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Author Jose Luis Gomez; Gabriel Villalonga; Antonio Lopez edit  url
openurl 
  Title Co-Training for Unsupervised Domain Adaptation of Semantic Segmentation Models Type Journal Article
  Year 2023 Publication Sensors – Special Issue on “Machine Learning for Autonomous Driving Perception and Prediction” Abbreviated Journal (down) SENS  
  Volume 23 Issue 2 Pages 621  
  Keywords Domain adaptation; semi-supervised learning; Semantic segmentation; Autonomous driving  
  Abstract Semantic image segmentation is a central and challenging task in autonomous driving, addressed by training deep models. Since this training draws to a curse of human-based image labeling, using synthetic images with automatically generated labels together with unlabeled real-world images is a promising alternative. This implies to address an unsupervised domain adaptation (UDA) problem. In this paper, we propose a new co-training procedure for synth-to-real UDA of semantic
segmentation models. It consists of a self-training stage, which provides two domain-adapted models, and a model collaboration loop for the mutual improvement of these two models. These models are then used to provide the final semantic segmentation labels (pseudo-labels) for the real-world images. The overall
procedure treats the deep models as black boxes and drives their collaboration at the level of pseudo-labeled target images, i.e., neither modifying loss functions is required, nor explicit feature alignment. We test our proposal on standard synthetic and real-world datasets for on-board semantic segmentation. Our
procedure shows improvements ranging from ∼13 to ∼26 mIoU points over baselines, so establishing new state-of-the-art results.
 
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  Notes ADAS; no proj Approved no  
  Call Number Admin @ si @ GVL2023 Serial 3705  
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Author M. Altillawi; S. Li; S.M. Prakhya; Z. Liu; Joan Serrat edit  doi
openurl 
  Title Implicit Learning of Scene Geometry From Poses for Global Localization Type Journal Article
  Year 2024 Publication IEEE Robotics and Automation Letters Abbreviated Journal (down) ROBOTAUTOMLET  
  Volume 9 Issue 2 Pages 955-962  
  Keywords Localization; Localization and mapping; Deep learning for visual perception; Visual learning  
  Abstract Global visual localization estimates the absolute pose of a camera using a single image, in a previously mapped area. Obtaining the pose from a single image enables many robotics and augmented/virtual reality applications. Inspired by latest advances in deep learning, many existing approaches directly learn and regress 6 DoF pose from an input image. However, these methods do not fully utilize the underlying scene geometry for pose regression. The challenge in monocular relocalization is the minimal availability of supervised training data, which is just the corresponding 6 DoF poses of the images. In this letter, we propose to utilize these minimal available labels (i.e., poses) to learn the underlying 3D geometry of the scene and use the geometry to estimate the 6 DoF camera pose. We present a learning method that uses these pose labels and rigid alignment to learn two 3D geometric representations ( X, Y, Z coordinates ) of the scene, one in camera coordinate frame and the other in global coordinate frame. Given a single image, it estimates these two 3D scene representations, which are then aligned to estimate a pose that matches the pose label. This formulation allows for the active inclusion of additional learning constraints to minimize 3D alignment errors between the two 3D scene representations, and 2D re-projection errors between the 3D global scene representation and 2D image pixels, resulting in improved localization accuracy. During inference, our model estimates the 3D scene geometry in camera and global frames and aligns them rigidly to obtain pose in real-time. We evaluate our work on three common visual localization datasets, conduct ablation studies, and show that our method exceeds state-of-the-art regression methods' pose accuracy on all datasets.  
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  ISSN 2377-3766 ISBN Medium  
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  Notes ADAS Approved no  
  Call Number Admin @ si @ Serial 3857  
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Author Miguel Oliveira; Victor Santos; Angel Sappa; P. Dias; A. Moreira edit   pdf
doi  openurl
  Title Incremental Scenario Representations for Autonomous Driving using Geometric Polygonal Primitives Type Journal Article
  Year 2016 Publication Robotics and Autonomous Systems Abbreviated Journal (down) RAS  
  Volume 83 Issue Pages 312-325  
  Keywords Incremental scene reconstruction; Point clouds; Autonomous vehicles; Polygonal primitives  
  Abstract When an autonomous vehicle is traveling through some scenario it receives a continuous stream of sensor data. This sensor data arrives in an asynchronous fashion and often contains overlapping or redundant information. Thus, it is not trivial how a representation of the environment observed by the vehicle can be created and updated over time. This paper presents a novel methodology to compute an incremental 3D representation of a scenario from 3D range measurements. We propose to use macro scale polygonal primitives to model the scenario. This means that the representation of the scene is given as a list of large scale polygons that describe the geometric structure of the environment. Furthermore, we propose mechanisms designed to update the geometric polygonal primitives over time whenever fresh sensor data is collected. Results show that the approach is capable of producing accurate descriptions of the scene, and that it is computationally very efficient when compared to other reconstruction techniques.  
