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Author Juan Jose Rubio; Takahiro Kashiwa; Teera Laiteerapong; Wenlong Deng; Kohei Nagai; Sergio Escalera; Kotaro Nakayama; Yutaka Matsuo; Helmut Prendinger edit  url
doi  openurl
  Title (up) Multi-class structural damage segmentation using fully convolutional networks Type Journal Article
  Year 2019 Publication Computers in Industry Abbreviated Journal COMPUTIND  
  Volume 112 Issue Pages 103121  
  Keywords Bridge damage detection; Deep learning; Semantic segmentation  
  Abstract Structural Health Monitoring (SHM) has benefited from computer vision and more recently, Deep Learning approaches, to accurately estimate the state of deterioration of infrastructure. In our work, we test Fully Convolutional Networks (FCNs) with a dataset of deck areas of bridges for damage segmentation. We create a dataset for delamination and rebar exposure that has been collected from inspection records of bridges in Niigata Prefecture, Japan. The dataset consists of 734 images with three labels per image, which makes it the largest dataset of images of bridge deck damage. This data allows us to estimate the performance of our method based on regions of agreement, which emulates the uncertainty of in-field inspections. We demonstrate the practicality of FCNs to perform automated semantic segmentation of surface damages. Our model achieves a mean accuracy of 89.7% for delamination and 78.4% for rebar exposure, and a weighted F1 score of 81.9%.  
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  Area Expedition Conference  
  Notes HuPBA; no proj;MILAB;ADAS Approved no  
  Call Number Admin @ si @ RKL2019 Serial 3315  
Permanent link to this record
 

 
Author Meysam Madadi; Sergio Escalera; Jordi Gonzalez; Xavier Roca; Felipe Lumbreras edit  doi
openurl 
  Title (up) Multi-part body segmentation based on depth maps for soft biometry analysis Type Journal Article
  Year 2015 Publication Pattern Recognition Letters Abbreviated Journal PRL  
  Volume 56 Issue Pages 14-21  
  Keywords 3D shape context; 3D point cloud alignment; Depth maps; Human body segmentation; Soft biometry analysis  
  Abstract This paper presents a novel method extracting biometric measures using depth sensors. Given a multi-part labeled training data, a new subject is aligned to the best model of the dataset, and soft biometrics such as lengths or circumference sizes of limbs and body are computed. The process is performed by training relevant pose clusters, defining a representative model, and fitting a 3D shape context descriptor within an iterative matching procedure. We show robust measures by applying orthogonal plates to body hull. We test our approach in a novel full-body RGB-Depth data set, showing accurate estimation of soft biometrics and better segmentation accuracy in comparison with random forest approach without requiring large training data.  
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  Area Expedition Conference  
  Notes HuPBA; ISE; ADAS; 600.076;600.049; 600.063; 600.054; 302.018;MILAB Approved no  
  Call Number Admin @ si @ MEG2015 Serial 2588  
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Author T. Mouats; N. Aouf; Angel Sappa; Cristhian A. Aguilera-Carrasco; Ricardo Toledo edit  doi
openurl 
  Title (up) Multi-Spectral Stereo Odometry Type Journal Article
  Year 2015 Publication IEEE Transactions on Intelligent Transportation Systems Abbreviated Journal TITS  
  Volume 16 Issue 3 Pages 1210-1224  
  Keywords Egomotion estimation; feature matching; multispectral odometry (MO); optical flow; stereo odometry; thermal imagery  
  Abstract In this paper, we investigate the problem of visual odometry for ground vehicles based on the simultaneous utilization of multispectral cameras. It encompasses a stereo rig composed of an optical (visible) and thermal sensors. The novelty resides in the localization of the cameras as a stereo setup rather
than two monocular cameras of different spectrums. To the best of our knowledge, this is the first time such task is attempted. Log-Gabor wavelets at different orientations and scales are used to extract interest points from both images. These are then described using a combination of frequency and spatial information within the local neighborhood. Matches between the pairs of multimodal images are computed using the cosine similarity function based
on the descriptors. Pyramidal Lucas–Kanade tracker is also introduced to tackle temporal feature matching within challenging sequences of the data sets. The vehicle egomotion is computed from the triangulated 3-D points corresponding to the matched features. A windowed version of bundle adjustment incorporating
Gauss–Newton optimization is utilized for motion estimation. An outlier removal scheme is also included within the framework to deal with outliers. Multispectral data sets were generated and used as test bed. They correspond to real outdoor scenarios captured using our multimodal setup. Finally, detailed results validating the proposed strategy are illustrated.
 
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  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1524-9050 ISBN Medium  
  Area Expedition Conference  
  Notes ADAS; 600.055; 600.076 Approved no  
  Call Number Admin @ si @ MAS2015a Serial 2533  
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Author Sergio Vera; Debora Gil; Antonio Lopez; Miguel Angel Gonzalez Ballester edit   pdf
url  openurl
  Title (up) Multilocal Creaseness Measure Type Journal
  Year 2012 Publication The Insight Journal Abbreviated Journal IJ  
  Volume Issue Pages  
  Keywords Ridges, Valley, Creaseness, Structure Tensor, Skeleton,  
  Abstract This document describes the implementation using the Insight Toolkit of an algorithm for detecting creases (ridges and valleys) in N-dimensional images, based on the Local Structure Tensor of the image. In addition to the filter used to calculate the creaseness image, a filter for the computation of the structure tensor is also included in this submission.  
  Address  
  Corporate Author Alma IT Systems Thesis  
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  Language english Summary Language english Original Title  
  Series Editor Series Title Abbreviated Series Title  
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  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes IAM;ADAS; Approved no  
  Call Number IAM @ iam @ VGL2012 Serial 1840  
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Author Yi Xiao; Felipe Codevilla; Akhil Gurram; Onay Urfalioglu; Antonio Lopez edit   pdf
url  doi
openurl 
  Title (up) Multimodal end-to-end autonomous driving Type Journal Article
  Year 2020 Publication IEEE Transactions on Intelligent Transportation Systems Abbreviated Journal TITS  
  Volume Issue Pages 1-11  
  Keywords  
  Abstract A crucial component of an autonomous vehicle (AV) is the artificial intelligence (AI) is able to drive towards a desired destination. Today, there are different paradigms addressing the development of AI drivers. On the one hand, we find modular pipelines, which divide the driving task into sub-tasks such as perception and maneuver planning and control. On the other hand, we find end-to-end driving approaches that try to learn a direct mapping from input raw sensor data to vehicle control signals. The later are relatively less studied, but are gaining popularity since they are less demanding in terms of sensor data annotation. This paper focuses on end-to-end autonomous driving. So far, most proposals relying on this paradigm assume RGB images as input sensor data. However, AVs will not be equipped only with cameras, but also with active sensors providing accurate depth information (e.g., LiDARs). Accordingly, this paper analyses whether combining RGB and depth modalities, i.e. using RGBD data, produces better end-to-end AI drivers than relying on a single modality. We consider multimodality based on early, mid and late fusion schemes, both in multisensory and single-sensor (monocular depth estimation) settings. Using the CARLA simulator and conditional imitation learning (CIL), we show how, indeed, early fusion multimodality outperforms single-modality.  
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  Notes ADAS Approved no  
  Call Number Admin @ si @ XCG2020 Serial 3490  
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Author Henry Velesaca; Gisel Bastidas-Guacho; Mohammad Rouhani; Angel Sappa edit  url
openurl 
  Title (up) 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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  Notes MSIAU;ADAS Approved no  
  Call Number Admin @ si @ VBR2024 Serial 3997  
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Author Miguel Oliveira; Victor Santos; Angel Sappa edit  doi
openurl 
  Title (up) Multimodal Inverse Perspective Mapping Type Journal Article
  Year 2015 Publication Information Fusion Abbreviated Journal IF  
  Volume 24 Issue Pages 108–121  
  Keywords Inverse perspective mapping; Multimodal sensor fusion; Intelligent vehicles  
  Abstract Over the past years, inverse perspective mapping has been successfully applied to several problems in the field of Intelligent Transportation Systems. In brief, the method consists of mapping images to a new coordinate system where perspective effects are removed. The removal of perspective associated effects facilitates road and obstacle detection and also assists in free space estimation. There is, however, a significant limitation in the inverse perspective mapping: the presence of obstacles on the road disrupts the effectiveness of the mapping. The current paper proposes a robust solution based on the use of multimodal sensor fusion. Data from a laser range finder is fused with images from the cameras, so that the mapping is not computed in the regions where obstacles are present. As shown in the results, this considerably improves the effectiveness of the algorithm and reduces computation time when compared with the classical inverse perspective mapping. Furthermore, the proposed approach is also able to cope with several cameras with different lenses or image resolutions, as well as dynamic viewpoints.  
