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Author Monica Piñol; Angel Sappa; Ricardo Toledo edit  doi
openurl 
  Title Adaptive Feature Descriptor Selection based on a Multi-Table Reinforcement Learning Strategy Type Journal Article
  Year 2015 Publication Neurocomputing Abbreviated Journal NEUCOM  
  Volume 150 Issue (down) A Pages 106–115  
  Keywords Reinforcement learning; Q-learning; Bag of features; Descriptors  
  Abstract This paper presents and evaluates a framework to improve the performance of visual object classification methods, which are based on the usage of image feature descriptors as inputs. The goal of the proposed framework is to learn the best descriptor for each image in a given database. This goal is reached by means of a reinforcement learning process using the minimum information. The visual classification system used to demonstrate the proposed framework is based on a bag of features scheme, and the reinforcement learning technique is implemented through the Q-learning approach. The behavior of the reinforcement learning with different state definitions is evaluated. Additionally, a method that combines all these states is formulated in order to select the optimal state. Finally, the chosen actions are obtained from the best set of image descriptors in the literature: PHOW, SIFT, C-SIFT, SURF and Spin. Experimental results using two public databases (ETH and COIL) are provided showing both the validity of the proposed approach and comparisons with state of the art. In all the cases the best results are obtained with the proposed approach.  
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  Notes ADAS; 600.055; 600.076 Approved no  
  Call Number Admin @ si @ PST2015 Serial 2473  
Permanent link to this record
 
 
Author Fadi Dornaika; Angel Sappa edit  url
doi  openurl
  Title A Featureless and Stochastic Approach to On-board Stereo Vision System Pose Type Journal Article
  Year 2009 Publication Image and Vision Computing Abbreviated Journal IMAVIS  
  Volume 27 Issue (down) 9 Pages 1382–1393  
  Keywords On-board stereo vision system; Pose estimation; Featureless approach; Particle filtering; Image warping  
  Abstract This paper presents a direct and stochastic technique for real-time estimation of on-board stereo head’s position and orientation. Unlike existing works which rely on feature extraction either in the image domain or in 3D space, our proposed approach directly estimates the unknown parameters from the stream of stereo pairs’ brightness. The pose parameters are tracked using the particle filtering framework which implicitly enforces the smoothness constraints on the estimated parameters. The proposed technique can be used with a driver assistance applications as well as with augmented reality applications. Extended experiments on urban environments with different road geometries are presented. Comparisons with a 3D data-based approach are presented. Moreover, we provide a performance study aiming at evaluating the accuracy of the proposed approach.  
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  Notes ADAS Approved no  
  Call Number ADAS @ adas @ DoS2009b Serial 1152  
Permanent link to this record
 
