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| Author | David Geronimo; David Vazquez; Arturo de la Escalera | ||||
| Title | Vision-Based Advanced Driver Assistance Systems | Type | Book Chapter | ||
| Year | 2017 | Publication | Computer Vision in Vehicle Technology: Land, Sea, and Air | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | ADAS; Autonomous Driving | ||||
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ADAS; 600.118 | Approved | no | ||
| Call Number | ADAS @ adas @ GVE2017 | Serial | 2881 | ||
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| Author | Victor Vaquero; German Ros; Francesc Moreno-Noguer; Antonio Lopez; Alberto Sanfeliu | ||||
| Title | Joint coarse-and-fine reasoning for deep optical flow | Type | Conference Article | ||
| Year | 2017 | Publication | 24th International Conference on Image Processing | Abbreviated Journal | |
| Volume | Issue | Pages | 2558-2562 | ||
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| Abstract | We propose a novel representation for dense pixel-wise estimation tasks using CNNs that boosts accuracy and reduces training time, by explicitly exploiting joint coarse-and-fine reasoning. The coarse reasoning is performed over a discrete classification space to obtain a general rough solution, while the fine details of the solution are obtained over a continuous regression space. In our approach both components are jointly estimated, which proved to be beneficial for improving estimation accuracy. Additionally, we propose a new network architecture, which combines coarse and fine components by treating the fine estimation as a refinement built on top of the coarse solution, and therefore adding details to the general prediction. We apply our approach to the challenging problem of optical flow estimation and empirically validate it against state-of-the-art CNN-based solutions trained from scratch and tested on large optical flow datasets. | ||||
| Address | Beijing; China; September 2017 | ||||
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| Area | Expedition | Conference | ICIP | ||
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ADAS; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ VRM2017 | Serial | 2898 | ||
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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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ADAS; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ LIP2017a | Serial | 2937 | ||
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| Author | Antonio Lopez; Gabriel Villalonga; Laura Sellart; German Ros; David Vazquez; Jiaolong Xu; Javier Marin; Azadeh S. Mozafari | ||||
| Title | Training my car to see using virtual worlds | Type | Journal Article | ||
| Year | 2017 | Publication | Image and Vision Computing | Abbreviated Journal | IMAVIS |
| Volume | 38 | Issue | Pages | 102-118 | |
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| Abstract | Computer vision technologies are at the core of different advanced driver assistance systems (ADAS) and will play a key role in oncoming autonomous vehicles too. One of the main challenges for such technologies is to perceive the driving environment, i.e. to detect and track relevant driving information in a reliable manner (e.g. pedestrians in the vehicle route, free space to drive through). Nowadays it is clear that machine learning techniques are essential for developing such a visual perception for driving. In particular, the standard working pipeline consists of collecting data (i.e. on-board images), manually annotating the data (e.g. drawing bounding boxes around pedestrians), learning a discriminative data representation taking advantage of such annotations (e.g. a deformable part-based model, a deep convolutional neural network), and then assessing the reliability of such representation with the acquired data. In the last two decades most of the research efforts focused on representation learning (first, designing descriptors and learning classifiers; later doing it end-to-end). Hence, collecting data and, especially, annotating it, is essential for learning good representations. While this has been the case from the very beginning, only after the disruptive appearance of deep convolutional neural networks that it became a serious issue due to their data hungry nature. In this context, the problem is that manual data annotation is a tiresome work prone to errors. Accordingly, in the late 00’s we initiated a research line consisting of training visual models using photo-realistic computer graphics, especially focusing on assisted and autonomous driving. In this paper, we summarize such a work and show how it has become a new tendency with increasing acceptance. | ||||
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ADAS; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ LVS2017 | Serial | 2985 | ||
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| Author | Katerine Diaz; Konstantia Georgouli; Anastasios Koidis; Jesus Martinez del Rincon | ||||
| Title | Incremental model learning for spectroscopy-based food analysis | Type | Journal Article | ||
| Year | 2017 | Publication | Chemometrics and Intelligent Laboratory Systems | Abbreviated Journal | CILS |
| Volume | 167 | Issue | Pages | 123-131 | |
| Keywords | Incremental model learning; IGDCV technique; Subspace based learning; IdentificationVegetable oils; FT-IR spectroscopy | ||||