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  Publisher Elsevier B.V. Place of Publication Editor  
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  Area Expedition Conference  
  Notes ADAS; 600.086, 600.076 Approved no  
  Call Number Admin @ si @OSS2016a Serial 2806  
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Author Angel Sappa; Cristhian A. Aguilera-Carrasco; Juan A. Carvajal Ayala; Miguel Oliveira; Dennis Romero; Boris X. Vintimilla; Ricardo Toledo edit   pdf
doi  openurl
  Title Monocular visual odometry: A cross-spectral image fusion based approach Type Journal Article
  Year 2016 Publication Robotics and Autonomous Systems Abbreviated Journal (down) RAS  
  Volume 85 Issue Pages 26-36  
  Keywords Monocular visual odometry; LWIR-RGB cross-spectral imaging; Image fusion  
  Abstract This manuscript evaluates the usage of fused cross-spectral images in a monocular visual odometry approach. Fused images are obtained through a Discrete Wavelet Transform (DWT) scheme, where the best setup is empirically obtained by means of a mutual information based evaluation metric. The objective is to have a flexible scheme where fusion parameters are adapted according to the characteristics of the given images. Visual odometry is computed from the fused monocular images using an off the shelf approach. Experimental results using data sets obtained with two different platforms are presented. Additionally, comparison with a previous approach as well as with monocular-visible/infrared spectra are also provided showing the advantages of the proposed scheme.  
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  Publisher Elsevier B.V. Place of Publication Editor  
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  Notes ADAS;600.086; 600.076 Approved no  
  Call Number Admin @ si @SAC2016 Serial 2811  
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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 (down) RAS  
  Volume 84 Issue Pages 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.  
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  Notes ADAS; 600.086 Approved no  
  Call Number Admin @ si @ OSS2016b Serial 2912  
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Author A. Pujol; Jordi Vitria; Felipe Lumbreras; Juan J. Villanueva edit  doi
openurl 
  Title Topological principal component analysis for face encoding and recognition Type Journal Article
  Year 2001 Publication Pattern Recognition Letters Abbreviated Journal (down) PRL  
  Volume 22 Issue 6-7 Pages 769–776  
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  Abstract IF: 0.552  
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  Notes ADAS;OR;MV Approved no  
  Call Number ADAS @ adas @ PVL2001 Serial 155  
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Author Jaume Amores; Petia Radeva edit  url
doi  openurl
  Title Registration and Retrieval of Highly Elastic Bodies using Contextual Information Type Journal Article
  Year 2005 Publication Pattern Recognition Letters Abbreviated Journal (down) PRL  
  Volume 26 Issue 11 Pages 1720–1731  
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  Abstract IF: 1.138  
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  Notes ADAS;MILAB Approved no  
  Call Number ADAS @ adas @ AmR2005b Serial 592  
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Author Jaume Amores; N. Sebe; Petia Radeva edit  doi
openurl 
  Title Boosting the distance estimation: Application to the K-Nearest Neighbor Classifier Type Journal Article
  Year 2006 Publication Pattern Recognition Letters Abbreviated Journal (down) PRL  
  Volume 27 Issue 3 Pages 201–209  
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  Notes ADAS;MILAB Approved no  
  Call Number ADAS @ adas @ ASR2006 Serial 643  
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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 (down) PRL  
  Volume 28 Issue 15 Pages 2116-2126  
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  Notes ADAS Approved no  
  Call Number ADAS @ adas @ DoS2007c Serial 877  
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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 (down) PRL  
  Volume 30 Issue 5 Pages 535–543  
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  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.  
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  Publisher Elsevier Science Inc. Place of Publication Editor  
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  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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