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Author Akhil Gurram; Onay Urfalioglu; Ibrahim Halfaoui; Fahd Bouzaraa; Antonio Lopez
Title Monocular Depth Estimation by Learning from Heterogeneous Datasets Type Conference Article
Year 2018 Publication IEEE Intelligent Vehicles Symposium Abbreviated Journal
Volume Issue Pages 2176 - 2181
Keywords
Abstract Depth estimation provides essential information to perform autonomous driving and driver assistance. Especially, Monocular Depth Estimation is interesting from a practical point of view, since using a single camera is cheaper than many other options and avoids the need for continuous calibration strategies as required by stereo-vision approaches. State-of-the-art methods for Monocular Depth Estimation are based on Convolutional Neural Networks (CNNs). A promising line of work consists of introducing additional semantic information about the traffic scene when training CNNs for depth estimation. In practice, this means that the depth data used for CNN training is complemented with images having pixel-wise semantic labels, which usually are difficult to annotate (eg crowded urban images). Moreover, so far it is common practice to assume that the same raw training data is associated with both types of ground truth, ie, depth and semantic labels. The main contribution of this paper is to show that this hard constraint can be circumvented, ie, that we can train CNNs for depth estimation by leveraging the depth and semantic information coming from heterogeneous datasets. In order to illustrate the benefits of our approach, we combine KITTI depth and Cityscapes semantic segmentation datasets, outperforming state-of-the-art results on Monocular Depth Estimation.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference (up) IV
Notes ADAS; 600.124; 600.116; 600.118 Approved no
Call Number Admin @ si @ GUH2018 Serial 3183
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Author Simone Balocco; Mauricio Gonzalez; Ricardo Ñancule; Petia Radeva; Gabriel Thomas
Title Calcified Plaque Detection in IVUS Sequences: Preliminary Results Using Convolutional Nets Type Conference Article
Year 2018 Publication International Workshop on Artificial Intelligence and Pattern Recognition Abbreviated Journal
Volume 11047 Issue Pages 34-42
Keywords Intravascular ultrasound images; Convolutional nets; Deep learning; Medical image analysis
Abstract The manual inspection of intravascular ultrasound (IVUS) images to detect clinically relevant patterns is a difficult and laborious task performed routinely by physicians. In this paper, we present a framework based on convolutional nets for the quick selection of IVUS frames containing arterial calcification, a pattern whose detection plays a vital role in the diagnosis of atherosclerosis. Preliminary experiments on a dataset acquired from eighty patients show that convolutional architectures improve detections of a shallow classifier in terms of 𝐹1-measure, precision and recall.
Address Cuba; September 2018
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference (up) IWAIPR
Notes MILAB; no menciona Approved no
Call Number Admin @ si @ BGÑ2018 Serial 3237
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Author Lluis Gomez; Marçal Rusiñol; Ali Furkan Biten; Dimosthenis Karatzas
Title Subtitulació automàtica d'imatges. Estat de l'art i limitacions en el context arxivístic Type Conference Article
Year 2018 Publication Jornades Imatge i Recerca Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference (up) JIR
Notes DAG; 600.084; 600.135; 601.338; 600.121; 600.129 Approved no
Call Number Admin @ si @ GRB2018 Serial 3173
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Author Laura Lopez-Fuentes; Alessandro Farasin; Harald Skinnemoen; Paolo Garza
Title Deep Learning models for passability detection of flooded roads Type Conference Article
Year 2018 Publication MediaEval 2018 Multimedia Benchmark Workshop Abbreviated Journal
Volume 2283 Issue Pages
Keywords
Abstract In this paper we study and compare several approaches to detect floods and evidence for passability of roads by conventional means in Twitter. We focus on tweets containing both visual information (a picture shared by the user) and metadata, a combination of text and related extra information intrinsic to the Twitter API. This work has been done in the context of the MediaEval 2018 Multimedia Satellite Task.
