toggle visibility Search & Display Options

Select All    Deselect All
 |   | 
Details
  Records Links
Author Carme Julia; Felipe Lumbreras; Angel Sappa edit  doi
openurl 
  Title A Factorization-based Approach to Photometric Stereo Type Journal Article
  Year 2011 Publication International Journal of Imaging Systems and Technology Abbreviated Journal IJIST  
  Volume 21 Issue 1 Pages (down) 115-119  
  Keywords  
  Abstract This article presents an adaptation of a factorization technique to tackle the photometric stereo problem. That is to recover the surface normals and reflectance of an object from a set of images obtained under different lighting conditions. The main contribution of the proposed approach is to consider pixels in shadow and saturated regions as missing data, in order to reduce their influence to the result. Concretely, an adapted Alternation technique is used to deal with missing data. Experimental results considering both synthetic and real images show the viability of the proposed factorization-based strategy. © 2011 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 21, 115–119, 2011.  
  Address  
  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 ADAS Approved no  
  Call Number Admin @ si @ JLS2011; ADAS @ adas @ Serial 1711  
Permanent link to this record
 

 
Author Miguel Oliveira; Victor Santos; Angel Sappa; P. Dias; A. Moreira edit   pdf
url  openurl
  Title Incremental texture mapping for autonomous driving Type Journal Article
  Year 2016 Publication Robotics and Autonomous Systems Abbreviated Journal RAS  
  Volume 84 Issue Pages (down) 113-128  
  Keywords Scene reconstruction; Autonomous driving; Texture mapping  
  Abstract Autonomous vehicles have a large number of on-board sensors, not only for providing coverage all around the vehicle, but also to ensure multi-modality in the observation of the scene. Because of this, it is not trivial to come up with a single, unique representation that feeds from the data given by all these sensors. We propose an algorithm which is capable of mapping texture collected from vision based sensors onto a geometric description of the scenario constructed from data provided by 3D sensors. The algorithm uses a constrained Delaunay triangulation to produce a mesh which is updated using a specially devised sequence of operations. These enforce a partial configuration of the mesh that avoids bad quality textures and ensures that there are no gaps in the texture. Results show that this algorithm is capable of producing fine quality textures.  
  Address  
  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 ADAS; 600.086 Approved no  
  Call Number Admin @ si @ OSS2016b Serial 2912  
Permanent link to this record
 

 
Author Miguel Oliveira; Victor Santos; Angel Sappa edit  doi
openurl 
  Title Multimodal Inverse Perspective Mapping Type Journal Article
  Year 2015 Publication Information Fusion Abbreviated Journal IF  
  Volume 24 Issue Pages (down) 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.  
  Address  
  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 ADAS; 600.055; 600.076 Approved no  
  Call Number Admin @ si @ OSS2015c Serial 2532  
Permanent link to this record
 

 
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 A Pages (down) 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.  
  Address  
  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 ADAS; 600.055; 600.076 Approved no  
  Call Number Admin @ si @ PST2015 Serial 2473  
Permanent link to this record
 

 
Author 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 (down) 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 ADAS; 600.086; 600.130; 600.122 Approved no  
  Call Number Admin @ si @ Serial 3708  
Permanent link to this record
 

 
Author Antonio Lopez; Gabriel Villalonga; Laura Sellart; German Ros; David Vazquez; Jiaolong Xu; Javier Marin; Azadeh S. Mozafari edit   pdf
url  openurl
  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 (down) 102-118  
  Keywords  
  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.  
  Address  
  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 ADAS; 600.118 Approved no  
  Call Number Admin @ si @ LVS2017 Serial 2985  
Permanent link to this record
 

 
Author Akhil Gurram; Onay Urfalioglu; Ibrahim Halfaoui; Fahd Bouzaraa; Antonio Lopez edit  url
doi  openurl
  Title Semantic Monocular Depth Estimation Based on Artificial Intelligence Type Journal Article
  Year 2020 Publication IEEE Intelligent Transportation Systems Magazine Abbreviated Journal ITSM  
  Volume 13 Issue 4 Pages (down) 99-103  
  Keywords  
  Abstract Depth estimation provides essential information to perform autonomous driving and driver assistance. A promising line of work consists of introducing additional semantic information about the traffic scene when training CNNs for depth estimation. In practice, this means that the depth data used for CNN training is complemented with images having pixel-wise semantic labels where the same raw training data is associated with both types of ground truth, i.e., depth and semantic labels. The main contribution of this paper is to show that this hard constraint can be circumvented, i.e., that we can train CNNs for depth estimation by leveraging the depth and semantic information coming from heterogeneous datasets. In order to illustrate the benefits of our approach, we combine KITTI depth and Cityscapes semantic segmentation datasets, outperforming state-of-the-art results on monocular depth estimation.  
  Address  
  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 ADAS; 600.124; 600.118 Approved no  
  Call Number Admin @ si @ GUH2019 Serial 3306  
Permanent link to this record
 

