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Author (down) Henry Velesaca; Gisel Bastidas-Guacho; Mohammad Rouhani; Angel Sappa edit  url
openurl 
  Title Multimodal image registration techniques: a comprehensive survey Type Journal Article
  Year 2024 Publication Multimedia Tools and Applications Abbreviated Journal MTAP  
  Volume Issue Pages  
  Keywords  
  Abstract This manuscript presents a review of state-of-the-art techniques proposed in the literature for multimodal image registration, addressing instances where images from different modalities need to be precisely aligned in the same reference system. This scenario arises when the images to be registered come from different modalities, among the visible and thermal spectral bands, 3D-RGB, or flash-no flash, or NIR-visible. The review spans different techniques from classical approaches to more modern ones based on deep learning, aiming to highlight the particularities required at each step in the registration pipeline when dealing with multimodal images. It is noteworthy that medical images are excluded from this review due to their specific characteristics, including the use of both active and passive sensors or the non-rigid nature of the body contained in the image.  
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  Area Expedition Conference  
  Notes MSIAU;ADAS Approved no  
  Call Number Admin @ si @ VBR2024 Serial 3997  
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Author (down) Hannes Mueller; Andre Groeger; Jonathan Hersh; Andrea Matranga; Joan Serrat edit   pdf
url  doi
openurl 
  Title Monitoring war destruction from space using machine learning Type Journal Article
  Year 2021 Publication Proceedings of the National Academy of Sciences of the United States of America Abbreviated Journal PNAS  
  Volume 118 Issue 23 Pages e2025400118  
  Keywords  
  Abstract Existing data on building destruction in conflict zones rely on eyewitness reports or manual detection, which makes it generally scarce, incomplete, and potentially biased. This lack of reliable data imposes severe limitations for media reporting, humanitarian relief efforts, human-rights monitoring, reconstruction initiatives, and academic studies of violent conflict. This article introduces an automated method of measuring destruction in high-resolution satellite images using deep-learning techniques combined with label augmentation and spatial and temporal smoothing, which exploit the underlying spatial and temporal structure of destruction. As a proof of concept, we apply this method to the Syrian civil war and reconstruct the evolution of damage in major cities across the country. Our approach allows generating destruction data with unprecedented scope, resolution, and frequency—and makes use of the ever-higher frequency at which satellite imagery becomes available.  
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  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
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  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes ADAS; 600.118 Approved no  
  Call Number Admin @ si @ MGH2021 Serial 3584  
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Author (down) Gemma Rotger; Francesc Moreno-Noguer; Felipe Lumbreras; Antonio Agudo edit  url
openurl 
  Title Detailed 3D face reconstruction from a single RGB image Type Journal
  Year 2019 Publication Journal of WSCG Abbreviated Journal JWSCG  
  Volume 27 Issue 2 Pages 103-112  
  Keywords 3D Wrinkle Reconstruction; Face Analysis, Optimization.  
  Abstract This paper introduces a method to obtain a detailed 3D reconstruction of facial skin from a single RGB image.
To this end, we propose the exclusive use of an input image without requiring any information about the observed material nor training data to model the wrinkle properties. They are detected and characterized directly from the image via a simple and effective parametric model, determining several features such as location, orientation, width, and height. With these ingredients, we propose to minimize a photometric error to retrieve the final detailed 3D map, which is initialized by current techniques based on deep learning. In contrast with other approaches, we only require estimating a depth parameter, making our approach fast and intuitive. Extensive experimental evaluation is presented in a wide variety of synthetic and real images, including different skin properties and facial
expressions. In all cases, our method outperforms the current approaches regarding 3D reconstruction accuracy, providing striking results for both large and fine wrinkles.
 
  Address 2019/11  
  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  
  Notes MSIAU; 600.086; 600.130; 600.122;ADAS Approved no  
  Call Number Admin @ si @ Serial 3708  
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Author (down) Gabriel Villalonga; Joost Van de Weijer; Antonio Lopez edit  url
doi  openurl
  Title Recognizing new classes with synthetic data in the loop: application to traffic sign recognition Type Journal Article
  Year 2020 Publication Sensors Abbreviated Journal SENS  
  Volume 20 Issue 3 Pages 583  
  Keywords  
  Abstract On-board vision systems may need to increase the number of classes that can be recognized in a relatively short period. For instance, a traffic sign recognition system may suddenly be required to recognize new signs. Since collecting and annotating samples of such new classes may need more time than we wish, especially for uncommon signs, we propose a method to generate these samples by combining synthetic images and Generative Adversarial Network (GAN) technology. In particular, the GAN is trained on synthetic and real-world samples from known classes to perform synthetic-to-real domain adaptation, but applied to synthetic samples of the new classes. Using the Tsinghua dataset with a synthetic counterpart, SYNTHIA-TS, we have run an extensive set of experiments. The results show that the proposed method is indeed effective, provided that we use a proper Convolutional Neural Network (CNN) to perform the traffic sign recognition (classification) task as well as a proper GAN to transform the synthetic images. Here, a ResNet101-based classifier and domain adaptation based on CycleGAN performed extremely well for a ratio∼ 1/4 for new/known classes; even for more challenging ratios such as∼ 4/1, the results are also very positive.  