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  Notes ADAS; 600.055; 600.076 Approved no  
  Call Number Admin @ si @ OSS2015c Serial 2532  
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Author Fernando Barrera; Felipe Lumbreras; Angel Sappa edit   pdf
doi  openurl
  Title (up) 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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Author Jaume Amores edit   pdf
doi  openurl
  Title (up) Multiple Instance Classification: review, taxonomy and comparative study Type Journal Article
  Year 2013 Publication Artificial Intelligence Abbreviated Journal AI  
  Volume 201 Issue Pages 81-105  
  Keywords Multi-instance learning; Codebook; Bag-of-Words  
  Abstract Multiple Instance Learning (MIL) has become an important topic in the pattern recognition community, and many solutions to this problemhave been proposed until now. Despite this fact, there is a lack of comparative studies that shed light into the characteristics and behavior of the different methods. In this work we provide such an analysis focused on the classification task (i.e.,leaving out other learning tasks such as regression). In order to perform our study, we implemented
fourteen methods grouped into three different families. We analyze the performance of the approaches across a variety of well-known databases, and we also study their behavior in synthetic scenarios in order to highlight their characteristics. As a result of this analysis, we conclude that methods that extract global bag-level information show a clearly superior performance in general. In this sense, the analysis permits us to understand why some types of methods are more successful than others, and it permits us to establish guidelines in the design of new MIL
methods.
 
  Address  
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  Publisher Elsevier Science Publishers Ltd. Essex, UK Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0004-3702 ISBN Medium  
  Area Expedition Conference  
  Notes ADAS; 601.042; 600.057 Approved no  
  Call Number Admin @ si @ Amo2013 Serial 2273  
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Author Jose Carlos Rubio; Joan Serrat; Antonio Lopez; Daniel Ponsa edit   pdf
url  doi
openurl 
  Title (up) Multiple target tracking for intelligent headlights control Type Journal Article
  Year 2012 Publication IEEE Transactions on Intelligent Transportation Systems Abbreviated Journal TITS  
  Volume 13 Issue 2 Pages 594-605  
  Keywords Intelligent Headlights  
  Abstract Intelligent vehicle lighting systems aim at automatically regulating the headlights' beam to illuminate as much of the road ahead as possible while avoiding dazzling other drivers. A key component of such a system is computer vision software that is able to distinguish blobs due to vehicles' headlights and rear lights from those due to road lamps and reflective elements such as poles and traffic signs. In a previous work, we have devised a set of specialized supervised classifiers to make such decisions based on blob features related to its intensity and shape. Despite the overall good performance, there remain challenging that have yet to be solved: notably, faint and tiny blobs corresponding to quite distant vehicles. In fact, for such distant blobs, classification decisions can be taken after observing them during a few frames. Hence, incorporating tracking could improve the overall lighting system performance by enforcing the temporal consistency of the classifier decision. Accordingly, this paper focuses on the problem of constructing blob tracks, which is actually one of multiple-target tracking (MTT), but under two special conditions: We have to deal with frequent occlusions, as well as blob splits and merges. We approach it in a novel way by formulating the problem as a maximum a posteriori inference on a Markov random field. The qualitative (in video form) and quantitative evaluation of our new MTT method shows good tracking results. In addition, we will also see that the classification performance of the problematic blobs improves due to the proposed MTT algorithm.  
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  Series Volume Series Issue Edition  
  ISSN 1524-9050 ISBN Medium  
  Area Expedition Conference  
  Notes ADAS Approved no  
  Call Number Admin @ si @ RLP2012; ADAS @ adas @ rsl2012g Serial 1877  
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