| Abstract | In this paper we propose the use of incremental learning for creating and improving multivariate analysis models in the field of chemometrics of spectral data. As main advantages, our proposed incremental subspace-based learning allows creating models faster, progressively improving previously created models and sharing them between laboratories and institutions without requiring transferring or disclosing individual spectra samples. In particular, our approach allows to improve the generalization and adaptability of previously generated models with a few new spectral samples to be applicable to real-world situations. The potential of our approach is demonstrated using vegetable oil type identification based on spectroscopic data as case study. Results show how incremental models maintain the accuracy of batch learning methodologies while reducing their computational cost and handicaps. | ||||
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ADAS; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ DGK2017 | Serial | 3002 | ||
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| Author | Cristhian Aguilera | ||||
| Title | Local feature description in cross-spectral imagery | Type | Book Whole | ||
| Year | 2017 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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| Abstract | Over the last few years, the number of consumer computer vision applications has increased dramatically. Today, computer vision solutions can be found in video game consoles, smartphone applications, driving assistance – just to name a few. Ideally, we require the performance of those applications, particularly those that are safety critical to remain constant under any external environment factors, such as changes in illumination or weather conditions. However, this is not always possible or very difficult to obtain by only using visible imagery, due to the inherent limitations of the images from that spectral band. For that reason, the use of images from different or multiple spectral bands is becoming more appealing.
The aforementioned possible advantages of using images from multiples spectral bands on various vision applications make multi-spectral image processing a relevant topic for research and development. Like in visible image processing, multi-spectral image processing needs tools and algorithms to handle information from various spectral bands. Furthermore, traditional tools such as local feature detection, which is the basis of many vision tasks such as visual odometry, image registration, or structure from motion, must be adjusted or reformulated to operate under new conditions. Traditional feature detection, description, and matching methods tend to underperform in multi-spectral settings, in comparison to mono-spectral settings, due to the natural differences between each spectral band. The work in this thesis is focused on the local feature description problem when cross-spectral images are considered. In this context, this dissertation has three main contributions. Firstly, the work starts by proposing the usage of a combination of frequency and spatial information, in a multi-scale scheme, as feature description. Evaluations of this proposal, based on classical hand-made feature descriptors, and comparisons with state of the art cross-spectral approaches help to find and understand limitations of such strategy. Secondly, different convolutional neural network (CNN) based architectures are evaluated when used to describe cross-spectral image patches. Results showed that CNN-based methods, designed to work with visible monocular images, could be successfully applied to the description of images from two different spectral bands, with just minor modifications. In this framework, a novel CNN-based network model, specifically intended to describe image patches from two different spectral bands, is proposed. This network, referred to as Q-Net, outperforms state of the art in the cross-spectral domain, including both previous hand-made solutions as well as L2 CNN-based architectures. The third contribution of this dissertation is in the cross-spectral feature description application domain. The multispectral odometry problem is tackled showing a real application of cross-spectral descriptors In addition to the three main contributions mentioned above, in this dissertation, two different multi-spectral datasets are generated and shared with the community to be used as benchmarks for further studies. |
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| Address | October 2017 | ||||
| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Angel Sappa | |
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| ISSN | ISBN | 978-84-945373-6-3 | Medium | ||
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ADAS; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ Agu2017 | Serial | 3020 | ||
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| Author | Jose M. Armingol; Jorge Alfonso; Nourdine Aliane; Miguel Clavijo; Sergio Campos-Cordobes; Arturo de la Escalera; Javier del Ser; Javier Fernandez; Fernando Garcia; Felipe Jimenez; Antonio Lopez; Mario Mata | ||||
| Title | Environmental Perception for Intelligent Vehicles | Type | Book Chapter | ||
| Year | 2018 | Publication | Intelligent Vehicles. Enabling Technologies and Future Developments | Abbreviated Journal | |
| Volume | Issue | Pages | 23–101 | ||
| Keywords | Computer vision; laser techniques; data fusion; advanced driver assistance systems; traffic monitoring systems; intelligent vehicles | ||||