 
Author Katerine Diaz; Aura Hernandez-Sabate; Antonio Lopez edit   pdf
doi  openurl
  Title A reduced feature set for driver head pose estimation Type Journal Article
  Year 2016 Publication Applied Soft Computing Abbreviated Journal ASOC  
  Volume 45 Issue Pages (down) 98-107  
  Keywords Head pose estimation; driving performance evaluation; subspace based methods; linear regression  
  Abstract Evaluation of driving performance is of utmost importance in order to reduce road accident rate. Since driving ability includes visual-spatial and operational attention, among others, head pose estimation of the driver is a crucial indicator of driving performance. This paper proposes a new automatic method for coarse and fine head's yaw angle estimation of the driver. We rely on a set of geometric features computed from just three representative facial keypoints, namely the center of the eyes and the nose tip. With these geometric features, our method combines two manifold embedding methods and a linear regression one. In addition, the method has a confidence mechanism to decide if the classification of a sample is not reliable. The approach has been tested using the CMU-PIE dataset and our own driver dataset. Despite the very few facial keypoints required, the results are comparable to the state-of-the-art techniques. The low computational cost of the method and its robustness makes feasible to integrate it in massive consume devices as a real time application.  
  Address  
  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 ADAS; 600.085; 600.076; Approved no  
  Call Number Admin @ si @ DHL2016 Serial 2760  
Permanent link to this record
 

 
Author Enric Marti; Carme Julia; Debora Gil edit  doi
openurl 
  Title A PBL Experience in the Teaching of Computer Graphics Type Journal Article
  Year 2006 Publication Computer Graphics Forum Abbreviated Journal CGF  
  Volume 25 Issue 1 Pages (down) 95-103  
  Keywords  
  Abstract Project-Based Learning (PBL) is an educational strategy to improve student’s learning capability that, in recent years, has had a progressive acceptance in undergraduate studies. This methodology is based on solving a problem or project in a student working group. In this way, PBL focuses on learning the necessary tools to correctly find a solution to given problems. Since the learning initiative is transferred to the student, the PBL method promotes students own abilities. This allows a better assessment of the true workload that carries out the student in the subject. It follows that the methodology conforms to the guidelines of the Bologna document, which quantifies the student workload in a subject by means of the European credit transfer system (ECTS). PBL is currently applied in undergraduate studies needing strong practical training such as medicine, nursing or law sciences. Although this is also the case in engineering studies, amazingly, few experiences have been reported. In this paper we propose to use PBL in the educational organization of the Computer Graphics subjects in the Computer Science degree. Our PBL project focuses in the development of a C++ graphical environment based on the OpenGL libraries for visualization and handling of different graphical objects. The starting point is a basic skeleton that already includes lighting functions, perspective projection with mouse interaction to change the point of view and three predefined objects. Students have to complete this skeleton by adding their own functions to solve the project. A total number of 10 projects have been proposed and successfully solved. The exercises range from human face rendering to articulated objects, such as robot arms or puppets. In the present paper we extensively report the statement and educational objectives for two of the projects: solar system visualization and a chess game. We report our earlier educational experience based on the standard classroom theoretical, problem and practice sessions and the reasons that motivated searching for other learning methods. We have mainly chosen PBL because it improves the student learning initiative. We have applied the PBL educational model since the beginning of the second semester. The student’s feedback increases in his interest for the subject. We present a comparative study of the teachers’ and students’ workload between PBL and the classic teaching approach, which suggests that the workload increase in PBL is not as high as it seems.  
  Address  
  Corporate Author Thesis  
  Publisher Computer Graphics Forum Place of Publication Computer Vision CenterComputer Science Department Escola Tcnica Superior d’Enginyeria (UAB), Edifi 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 IAM;ADAS; Approved no  
  Call Number IAM @ iam @ MJG2006a Serial 1607  
Permanent link to this record
 

 
Author Carme Julia; Angel Sappa; Felipe Lumbreras; Joan Serrat; Antonio Lopez edit   pdf
doi  openurl
  Title Predicting Missing Ratings in Recommender Systems: Adapted Factorization Approach Type Journal Article
  Year 2009 Publication International Journal of Electronic Commerce Abbreviated Journal  
  Volume 14 Issue 1 Pages (down) 89-108  
  Keywords  
  Abstract The paper presents a factorization-based approach to make predictions in recommender systems. These systems are widely used in electronic commerce to help customers find products according to their preferences. Taking into account the customer's ratings of some products available in the system, the recommender system tries to predict the ratings the customer would give to other products in the system. The proposed factorization-based approach uses all the information provided to compute the predicted ratings, in the same way as approaches based on Singular Value Decomposition (SVD). The main advantage of this technique versus SVD-based approaches is that it can deal with missing data. It also has a smaller computational cost. Experimental results with public data sets are provided to show that the proposed adapted factorization approach gives better predicted ratings than a widely used SVD-based approach.  
  Address  
  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 1086-4415 ISBN Medium  
  Area Expedition Conference  
  Notes ADAS Approved no  
  Call Number ADAS @ adas @ JSL2009b Serial 1237  
Permanent link to this record
Select All    Deselect All
 |   | 
Details

Save Citations:
Export Records: