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Author Fei Yang; Luis Herranz; Joost van de Weijer; Jose Antonio Iglesias; Antonio Lopez; Mikhail Mozerov edit   pdf
url  doi
openurl 
  Title Variable Rate Deep Image Compression with Modulated Autoencoder Type Journal Article
  Year 2020 Publication IEEE Signal Processing Letters Abbreviated Journal SPL  
  Volume (up) 27 Issue Pages 331-335  
  Keywords  
  Abstract Variable rate is a requirement for flexible and adaptable image and video compression. However, deep image compression methods (DIC) are optimized for a single fixed rate-distortion (R-D) tradeoff. While this can be addressed by training multiple models for different tradeoffs, the memory requirements increase proportionally to the number of models. Scaling the bottleneck representation of a shared autoencoder can provide variable rate compression with a single shared autoencoder. However, the R-D performance using this simple mechanism degrades in low bitrates, and also shrinks the effective range of bitrates. To address these limitations, we formulate the problem of variable R-D optimization for DIC, and propose modulated autoencoders (MAEs), where the representations of a shared autoencoder are adapted to the specific R-D tradeoff via a modulation network. Jointly training this modulated autoencoder and the modulation network provides an effective way to navigate the R-D operational curve. Our experiments show that the proposed method can achieve almost the same R-D performance of independent models with significantly fewer parameters.  
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  Area Expedition Conference  
  Notes LAMP; ADAS; 600.141; 600.120; 600.118 Approved no  
  Call Number Admin @ si @ YHW2020 Serial 3346  
Permanent link to this record
 
 
Author Fadi Dornaika; Angel Sappa edit  doi
openurl 
  Title Rigid and Non-rigid Face Motion Tracking by Aligning Texture Maps and Stereo 3D Models Type Journal Article
  Year 2007 Publication Pattern Recognition Letters Abbreviated Journal PRL  
  Volume (up) 28 Issue 15 Pages 2116-2126  
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  Area Expedition Conference  
  Notes ADAS Approved no  
  Call Number ADAS @ adas @ DoS2007c Serial 877  
Permanent link to this record
 
 
Author Carme Julia; Angel Sappa; Felipe Lumbreras; Joan Serrat; Antonio Lopez edit   pdf
doi  openurl
  Title An Iterative Multiresolution Scheme for SFM with Missing Data: single and multiple object scenes Type Journal Article
  Year 2010 Publication Image and Vision Computing Abbreviated Journal IMAVIS  
  Volume (up) 28 Issue 1 Pages 164-176  
  Keywords  
  Abstract Most of the techniques proposed for tackling the Structure from Motion problem (SFM) cannot deal with high percentages of missing data in the matrix of trajectories. Furthermore, an additional problem should be faced up when working with multiple object scenes: the rank of the matrix of trajectories should be estimated. This paper presents an iterative multiresolution scheme for SFM with missing data to be used in both the single and multiple object cases. The proposed scheme aims at recovering missing entries in the original input matrix. The objective is to improve the results by applying a factorization technique to the partially or totally filled in matrix instead of to the original input one. Experimental results obtained with synthetic and real data sequences, containing single and multiple objects, are presented to show the viability of the proposed approach.  
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  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0262-8856 ISBN Medium  
  Area Expedition Conference  
  Notes ADAS Approved no  
  Call Number ADAS @ adas @ JSL2010 Serial 1278  
Permanent link to this record
 
 
Author Fadi Dornaika; Angel Sappa edit  doi
openurl 
  Title Instantaneous 3D motion from image derivatives using the Least Trimmed Square Regression Type Journal Article
  Year 2009 Publication Pattern Recognition Letters Abbreviated Journal PRL  
  Volume (up) 30 Issue 5 Pages 535–543  
  Keywords  
  Abstract This paper presents a new technique to the instantaneous 3D motion estimation. The main contributions are as follows. First, we show that the 3D camera or scene velocity can be retrieved from image derivatives only assuming that the scene contains a dominant plane. Second, we propose a new robust algorithm that simultaneously provides the Least Trimmed Square solution and the percentage of inliers-the non-contaminated data. Experiments on both synthetic and real image sequences demonstrated the effectiveness of the developed method. Those experiments show that the new robust approach can outperform classical robust schemes.  
  Address  
  Corporate Author Thesis  
  Publisher Elsevier Science Inc. Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0167-8655 ISBN Medium  
  Area Expedition Conference  
  Notes ADAS Approved no  
  Call Number ADAS @ adas @ DoS2009a Serial 1115  
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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 (up) 32 Issue 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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Author Daniel Ponsa; Joan Serrat; Antonio Lopez edit   pdf
doi  openurl
  Title On-board image-based vehicle detection and tracking Type Journal Article
  Year 2011 Publication Transactions of the Institute of Measurement and Control Abbreviated Journal TIM  
  Volume (up) 33 Issue 7 Pages 783-805  
  Keywords vehicle detection  
  Abstract In this paper we present a computer vision system for daytime vehicle detection and localization, an essential step in the development of several types of advanced driver assistance systems. It has a reduced processing time and high accuracy thanks to the combination of vehicle detection with lane-markings estimation and temporal tracking of both vehicles and lane markings. Concerning vehicle detection, our main contribution is a frame scanning process that inspects images according to the geometry of image formation, and with an Adaboost-based detector that is robust to the variability in the different vehicle types (car, van, truck) and lighting conditions. In addition, we propose a new method to estimate the most likely three-dimensional locations of vehicles on the road ahead. With regards to the lane-markings estimation component, we have two main contributions. First, we employ a different image feature to the other commonly used edges: we use ridges, which are better suited to this problem. Second, we adapt RANSAC, a generic robust estimation method, to fit a parametric model of a pair of lane markings to the image features. We qualitatively assess our vehicle detection system in sequences captured on several road types and under very different lighting conditions. The processed videos are available on a web page associated with this paper. A quantitative evaluation of the system has shown quite accurate results (a low number of false positives and negatives) at a reasonable computation time.  
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  Series Editor Series Title Abbreviated Series Title  
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  Notes ADAS Approved no  
  Call Number ADAS @ adas @ PSL2011 Serial 1413  
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Author Carme Julia; Angel Sappa; Felipe Lumbreras; Joan Serrat; Antonio Lopez edit   pdf
url  openurl
  Title An iterative multiresolution scheme for SFM with missing data Type Journal Article
  Year 2009 Publication Journal of Mathematical Imaging and Vision Abbreviated Journal JMIV  
  Volume (up) 34 Issue 3 Pages 240–258  
  Keywords  
  Abstract Several techniques have been proposed for tackling the Structure from Motion problem through factorization in the case of missing data. However, when the percentage of unknown data is high, most of them may not perform as well as expected. Focussing on this problem, an iterative multiresolution scheme, which aims at recovering missing entries in the originally given input matrix, is proposed. Information recovered following a coarse-to-fine strategy is used for filling in the missing entries. The objective is to recover, as much as possible, missing data in the given matrix.
Thus, when a factorization technique is applied to the partially or totally filled in matrix, instead of to the originally given input one, better results will be obtained. An evaluation study about the robustness to missing and noisy data is reported.
Experimental results obtained with synthetic and real video sequences are presented to show the viability of the proposed approach.		  
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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 Approved no  
  Call Number ADAS @ adas @ JSL2009a Serial 1163  
Permanent link to this record
 