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  Area Expedition Conference  
  Notes LAMP; ADAS; 600.118; 600.120;CIC Approved no  
  Call Number Admin @ si @ VWL2020 Serial 3405  
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Author (down) Gabriel Villalonga; Antonio Lopez edit   pdf
doi  openurl
  Title Co-Training for On-Board Deep Object Detection Type Journal Article
  Year 2020 Publication IEEE Access Abbreviated Journal ACCESS  
  Volume Issue Pages 194441 - 194456  
  Keywords  
  Abstract Providing ground truth supervision to train visual models has been a bottleneck over the years, exacerbated by domain shifts which degenerate the performance of such models. This was the case when visual tasks relied on handcrafted features and shallow machine learning and, despite its unprecedented performance gains, the problem remains open within the deep learning paradigm due to its data-hungry nature. Best performing deep vision-based object detectors are trained in a supervised manner by relying on human-labeled bounding boxes which localize class instances (i.e. objects) within the training images. Thus, object detection is one of such tasks for which human labeling is a major bottleneck. In this article, we assess co-training as a semi-supervised learning method for self-labeling objects in unlabeled images, so reducing the human-labeling effort for developing deep object detectors. Our study pays special attention to a scenario involving domain shift; in particular, when we have automatically generated virtual-world images with object bounding boxes and we have real-world images which are unlabeled. Moreover, we are particularly interested in using co-training for deep object detection in the context of driver assistance systems and/or self-driving vehicles. Thus, using well-established datasets and protocols for object detection in these application contexts, we will show how co-training is a paradigm worth to pursue for alleviating object labeling, working both alone and together with task-agnostic domain adaptation.  
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  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
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  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes ADAS; 600.118 Approved no  
  Call Number Admin @ si @ ViL2020 Serial 3488  
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Author (down) Francisco Blanco; Felipe Lumbreras; Joan Serrat; Roswitha Siener; Silvia Serranti; Giuseppe Bonifazi; Montserrat Lopez Mesas; Manuel Valiente edit  doi
openurl 
  Title Taking advantage of Hyperspectral Imaging classification of urinary stones against conventional IR Spectroscopy Type Journal Article
  Year 2014 Publication Journal of Biomedical Optics Abbreviated Journal JBiO  
  Volume 19 Issue 12 Pages 126004-1 - 126004-9  
  Keywords  
  Abstract The analysis of urinary stones is mandatory for the best management of the disease after the stone passage in order to prevent further stone episodes. Thus the use of an appropriate methodology for an individualized stone analysis becomes a key factor for giving the patient the most suitable treatment. A recently developed hyperspectral imaging methodology, based on pixel-to-pixel analysis of near-infrared spectral images, is compared to the reference technique in stone analysis, infrared (IR) spectroscopy. The developed classification model yields >90% correct classification rate when compared to IR and is able to precisely locate stone components within the structure of the stone with a 15 µm resolution. Due to the little sample pretreatment, low analysis time, good performance of the model, and the automation of the measurements, they become analyst independent; this methodology can be considered to become a routine analysis for clinical laboratories.  
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  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes ADAS; 600.076 Approved no  
  Call Number Admin @ si @ BLS2014 Serial 2563  
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Author (down) Ferran Diego; Joan Serrat; Antonio Lopez edit   pdf
doi  openurl
  Title Joint spatio-temporal alignment of sequences Type Journal Article
  Year 2013 Publication IEEE Transactions on Multimedia Abbreviated Journal TMM  
  Volume 15 Issue 6 Pages 1377-1387  
  Keywords video alignment  
  Abstract Video alignment is important in different areas of computer vision such as wide baseline matching, action recognition, change detection, video copy detection and frame dropping prevention. Current video alignment methods usually deal with a relatively simple case of fixed or rigidly attached cameras or simultaneous acquisition. Therefore, in this paper we propose a joint video alignment for bringing two video sequences into a spatio-temporal alignment. Specifically, the novelty of the paper is to formulate the video alignment to fold the spatial and temporal alignment into a single alignment framework. This simultaneously satisfies a frame-correspondence and frame-alignment similarity; exploiting the knowledge among neighbor frames by a standard pairwise Markov random field (MRF). This new formulation is able to handle the alignment of sequences recorded at different times by independent moving cameras that follows a similar trajectory, and also generalizes the particular cases that of fixed geometric transformation and/or linear temporal mapping. We conduct experiments on different scenarios such as sequences recorded simultaneously or by moving cameras to validate the robustness of the proposed approach. The proposed method provides the highest video alignment accuracy compared to the state-of-the-art methods on sequences recorded from vehicles driving along the same track at different times.  