 
Author Debora Gil; Aura Hernandez-Sabate; Mireia Brunat;Steven Jansen; Jordi Martinez-Vilalta edit   pdf
doi  openurl
  Title Structure-preserving smoothing of biomedical images Type Journal Article
  Year 2011 Publication Pattern Recognition Abbreviated Journal PR  
  Volume 44 Issue (down) 9 Pages 1842-1851  
  Keywords Non-linear smoothing; Differential geometry; Anatomical structures; segmentation; Cardiac magnetic resonance; Computerized tomography  
  Abstract Smoothing of biomedical images should preserve gray-level transitions between adjacent tissues, while restoring contours consistent with anatomical structures. Anisotropic diffusion operators are based on image appearance discontinuities (either local or contextual) and might fail at weak inter-tissue transitions. Meanwhile, the output of block-wise and morphological operations is prone to present a block structure due to the shape and size of the considered pixel neighborhood. In this contribution, we use differential geometry concepts to define a diffusion operator that restricts to image consistent level-sets. In this manner, the final state is a non-uniform intensity image presenting homogeneous inter-tissue transitions along anatomical structures, while smoothing intra-structure texture. Experiments on different types of medical images (magnetic resonance, computerized tomography) illustrate its benefit on a further process (such as segmentation) of images.  
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  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0031-3203 ISBN Medium  
  Area Expedition Conference  
  Notes IAM; ADAS Approved no  
  Call Number IAM @ iam @ GHB2011 Serial 1526  
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Author Cristhian Aguilera; Fernando Barrera; Felipe Lumbreras; Angel Sappa; Ricardo Toledo edit   pdf
doi  openurl
  Title Multispectral Image Feature Points Type Journal Article
  Year 2012 Publication Sensors Abbreviated Journal SENS  
  Volume 12 Issue (down) 9 Pages 12661-12672  
  Keywords multispectral image descriptor; color and infrared images; feature point descriptor  
  Abstract Far-Infrared and Visible Spectrum images. It allows matching interest points on images of the same scene but acquired in different spectral bands. Initially, points of interest are detected on both images through a SIFT-like based scale space representation. Then, these points are characterized using an Edge Oriented Histogram (EOH) descriptor. Finally, points of interest from multispectral images are matched by finding nearest couples using the information from the descriptor. The provided experimental results and comparisons with similar methods show both the validity of the proposed approach as well as the improvements it offers with respect to the current state-of-the-art.  
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  Notes ADAS Approved no  
  Call Number Admin @ si @ ABL2012 Serial 2154  
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Author Naveen Onkarappa; Angel Sappa edit  doi
openurl 
  Title Synthetic sequences and ground-truth flow field generation for algorithm validation Type Journal Article
  Year 2015 Publication Multimedia Tools and Applications Abbreviated Journal MTAP  
  Volume 74 Issue (down) 9 Pages 3121-3135  
  Keywords Ground-truth optical flow; Synthetic sequence; Algorithm validation  
  Abstract Research in computer vision is advancing by the availability of good datasets that help to improve algorithms, validate results and obtain comparative analysis. The datasets can be real or synthetic. For some of the computer vision problems such as optical flow it is not possible to obtain ground-truth optical flow with high accuracy in natural outdoor real scenarios directly by any sensor, although it is possible to obtain ground-truth data of real scenarios in a laboratory setup with limited motion. In this difficult situation computer graphics offers a viable option for creating realistic virtual scenarios. In the current work we present a framework to design virtual scenes and generate sequences as well as ground-truth flow fields. Particularly, we generate a dataset containing sequences of driving scenarios. The sequences in the dataset vary in different speeds of the on-board vision system, different road textures, complex motion of vehicle and independent moving vehicles in the scene. This dataset enables analyzing and adaptation of existing optical flow methods, and leads to invention of new approaches particularly for driver assistance systems.  
  Address  
  Corporate Author Thesis  
  Publisher Springer US Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1380-7501 ISBN Medium  
  Area Expedition Conference  
  Notes ADAS; 600.055; 601.215; 600.076 Approved no  
  Call Number Admin @ si @ OnS2014b Serial 2472  
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Author Adrien Gaidon; Antonio Lopez; Florent Perronnin edit  url
openurl 
  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 (down) 9 Pages 899–901  
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  Notes ADAS; 600.118 Approved no  
  Call Number Admin @ si @ GLP2018 Serial 3180  
Permanent link to this record
 
 
Author Jose L. Gomez; Gabriel Villalonga; Antonio Lopez edit   pdf
url  openurl
  Title Co-Training for Deep Object Detection: Comparing Single-Modal and Multi-Modal Approaches Type Journal Article
  Year 2021 Publication Sensors Abbreviated Journal SENS  
  Volume 21 Issue (down) 9 Pages 3185  
  Keywords co-training; multi-modality; vision-based object detection; ADAS; self-driving  
  Abstract Top-performing computer vision models are powered by convolutional neural networks (CNNs). Training an accurate CNN highly depends on both the raw sensor data and their associated ground truth (GT). Collecting such GT is usually done through human labeling, which is time-consuming and does not scale as we wish. This data-labeling bottleneck may be intensified due to domain shifts among image sensors, which could force per-sensor data labeling. In this paper, we focus on the use of co-training, a semi-supervised learning (SSL) method, for obtaining self-labeled object bounding boxes (BBs), i.e., the GT to train deep object detectors. In particular, we assess the goodness of multi-modal co-training by relying on two different views of an image, namely, appearance (RGB) and estimated depth (D). Moreover, we compare appearance-based single-modal co-training with multi-modal. Our results suggest that in a standard SSL setting (no domain shift, a few human-labeled data) and under virtual-to-real domain shift (many virtual-world labeled data, no human-labeled data) multi-modal co-training outperforms single-modal. In the latter case, by performing GAN-based domain translation both co-training modalities are on par, at least when using an off-the-shelf depth estimation model not specifically trained on the translated images.  
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  Notes ADAS; 600.118 Approved no  
  Call Number Admin @ si @ GVL2021 Serial 3562  
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Author Katerine Diaz; Francesc J. Ferri; W. Diaz edit  doi
openurl 
  Title Incremental Generalized Discriminative Common Vectors for Image Classification Type Journal Article
  Year 2015 Publication IEEE Transactions on Neural Networks and Learning Systems Abbreviated Journal TNNLS  
  Volume 26 Issue (down) 8 Pages 1761 - 1775  
  Keywords  
  Abstract Subspace-based methods have become popular due to their ability to appropriately represent complex data in such a way that both dimensionality is reduced and discriminativeness is enhanced. Several recent works have concentrated on the discriminative common vector (DCV) method and other closely related algorithms also based on the concept of null space. In this paper, we present a generalized incremental formulation of the DCV methods, which allows the update of a given model by considering the addition of new examples even from unseen classes. Having efficient incremental formulations of well-behaved batch algorithms allows us to conveniently adapt previously trained classifiers without the need of recomputing them from scratch. The proposed generalized incremental method has been empirically validated in different case studies from different application domains (faces, objects, and handwritten digits) considering several different scenarios in which new data are continuously added at different rates starting from an initial model.  
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  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 2162-237X ISBN Medium  
  Area Expedition Conference  
  Notes ADAS; 600.076 Approved no  
  Call Number Admin @ si @ DFD2015 Serial 2547  
Permanent link to this record
 
 
Author Akhil Gurram; Ahmet Faruk Tuna; Fengyi Shen; Onay Urfalioglu; Antonio Lopez edit   pdf
doi  openurl
  Title Monocular Depth Estimation through Virtual-world Supervision and Real-world SfM Self-Supervision Type Journal Article
  Year 2021 Publication IEEE Transactions on Intelligent Transportation Systems Abbreviated Journal TITS  
  Volume 23 Issue (down) 8 Pages 12738-12751  
  Keywords  
  Abstract Depth information is essential for on-board perception in autonomous driving and driver assistance. Monocular depth estimation (MDE) is very appealing since it allows for appearance and depth being on direct pixelwise correspondence without further calibration. Best MDE models are based on Convolutional Neural Networks (CNNs) trained in a supervised manner, i.e., assuming pixelwise ground truth (GT). Usually, this GT is acquired at training time through a calibrated multi-modal suite of sensors. However, also using only a monocular system at training time is cheaper and more scalable. This is possible by relying on structure-from-motion (SfM) principles to generate self-supervision. Nevertheless, problems of camouflaged objects, visibility changes, static-camera intervals, textureless areas, and scale ambiguity, diminish the usefulness of such self-supervision. In this paper, we perform monocular depth estimation by virtual-world supervision (MonoDEVS) and real-world SfM self-supervision. We compensate the SfM self-supervision limitations by leveraging virtual-world images with accurate semantic and depth supervision and addressing the virtual-to-real domain gap. Our MonoDEVSNet outperforms previous MDE CNNs trained on monocular and even stereo sequences.  
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  Notes ADAS; 600.118 Approved no  
  Call Number Admin @ si @ GTS2021 Serial 3598  
Permanent link to this record
 
 
Author David Geronimo; Antonio Lopez; Angel Sappa; Thorsten Graf edit   pdf
url  doi
openurl 
  Title Survey on Pedestrian Detection for Advanced Driver Assistance Systems Type Journal Article
  Year 2010 Publication IEEE Transaction on Pattern Analysis and Machine Intelligence Abbreviated Journal TPAMI  
  Volume 32 Issue (down) 7 Pages 1239–1258  
  Keywords ADAS, pedestrian detection, on-board vision, survey  
  Abstract Advanced driver assistance systems (ADASs), and particularly pedestrian protection systems (PPSs), have become an active research area aimed at improving traffic safety. The major challenge of PPSs is the development of reliable on-board pedestrian detection systems. Due to the varying appearance of pedestrians (e.g., different clothes, changing size, aspect ratio, and dynamic shape) and the unstructured environment, it is very difficult to cope with the demanded robustness of this kind of system. Two problems arising in this research area are the lack of public benchmarks and the difficulty in reproducing many of the proposed methods, which makes it difficult to compare the approaches. As a result, surveying the literature by enumerating the proposals one-after-another is not the most useful way to provide a comparative point of view. Accordingly, we present a more convenient strategy to survey the different approaches. We divide the problem of detecting pedestrians from images into different processing steps, each with attached responsibilities. Then, the different proposed methods are analyzed and classified with respect to each processing stage, favoring a comparative viewpoint. Finally, discussion of the important topics is presented, putting special emphasis on the future needs and challenges.  
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  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0162-8828 ISBN Medium  
  Area Expedition Conference  
  Notes ADAS Approved no  
  Call Number ADAS @ adas @ GLS2010 Serial 1340  
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