 
Author 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 (up) 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  
Permanent link to this record
 
 
Author Jiaolong Xu; Sebastian Ramos; David Vazquez; Antonio Lopez edit   pdf
doi  openurl
  Title Domain Adaptation of Deformable Part-Based Models Type Journal Article
  Year 2014 Publication IEEE Transactions on Pattern Analysis and Machine Intelligence Abbreviated Journal TPAMI  
  Volume (up) 36 Issue 12 Pages 2367-2380  
  Keywords Domain Adaptation; Pedestrian Detection  
  Abstract The accuracy of object classifiers can significantly drop when the training data (source domain) and the application scenario (target domain) have inherent differences. Therefore, adapting the classifiers to the scenario in which they must operate is of paramount importance. We present novel domain adaptation (DA) methods for object detection. As proof of concept, we focus on adapting the state-of-the-art deformable part-based model (DPM) for pedestrian detection. We introduce an adaptive structural SVM (A-SSVM) that adapts a pre-learned classifier between different domains. By taking into account the inherent structure in feature space (e.g., the parts in a DPM), we propose a structure-aware A-SSVM (SA-SSVM). Neither A-SSVM nor SA-SSVM needs to revisit the source-domain training data to perform the adaptation. Rather, a low number of target-domain training examples (e.g., pedestrians) are used. To address the scenario where there are no target-domain annotated samples, we propose a self-adaptive DPM based on a self-paced learning (SPL) strategy and a Gaussian Process Regression (GPR). Two types of adaptation tasks are assessed: from both synthetic pedestrians and general persons (PASCAL VOC) to pedestrians imaged from an on-board camera. Results show that our proposals avoid accuracy drops as high as 15 points when comparing adapted and non-adapted detectors.  
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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; 600.057; 600.054; 601.217; 600.076 Approved no  
  Call Number ADAS @ adas @ XRV2014b Serial 2436  
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Author David Vazquez; Javier Marin; Antonio Lopez; Daniel Ponsa; David Geronimo edit   pdf
doi  openurl
  Title Virtual and Real World Adaptation for Pedestrian Detection Type Journal Article
  Year 2014 Publication IEEE Transactions on Pattern Analysis and Machine Intelligence Abbreviated Journal TPAMI  
  Volume (up) 36 Issue 4 Pages 797-809  
  Keywords Domain Adaptation; Pedestrian Detection  
  Abstract Pedestrian detection is of paramount interest for many applications. Most promising detectors rely on discriminatively learnt classifiers, i.e., trained with annotated samples. However, the annotation step is a human intensive and subjective task worth to be minimized. By using virtual worlds we can automatically obtain precise and rich annotations. Thus, we face the question: can a pedestrian appearance model learnt in realistic virtual worlds work successfully for pedestrian detection in realworld images?. Conducted experiments show that virtual-world based training can provide excellent testing accuracy in real world, but it can also suffer the dataset shift problem as real-world based training does. Accordingly, we have designed a domain adaptation framework, V-AYLA, in which we have tested different techniques to collect a few pedestrian samples from the target domain (real world) and combine them with the many examples of the source domain (virtual world) in order to train a domain adapted pedestrian classifier that will operate in the target domain. V-AYLA reports the same detection accuracy than when training with many human-provided pedestrian annotations and testing with real-world images of the same domain. To the best of our knowledge, this is the first work demonstrating adaptation of virtual and real worlds for developing an object detector.  
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  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0162-8828 ISBN Medium  
  Area Expedition Conference  
  Notes ADAS; 600.057; 600.054; 600.076 Approved no  
  Call Number ADAS @ adas @ VML2014 Serial 2275  
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