstract Book |
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55-56 |
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Miraflores de la Sierra; Madrid; June 2017 |
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MEIONET |
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IAM; 600.096; 600.145 |
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Admin @ si @ |
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2958 |
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Author |
Mireia Sole; Joan Blanco; Debora Gil; G. Fonseka; Richard Frodsham; Oliver Valero; Francesca Vidal; Zaida Sarrate |
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Title |
Unraveling the enigmas of chromosome territoriality during spermatogenesis |
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Conference Article |
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2017 |
Publication |
IX Jornada del Departament de Biologia Cel•lular, Fisiologia i Immunologia |
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UAB; Barcelona; June 2017 |
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IAM; 600.145 |
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no |
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Admin @ si @ SBG2017b |
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2959 |
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Author |
Mireia Sole; Joan Blanco; Debora Gil; G. Fonseka; Richard Frodsham; Francesca Vidal; Zaida Sarrate |
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Title |
Noves perspectives en l estudi de la territorialitat cromosomica de cel·lules germinals masculines: estudis tridimensionals |
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2017 |
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Biologia de la Reproduccio |
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JBR |
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15 |
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73-78 |
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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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978-84-697-3767-5 |
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IAM; 600.096; 600.145 |
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no |
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Admin @ si @ SBG2017c |
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2961 |
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Author |
Xinhang Song; Shuqiang Jiang; Luis Herranz |
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Title |
Multi-Scale Multi-Feature Context Modeling for Scene Recognition in the Semantic Manifold |
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Journal Article |
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2017 |
Publication |
IEEE Transactions on Image Processing |
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TIP |
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26 |
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6 |
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2721-2735 |
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Abstract |
Before the big data era, scene recognition was often approached with two-step inference using localized intermediate representations (objects, topics, and so on). One of such approaches is the semantic manifold (SM), in which patches and images are modeled as points in a semantic probability simplex. Patch models are learned resorting to weak supervision via image labels, which leads to the problem of scene categories co-occurring in this semantic space. Fortunately, each category has its own co-occurrence patterns that are consistent across the images in that category. Thus, discovering and modeling these patterns are critical to improve the recognition performance in this representation. Since the emergence of large data sets, such as ImageNet and Places, these approaches have been relegated in favor of the much more powerful convolutional neural networks (CNNs), which can automatically learn multi-layered representations from the data. In this paper, we address many limitations of the original SM approach and related works. We propose discriminative patch representations using neural networks and further propose a hybrid architecture in which the semantic manifold is built on top of multiscale CNNs. Both representations can be computed significantly faster than the Gaussian mixture models of the original SM. To combine multiple scales, spatial relations, and multiple features, we formulate rich context models using Markov random fields. To solve the optimization problem, we analyze global and local approaches, where a top-down hierarchical algorithm has the best performance. Experimental results show that exploiting different types of contextual relations jointly consistently improves the recognition accuracy. |
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LAMP; 600.120 |
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Admin @ si @ SJH2017a |
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2963 |
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Author |
Weiqing Min; Shuqiang Jiang; Jitao Sang; Huayang Wang; Xinda Liu; Luis Herranz |
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Title |
Being a Supercook: Joint Food Attributes and Multimodal Content Modeling for Recipe Retrieval and Exploration |
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2017 |
Publication |
IEEE Transactions on Multimedia |
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TMM |
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19 |
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5 |
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1100 - 1113 |
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This paper considers the problem of recipe-oriented image-ingredient correlation learning with multi-attributes for recipe retrieval and exploration. Existing methods mainly focus on food visual information for recognition while we model visual information, textual content (e.g., ingredients), and attributes (e.g., cuisine and course) together to solve extended recipe-oriented problems, such as multimodal cuisine classification and attribute-enhanced food image retrieval. As a solution, we propose a multimodal multitask deep belief network (M3TDBN) to learn joint image-ingredient representation regularized by different attributes. By grouping ingredients into visible ingredients (which are visible in the food image, e.g., “chicken” and “mushroom”) and nonvisible ingredients (e.g., “salt” and “oil”), M3TDBN is capable of learning both midlevel visual representation between images and visible ingredients and nonvisual representation. Furthermore, in order to utilize different attributes to improve the intermodality correlation, M3TDBN incorporates multitask learning to make different attributes collaborate each other. Based on the proposed M3TDBN, we exploit the derived deep features and the discovered correlations for three extended novel applications: 1) multimodal cuisine classification; 2) attribute-augmented cross-modal recipe image retrieval; and 3) ingredient and attribute inference from food images. The proposed approach is evaluated on the constructed Yummly dataset and the evaluation results have validated the effectiveness of the proposed approach. |