| Abstract | Environmental perception represents, because of its complexity, a challenge for Intelligent Transport Systems due to the great variety of situations and different elements that can happen in road environments and that must be faced by these systems. In connection with this, so far there are a variety of solutions as regards sensors and methods, so the results of precision, complexity, cost, or computational load obtained by these works are different. In this chapter some systems based on computer vision and laser techniques are presented. Fusion methods are also introduced in order to provide advanced and reliable perception systems. | ||||
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ADAS; 600.118 | Approved | no | ||
| Call Number | Admin @ si @AAA2018 | Serial | 3046 | ||
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| Author | Antonio Lopez; David Vazquez; Gabriel Villalonga | ||||
| Title | Data for Training Models, Domain Adaptation | Type | Book Chapter | ||
| Year | 2018 | Publication | Intelligent Vehicles. Enabling Technologies and Future Developments | Abbreviated Journal | |
| Volume | Issue | Pages | 395–436 | ||
| Keywords | Driving simulator; hardware; software; interface; traffic simulation; macroscopic simulation; microscopic simulation; virtual data; training data | ||||
| Abstract | Simulation can enable several developments in the field of intelligent vehicles. This chapter is divided into three main subsections. The first one deals with driving simulators. The continuous improvement of hardware performance is a well-known fact that is allowing the development of more complex driving simulators. The immersion in the simulation scene is increased by high fidelity feedback to the driver. In the second subsection, traffic simulation is explained as well as how it can be used for intelligent transport systems. Finally, it is rather clear that sensor-based perception and action must be based on data-driven algorithms. Simulation could provide data to train and test algorithms that are afterwards implemented in vehicles. These tools are explained in the third subsection. | ||||
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ADAS; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ LVV2018 | Serial | 3047 | ||
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| Author | Konstantia Georgouli; Katerine Diaz; Jesus Martinez del Rincon; Anastasios Koidis | ||||
| Title | Building generic, easily-updatable chemometric models with harmonisation and augmentation features: The case of FTIR vegetable oils classification | Type | Conference Article | ||
| Year | 2017 | Publication | 3rd Ιnternational Conference Metrology Promoting Standardization and Harmonization in Food and Nutrition | Abbreviated Journal | |
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| Address | Thessaloniki; Greece; October 2017 | ||||
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| Area | Expedition | Conference | IMEKOFOODS | ||
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ADAS; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ GDM2017 | Serial | 3081 | ||
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| Author | Katerine Diaz; Francesc J. Ferri; Aura Hernandez-Sabate | ||||
| Title | An overview of incremental feature extraction methods based on linear subspaces | Type | Journal Article | ||
| Year | 2018 | Publication | Knowledge-Based Systems | Abbreviated Journal | KBS |
| Volume | 145 | Issue | Pages | 219-235 | |
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| Abstract | With the massive explosion of machine learning in our day-to-day life, incremental and adaptive learning has become a major topic, crucial to keep up-to-date and improve classification models and their corresponding feature extraction processes. This paper presents a categorized overview of incremental feature extraction based on linear subspace methods which aim at incorporating new information to the already acquired knowledge without accessing previous data. Specifically, this paper focuses on those linear dimensionality reduction methods with orthogonal matrix constraints based on global loss function, due to the extensive use of their batch approaches versus other linear alternatives. Thus, we cover the approaches derived from Principal Components Analysis, Linear Discriminative Analysis and Discriminative Common Vector methods. For each basic method, its incremental approaches are differentiated according to the subspace model and matrix decomposition involved in the updating process. Besides this categorization, several updating strategies are distinguished according to the amount of data used to update and to the fact of considering a static or dynamic number of classes. Moreover, the specific role of the size/dimension ratio in each method is considered. Finally, computational complexity, experimental setup and the accuracy rates according to published results are compiled and analyzed, and an empirical evaluation is done to compare the best approach of each kind. | ||||
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| ISSN | 0950-7051 | ISBN | Medium | ||
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ADAS; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ DFH2018 | Serial | 3090 | ||
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| Author | Katerine Diaz; Jesus Martinez del Rincon; Aura Hernandez-Sabate; Debora Gil | ||||
| Title | Continuous head pose estimation using manifold subspace embedding and multivariate regression | Type | Journal Article | ||
| Year | 2018 | Publication | IEEE Access | Abbreviated Journal | ACCESS |
| Volume | 6 | Issue | Pages | 18325 - 18334 | |