Address Sophia Antipolis; France; October 2018
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference (up) MediaEval
Notes LAMP; 600.084; 600.109; 600.120 Approved no
Call Number Admin @ si @ LFS2018 Serial 3224
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Author Md. Mostafa Kamal Sarker; Hatem A. Rashwan; Farhan Akram; Syeda Furruka Banu; Adel Saleh; Vivek Kumar Singh; Forhad U. H. Chowdhury; Saddam Abdulwahab; Santiago Romani; Petia Radeva; Domenec Puig
Title SLSDeep: Skin Lesion Segmentation Based on Dilated Residual and Pyramid Pooling Networks. Type Conference Article
Year 2018 Publication 21st International Conference on Medical Image Computing & Computer Assisted Intervention Abbreviated Journal
Volume 2 Issue Pages 21-29
Keywords
Abstract Skin lesion segmentation (SLS) in dermoscopic images is a crucial task for automated diagnosis of melanoma. In this paper, we present a robust deep learning SLS model, so-called SLSDeep, which is represented as an encoder-decoder network. The encoder network is constructed by dilated residual layers, in turn, a pyramid pooling network followed by three convolution layers is used for the decoder. Unlike the traditional methods employing a cross-entropy loss, we investigated a loss function by combining both Negative Log Likelihood (NLL) and End Point Error (EPE) to accurately segment the melanoma regions with sharp boundaries. The robustness of the proposed model was evaluated on two public databases: ISBI 2016 and 2017 for skin lesion analysis towards melanoma detection challenge. The proposed model outperforms the state-of-the-art methods in terms of segmentation accuracy. Moreover, it is capable to segment more than 100 images of size 384x384 per second on a recent GPU.
Address Granada; Espanya; September 2018
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference (up) MICCAI
Notes MILAB; no proj Approved no
Call Number Admin @ si @ SRA2018 Serial 3112
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Author Esmitt Ramirez; Carles Sanchez; Agnes Borras; Marta Diez-Ferrer; Antoni Rosell; Debora Gil
Title Image-Based Bronchial Anatomy Codification for Biopsy Guiding in Video Bronchoscopy Type Conference Article
Year 2018 Publication OR 2.0 Context-Aware Operating Theaters, Computer Assisted Robotic Endoscopy, Clinical Image-Based Procedures, and Skin Image Analysis Abbreviated Journal
Volume 11041 Issue Pages
Keywords Biopsy guiding; Bronchoscopy; Lung biopsy; Intervention guiding; Airway codification
Abstract Bronchoscopy examinations allow biopsy of pulmonary nodules with minimum risk for the patient. Even for experienced bronchoscopists, it is difficult to guide the bronchoscope to most distal lesions and obtain an accurate diagnosis. This paper presents an image-based codification of the bronchial anatomy for bronchoscopy biopsy guiding. The 3D anatomy of each patient is codified as a binary tree with nodes representing bronchial levels and edges labeled using their position on images projecting the 3D anatomy from a set of branching points. The paths from the root to leaves provide a codification of navigation routes with spatially consistent labels according to the anatomy observes in video bronchoscopy explorations. We evaluate our labeling approach as a guiding system in terms of the number of bronchial levels correctly codified, also in the number of labels-based instructions correctly supplied, using generalized mixed models and computer-generated data. Results obtained for three independent observers prove the consistency and reproducibility of our guiding system. We trust that our codification based on viewer’s projection might be used as a foundation for the navigation process in Virtual Bronchoscopy systems.
Address Granada; September 2018
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference (up) MICCAIW
Notes IAM; 600.096; 600.075; 601.323; 600.145 Approved no
Call Number Admin @ si @ RSB2018b Serial 3137
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Author Santi Puch; Irina Sanchez; Aura Hernandez-Sabate; Gemma Piella; Vesna Prckovska
Title Global Planar Convolutions for Improved Context Aggregation in Brain Tumor Segmentation Type Conference Article
Year 2018 Publication International MICCAI Brainlesion Workshop Abbreviated Journal
Volume 11384 Issue Pages 393-405
Keywords Brain tumors; 3D fully-convolutional CNN; Magnetic resonance imaging; Global planar convolution
Abstract In this work, we introduce the Global Planar Convolution module as a building-block for fully-convolutional networks that aggregates global information and, therefore, enhances the context perception capabilities of segmentation networks in the context of brain tumor segmentation. We implement two baseline architectures (3D UNet and a residual version of 3D UNet, ResUNet) and present a novel architecture based on these two architectures, ContextNet, that includes the proposed Global Planar Convolution module. We show that the addition of such module eliminates the need of building networks with several representation levels, which tend to be over-parametrized and to showcase slow rates of convergence. Furthermore, we provide a visual demonstration of the behavior of GPC modules via visualization of intermediate representations. We finally participate in the 2018 edition of the BraTS challenge with our best performing models, that are based on ContextNet, and report the evaluation scores on the validation and the test sets of the challenge.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference (up) MICCAIW
Notes ADAS; 600.118 Approved no
Call Number Admin @ si @ PSH2018 Serial 3251
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Author Boris N. Oreshkin; Pau Rodriguez; Alexandre Lacoste
Title TADAM: Task dependent adaptive metric for improved few-shot learning Type Conference Article
Year 2018 Publication 32nd Annual Conference on Neural Information Processing Systems Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Few-shot learning has become essential for producing models that generalize from few examples. In this work, we identify that metric scaling and metric task conditioning are important to improve the performance of few-shot algorithms. Our analysis reveals that simple metric scaling completely changes the nature of few-shot algorithm parameter updates. Metric scaling provides improvements up to 14% in accuracy for certain metrics on the mini-Imagenet 5-way 5-shot classification task. We further propose a simple and effective way of conditioning a learner on the task sample set, resulting in learning a task-dependent metric space. Moreover, we propose and empirically test a practical end-to-end optimization procedure based on auxiliary task co-training to learn a task-dependent metric space. The resulting few-shot learning model based on the task-dependent scaled metric achieves state of the art on mini-Imagenet. We confirm these results on another few-shot dataset that we introduce in this paper based on CIFAR100.