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LAMP; 600.120 |
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Admin @ si @ MJS2017 |
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2964 |
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Author |
Luis Herranz; Shuqiang Jiang; Ruihan Xu |
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Title |
Modeling Restaurant Context for Food Recognition |
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Journal Article |
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2017 |
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IEEE Transactions on Multimedia |
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TMM |
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19 |
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2 |
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430 - 440 |
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Food photos are widely used in food logs for diet monitoring and in social networks to share social and gastronomic experiences. A large number of these images are taken in restaurants. Dish recognition in general is very challenging, due to different cuisines, cooking styles, and the intrinsic difficulty of modeling food from its visual appearance. However, contextual knowledge can be crucial to improve recognition in such scenario. In particular, geocontext has been widely exploited for outdoor landmark recognition. Similarly, we exploit knowledge about menus and location of restaurants and test images. We first adapt a framework based on discarding unlikely categories located far from the test image. Then, we reformulate the problem using a probabilistic model connecting dishes, restaurants, and locations. We apply that model in three different tasks: dish recognition, restaurant recognition, and location refinement. Experiments on six datasets show that by integrating multiple evidences (visual, location, and external knowledge) our system can boost the performance in all tasks. |
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LAMP; 600.120 |
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Admin @ si @ HJX2017 |
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2965 |
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Author |
Simone Balocco; Francesco Ciompi; Juan Rigla; Xavier Carrillo; J. Mauri; Petia Radeva |
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Title |
Intra-Coronary Stent localization In Intravascular Ultrasound Sequences, A Preliminary Study |
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2017 |
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International workshop on Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting (CVII-STENT) |
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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 identication 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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Quebec; Canada; September 2017 |
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MICCAIW |
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MILAB; no proj |
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no |
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Admin @ si @ BCR2017 |
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2968 |
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Author |
Aitor Alvarez-Gila; Joost Van de Weijer; Estibaliz Garrote |
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Title |
Adversarial Networks for Spatial Context-Aware Spectral Image Reconstruction from RGB |
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2017 |
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1st International Workshop on Physics Based Vision meets Deep Learning |
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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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Venice; Italy; October 2017 |
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ICCV-PBDL |
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LAMP; 600.109; 600.106; 600.120 |
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no |
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Admin @ si @ AWG2017 |
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2969 |
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Meysam Madadi; Sergio Escalera; Alex Carruesco; Carlos Andujar; Xavier Baro; Jordi Gonzalez |
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Occlusion Aware Hand Pose Recovery from Sequences of Depth Images |
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2017 |
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12th IEEE International Conference on Automatic Face and Gesture Recognition |
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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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HUPBA; ISE; 602.143; 600.098; 600.119 |
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Admin @ si @ MEC2017 |
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2970 |
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Laura Lopez-Fuentes; Sebastia Massanet; Manuel Gonzalez-Hidalgo |
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Image vignetting reduction via a maximization of fuzzy entropy |
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Conference Article |
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2017 |
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IEEE International Conference on Fuzzy Systems |
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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. |
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Napoles; Italia; July 2017 |
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FUZZ-IEEE |
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LAMP; 600.120 |
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Admin @ si @ LMG2017 |
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2972 |
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