| Keywords | Head Pose estimation; HOG features; Generalized Discriminative Common Vectors; B-splines; Multiple linear regression | ||||
| Abstract | In this paper, a continuous head pose estimation system is proposed to estimate yaw and pitch head angles from raw facial images. Our approach is based on manifold learningbased methods, due to their promising generalization properties shown for face modelling from images. The method combines histograms of oriented gradients, generalized discriminative common vectors and continuous local regression to achieve successful performance. Our proposal was tested on multiple standard face datasets, as well as in a realistic scenario. Results show a considerable performance improvement and a higher consistence of our model in comparison with other state-of-art methods, with angular errors varying between 9 and 17 degrees. | ||||
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| ISSN | 2169-3536 | ISBN | Medium | ||
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ADAS; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ DMH2018b | Serial | 3091 | ||
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| Author | Cesar de Souza | ||||
| Title | Action Recognition in Videos: Data-efficient approaches for supervised learning of human action classification models for video | Type | Book Whole | ||
| Year | 2018 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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| Abstract | In this dissertation, we explore different ways to perform human action recognition in video clips. We focus on data efficiency, proposing new approaches that alleviate the need for laborious and time-consuming manual data annotation. In the first part of this dissertation, we start by analyzing previous state-of-the-art models, comparing their differences and similarities in order to pinpoint where their real strengths come from. Leveraging this information, we then proceed to boost the classification accuracy of shallow models to levels that rival deep neural networks. We introduce hybrid video classification architectures based on carefully designed unsupervised representations of handcrafted spatiotemporal features classified by supervised deep networks. We show in our experiments that our hybrid model combine the best of both worlds: it is data efficient (trained on 150 to 10,000 short clips) and yet improved significantly on the state of the art, including deep models trained on millions of manually labeled images and videos. In the second part of this research, we investigate the generation of synthetic training data for action recognition, as it has recently shown promising results for a variety of other computer vision tasks. We propose an interpretable parametric generative model of human action videos that relies on procedural generation and other computer graphics techniques of modern game engines. We generate a diverse, realistic, and physically plausible dataset of human action videos, called PHAV for “Procedural Human Action Videos”. It contains a total of 39,982 videos, with more than 1,000 examples for each action of 35 categories. Our approach is not limited to existing motion capture sequences, and we procedurally define 14 synthetic actions. We then introduce deep multi-task representation learning architectures to mix synthetic and real videos, even if the action categories differ. Our experiments on the UCF-101 and HMDB-51 benchmarks suggest that combining our large set of synthetic videos with small real-world datasets can boost recognition performance, outperforming fine-tuning state-of-the-art unsupervised generative models of videos. | ||||
| Address | April 2018 | ||||
| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Antonio Lopez;Naila Murray | |
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ADAS; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ Sou2018 | Serial | 3127 | ||
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| Author | Adrien Gaidon; Antonio Lopez; Florent Perronnin | ||||
| Title | The Reasonable Effectiveness of Synthetic Visual Data | Type | Journal Article | ||
| Year | 2018 | Publication | International Journal of Computer Vision | Abbreviated Journal | IJCV |
| Volume | 126 | Issue | 9 | Pages | 899–901 |
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ADAS; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ GLP2018 | Serial | 3180 | ||
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| Author | Antonio Lopez | ||||
| Title | Pedestrian Detection Systems | Type | Book Chapter | ||
| Year | 2018 | Publication | Wiley Encyclopedia of Electrical and Electronics Engineering | Abbreviated Journal | |
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| Abstract | Pedestrian detection is a highly relevant topic for both advanced driver assistance systems (ADAS) and autonomous driving. In this entry, we review the ideas behind pedestrian detection systems from the point of view of perception based on computer vision and machine learning. | ||||
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ADAS; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ Lop2018 | Serial | 3230 | ||
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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. | ||||
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| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| Area | Expedition | Conference | MICCAIW | ||
| Notes |
ADAS; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ PSH2018 | Serial | 3251 | ||
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