Address Montreal; Canada; December 2018
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference (up) NIPS
Notes ISE; 600.098; 600.119 Approved no
Call Number Admin @ si @ ORL2018 Serial 3140
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Author Abel Gonzalez-Garcia; Joost Van de Weijer; Yoshua Bengio
Title Image-to-image translation for cross-domain disentanglement Type Conference Article
Year 2018 Publication 32nd Annual Conference on Neural Information Processing Systems Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address Montreal; Canada; December 2018
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference (up) NIPS
Notes LAMP; 600.120 Approved no
Call Number Admin @ si @ GWB2018 Serial 3155
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Author Chenshen Wu; Luis Herranz; Xialei Liu; Joost Van de Weijer; Bogdan Raducanu
Title Memory Replay GANs: Learning to Generate New Categories without Forgetting Type Conference Article
Year 2018 Publication 32nd Annual Conference on Neural Information Processing Systems Abbreviated Journal
Volume Issue Pages 5966-5976
Keywords
Abstract Previous works on sequential learning address the problem of forgetting in discriminative models. In this paper we consider the case of generative models. In particular, we investigate generative adversarial networks (GANs) in the task of learning new categories in a sequential fashion. We first show that sequential fine tuning renders the network unable to properly generate images from previous categories (ie forgetting). Addressing this problem, we propose Memory Replay GANs (MeRGANs), a conditional GAN framework that integrates a memory replay generator. We study two methods to prevent forgetting by leveraging these replays, namely joint training with replay and replay alignment. Qualitative and quantitative experimental results in MNIST, SVHN and LSUN datasets show that our memory replay approach can generate competitive images while significantly mitigating the forgetting of previous categories.
Address Montreal; Canada; December 2018
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference (up) NIPS
Notes LAMP; 600.106; 600.109; 602.200; 600.120 Approved no
Call Number Admin @ si @ WHL2018 Serial 3249
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Author Xavier Soria; Angel Sappa
Title Improving Edge Detection in RGB Images by Adding NIR Channel Type Conference Article
Year 2018 Publication 14th IEEE International Conference on Signal Image Technology & Internet Based System Abbreviated Journal
Volume Issue Pages
Keywords Edge detection; Contour detection; VGG; CNN; RGB-NIR; Near infrared images
Abstract The edge detection is yet a critical problem in many computer vision and image processing tasks. The manuscript presents an Holistically-Nested Edge Detection based approach to study the inclusion of Near-Infrared in the Visible spectrum
images. To do so, a Single Sensor based dataset has been acquired in the range of 400nm to 1100nm wavelength spectral band. Prominent results have been obtained even when the ground truth (annotated edge-map) is based in the visible wavelength spectrum.