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  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1520-9210 ISBN Medium  
  Area Expedition Conference  
  Notes ADAS Approved no  
  Call Number Admin @ si @ DSL2013; ADAS @ adas @ Serial 2228  
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Author (down) Ferran Diego; Daniel Ponsa; Joan Serrat; Antonio Lopez edit   pdf
openurl 
  Title Video Alignment for Change Detection Type Journal Article
  Year 2011 Publication IEEE Transactions on Image Processing Abbreviated Journal TIP  
  Volume 20 Issue 7 Pages 1858-1869  
  Keywords video alignment  
  Abstract In this work, we address the problem of aligning two video sequences. Such alignment refers to synchronization, i.e., the establishment of temporal correspondence between frames of the first and second video, followed by spatial registration of all the temporally corresponding frames. Video synchronization and alignment have been attempted before, but most often in the relatively simple cases of fixed or rigidly attached cameras and simultaneous acquisition. In addition, restrictive assumptions have been applied, including linear time correspondence or the knowledge of the complete trajectories of corresponding scene points; to some extent, these assumptions limit the practical applicability of any solutions developed. We intend to solve the more general problem of aligning video sequences recorded by independently moving cameras that follow similar trajectories, based only on the fusion of image intensity and GPS information. The novelty of our approach is to pose the synchronization as a MAP inference problem on a Bayesian network including the observations from these two sensor types, which have been proved complementary. Alignment results are presented in the context of videos recorded from vehicles driving along the same track at different times, for different road types. In addition, we explore two applications of the proposed video alignment method, both based on change detection between aligned videos. One is the detection of vehicles, which could be of use in ADAS. The other is online difference spotting videos of surveillance rounds.  
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  Notes ADAS; IF Approved no  
  Call Number DPS 2011; ADAS @ adas @ dps2011 Serial 1705  
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Author (down) Fernando Barrera; Felipe Lumbreras; Angel Sappa edit  url
doi  openurl
  Title Multispectral Piecewise Planar Stereo using Manhattan-World Assumption Type Journal Article
  Year 2013 Publication Pattern Recognition Letters Abbreviated Journal PRL  
  Volume 34 Issue 1 Pages 52-61  
  Keywords Multispectral stereo rig; Dense disparity maps from multispectral stereo; Color and infrared images  
  Abstract This paper proposes a new framework for extracting dense disparity maps from a multispectral stereo rig. The system is constructed with an infrared and a color camera. It is intended to explore novel multispectral stereo matching approaches that will allow further extraction of semantic information. The proposed framework consists of three stages. Firstly, an initial sparse disparity map is generated by using a cost function based on feature matching in a multiresolution scheme. Then, by looking at the color image, a set of planar hypotheses is defined to describe the surfaces on the scene. Finally, the previous stages are combined by reformulating the disparity computation as a global minimization problem. The paper has two main contributions. The first contribution combines mutual information with a shape descriptor based on gradient in a multiresolution scheme. The second contribution, which is based on the Manhattan-world assumption, extracts a dense disparity representation using the graph cut algorithm. Experimental results in outdoor scenarios are provided showing the validity of the proposed framework.  
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  Notes ADAS; 600.054; 600.055; 605.203 Approved no  
  Call Number Admin @ si @ BLS2013 Serial 2245  
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Author (down) Fernando Barrera; Felipe Lumbreras; Angel Sappa edit   pdf
doi  openurl
  Title Multimodal Stereo Vision System: 3D Data Extraction and Algorithm Evaluation Type Journal Article
  Year 2012 Publication IEEE Journal of Selected Topics in Signal Processing Abbreviated Journal J-STSP  
  Volume 6 Issue 5 Pages 437-446  
  Keywords  
  Abstract This paper proposes an imaging system for computing sparse depth maps from multispectral images. A special stereo head consisting of an infrared and a color camera defines the proposed multimodal acquisition system. The cameras are rigidly attached so that their image planes are parallel. Details about the calibration and image rectification procedure are provided. Sparse disparity maps are obtained by the combined use of mutual information enriched with gradient information. The proposed approach is evaluated using a Receiver Operating Characteristics curve. Furthermore, a multispectral dataset, color and infrared images, together with their corresponding ground truth disparity maps, is generated and used as a test bed. Experimental results in real outdoor scenarios are provided showing its viability and that the proposed approach is not restricted to a specific domain.  
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  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1932-4553 ISBN Medium  
  Area Expedition Conference  
  Notes ADAS Approved no  
  Call Number Admin @ si @ BLS2012b Serial 2155  
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