Address Las Palmas de Gran Canaria; November 2018
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference (up) SITIS
Notes MSIAU; 600.122 Approved no
Call Number Admin @ si @ SoS2018 Serial 3192
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Author Patricia Suarez; Angel Sappa; Boris X. Vintimilla
Title Cross-spectral image dehaze through a dense stacked conditional GAN based approach Type Conference Article
Year 2018 Publication 14th IEEE International Conference on Signal Image Technology & Internet Based System Abbreviated Journal
Volume Issue Pages
Keywords Infrared imaging; Dense; Stacked CGAN; Crossspectral; Convolutional networks
Abstract This paper proposes a novel approach to remove haze from RGB images using a near infrared images based on a dense stacked conditional Generative Adversarial Network (CGAN). The architecture of the deep network implemented
receives, besides the images with haze, its corresponding image in the near infrared spectrum, which serve to accelerate the learning process of the details of the characteristics of the images. The model uses a triplet layer that allows the independence learning of each channel of the visible spectrum image to remove the haze on each color channel separately. A multiple loss function scheme is proposed, which ensures balanced learning between the colors
and the structure of the images. Experimental results have shown that the proposed method effectively removes the haze from the images. Additionally, the proposed approach is compared with a state of the art approach showing better results.
Address Las Palmas de Gran Canaria; November 2018
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-1-5386-9385-8 Medium
Area Expedition Conference (up) SITIS
Notes MSIAU; 600.086; 600.130; 600.122 Approved no
Call Number Admin @ si @ SSV2018a Serial 3193
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Author Jorge Charco; Boris X. Vintimilla; Angel Sappa
Title Deep learning based camera pose estimation in multi-view environment Type Conference Article
Year 2018 Publication 14th IEEE International Conference on Signal Image Technology & Internet Based System Abbreviated Journal
Volume Issue Pages
Keywords Deep learning; Camera pose estimation; Multiview environment; Siamese architecture
Abstract This paper proposes to use a deep learning network architecture for relative camera pose estimation on a multi-view environment. The proposed network is a variant architecture of AlexNet to use as regressor for prediction the relative translation and rotation as output. The proposed approach is trained from
scratch on a large data set that takes as input a pair of imagesfrom the same scene. This new architecture is compared with a previous approach using standard metrics, obtaining better results on the relative camera pose.
Address Las Palmas de Gran Canaria; November 2018
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference (up) SITIS
Notes MSIAU; 600.086; 600.130; 600.122 Approved no
Call Number Admin @ si @ CVS2018 Serial 3194
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Author Carles Sanchez; Miguel Viñas; Coen Antens; Agnes Borras; Debora Gil
Title Back to Front Architecture for Diagnosis as a Service Type Conference Article
Year 2018 Publication 20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing Abbreviated Journal
Volume Issue Pages 343-346
Keywords
Abstract Software as a Service (SaaS) is a cloud computing model in which a provider hosts applications in a server that customers use via internet. Since SaaS does not require to install applications on customers' own computers, it allows the use by multiple users of highly specialized software without extra expenses for hardware acquisition or licensing. A SaaS tailored for clinical needs not only would alleviate licensing costs, but also would facilitate easy access to new methods for diagnosis assistance. This paper presents a SaaS client-server architecture for Diagnosis as a Service (DaaS). The server is based on docker technology in order to allow execution of softwares implemented in different languages with the highest portability and scalability. The client is a content management system allowing the design of websites with multimedia content and interactive visualization of results allowing user editing. We explain a usage case that uses our DaaS as crowdsourcing platform in a multicentric pilot study carried out to evaluate the clinical benefits of a software for assessment of central airway obstruction.
Address Timisoara; Rumania; September 2018
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference (up) SYNASC
Notes IAM; 600.145 Approved no
Call Number Admin @ si @ SVA2018 Serial 3360
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Author Mohamed Ilyes Lakhal; Hakan Cevikalp; Sergio Escalera
Title CRN: End-to-end Convolutional Recurrent Network Structure Applied to Vehicle Classification Type Conference Article
Year 2018 Publication 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications Abbreviated Journal
Volume 5 Issue Pages 137-144
Keywords Vehicle Classification; Deep Learning; End-to-end Learning
Abstract Vehicle type classification is considered to be a central part of Intelligent Traffic Systems. In the recent years, deep learning methods have emerged in as being the state-of-the-art in many computer vision tasks. In this paper, we present a novel yet simple deep learning framework for the vehicle type classification problem. We propose an end-to-end trainable system, that combines convolution neural network for feature extraction and recurrent neural network as a classifier. The recurrent network structure is used to handle various types of feature inputs, and at the same time allows to produce a single or a set of class predictions. In order to assess the effectiveness of our solution, we have conducted a set of experiments in two public datasets, obtaining state of the art results. In addition, we also report results on the newly released MIO-TCD dataset.
Address Funchal; Madeira; Portugal; January 2018
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference (up) VISAPP
Notes HUPBA Approved no
Call Number Admin @ si @ LCE2018a Serial 3094
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