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M.Gomez; Josefina Mauri; Eduard Fernandez-Nofrerias; Oriol Rodriguez-Leon; Carme Julia; Debora Gil; Petia Radeva |
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Reconstrucción de un modelo espacio-temporal de la luz del vaso a partir de secuencias de ecografía intracoronaria |
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2002 |
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XXXVIII Congreso Nacional de la Sociedad Española de Cardiología. |
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IAM;ADAS;MILAB |
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IAM @ iam @ GMF2002d |
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1516 |
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Author |
Fadi Dornaika; Angel Sappa |
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Title |
Real-time Vehicle Ego-Motion using Stereo Pairs and Particle Filters |
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Conference Article |
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2007 |
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Int. Conf. on Image Analysis and Recognition, |
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4633 |
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469–480 |
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Montreal (Canada) |
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ADAS |
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ADAS @ adas @ DoS2007a |
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813 |
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Author |
David Aldavert; Arnau Ramisa; Ramon Lopez de Mantaras; Ricardo Toledo |
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Title |
Real-time Object Segmentation using a Bag of Features Approach |
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Conference Article |
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2010 |
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13th International Conference of the Catalan Association for Artificial Intelligence |
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220 |
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321–329 |
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Object Segmentation; Bag Of Features; Feature Quantization; Densely sampled descriptors |
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In this paper, we propose an object segmentation framework, based on the popular bag of features (BoF), which can process several images per second while achieving a good segmentation accuracy assigning an object category to every pixel of the image. We propose an efficient color descriptor to complement the information obtained by a typical gradient-based local descriptor. Results show that color proves to be a useful cue to increase the segmentation accuracy, specially in large homogeneous regions. Then, we extend the Hierarchical K-Means codebook using the recently proposed Vector of Locally Aggregated Descriptors method. Finally, we show that the BoF method can be easily parallelized since it is applied locally, thus the time necessary to process an image is further reduced. The performance of the proposed method is evaluated in the standard PASCAL 2007 Segmentation Challenge object segmentation dataset. |
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IOS Press Amsterdam, |
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In R.Alquezar, A.Moreno, J.Aguilar. |
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9781607506423 |
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CCIA |
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ADAS |
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Admin @ si @ ARL2010b |
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1417 |
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Daniel Hernandez; Juan Carlos Moure; Toni Espinosa; Alejandro Chacon; David Vazquez; Antonio Lopez |
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Title |
Real-time 3D Reconstruction for Autonomous Driving via Semi-Global Matching |
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2016 |
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GPU Technology Conference |
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Stereo; Autonomous Driving; GPU; 3d reconstruction |
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Robust and dense computation of depth information from stereo-camera systems is a computationally demanding requirement for real-time autonomous driving. Semi-Global Matching (SGM) [1] approximates heavy-computation global algorithms results but with lower computational complexity, therefore it is a good candidate for a real-time implementation. SGM minimizes energy along several 1D paths across the image. The aim of this work is to provide a real-time system producing reliable results on energy-efficient hardware. Our design runs on a NVIDIA Titan X GPU at 104.62 FPS and on a NVIDIA Drive PX at 6.7 FPS, promising for real-time platforms |
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Silicon Valley; San Francisco; USA; April 2016 |
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GTC |
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ADAS; 600.085; 600.082; 600.076 |
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ADAS @ adas @ HME2016 |
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2738 |
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Author |
Angel Sappa; David Geronimo; Fadi Dornaika; Antonio Lopez |
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Title |
Real Time Vehicle Pose Using On-Board Stereo Vision System |
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Conference Article |
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2006 |
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International Conference on Image Analysis and Recognition |
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ICIAR |
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LNCS 4142 |
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205–216 |
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This paper presents a robust technique for a real time estimation of both camera’s position and orientation—referred as pose. A commercial stereo vision system is used. Unlike previous approaches, it can be used either for urban or highway scenarios. The proposed technique consists of two stages. Initially, a compact 2D representation of the original 3D data points is computed. Then, a RANSAC based least squares approach is used for fitting a plane to the road. At the same time,
relative camera’s position and orientation are computed. The proposed technique is intended to be used on a driving assistance scheme for applications such as obstacle or pedestrian detection. Experimental results on urban environments with different road geometries are presented. |
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ADAS |
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ADAS @ adas @ SGD2006b |
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671 |
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Author |
Fadi Dornaika; Angel Sappa |
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Title |
Real Time on Board Stereo Camera Pose through Image Registration |
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Conference Article |
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2008 |
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IEEE Intelligent Vehicles Symposium, |
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804–809 |
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Eindhoven (Netherlands) |
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ADAS |
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ADAS @ adas @ DoS2008a |
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1015 |
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Author |
Idoia Ruiz; Joan Serrat |
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Title |
Rank-based ordinal classification |
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2020 |
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25th International Conference on Pattern Recognition |
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8069-8076 |
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Differently from the regular classification task, in ordinal classification there is an order in the classes. As a consequence not all classification errors matter the same: a predicted class close to the groundtruth one is better than predicting a farther away class. To account for this, most previous works employ loss functions based on the absolute difference between the predicted and groundtruth class labels. We argue that there are many cases in ordinal classification where label values are arbitrary (for instance 1. . . C, being C the number of classes) and thus such loss functions may not be the best choice. We instead propose a network architecture that produces not a single class prediction but an ordered vector, or ranking, of all the possible classes from most to least likely. This is thanks to a loss function that compares groundtruth and predicted rankings of these class labels, not the labels themselves. Another advantage of this new formulation is that we can enforce consistency in the predictions, namely, predicted rankings come from some unimodal vector of scores with mode at the groundtruth class. We compare with the state of the art ordinal classification methods, showing
that ours attains equal or better performance, as measured by common ordinal classification metrics, on three benchmark datasets. Furthermore, it is also suitable for a new task on image aesthetics assessment, i.e. most voted score prediction. Finally, we also apply it to building damage assessment from satellite images, providing an analysis of its performance depending on the degree of imbalance of the dataset. |
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Virtual; January 2021 |
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ICPR |
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ADAS; 600.118; 600.124 |
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Admin @ si @ RuS2020 |
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3549 |
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Author |
Javier Marin; David Vazquez; Antonio Lopez; Jaume Amores; Bastian Leibe |
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Title |
Random Forests of Local Experts for Pedestrian Detection |
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Conference Article |
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2013 |
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15th IEEE International Conference on Computer Vision |
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2592 - 2599 |
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ADAS; Random Forest; Pedestrian Detection |
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Pedestrian detection is one of the most challenging tasks in computer vision, and has received a lot of attention in the last years. Recently, some authors have shown the advantages of using combinations of part/patch-based detectors in order to cope with the large variability of poses and the existence of partial occlusions. In this paper, we propose a pedestrian detection method that efficiently combines multiple local experts by means of a Random Forest ensemble. The proposed method works with rich block-based representations such as HOG and LBP, in such a way that the same features are reused by the multiple local experts, so that no extra computational cost is needed with respect to a holistic method. Furthermore, we demonstrate how to integrate the proposed approach with a cascaded architecture in order to achieve not only high accuracy but also an acceptable efficiency. In particular, the resulting detector operates at five frames per second using a laptop machine. We tested the proposed method with well-known challenging datasets such as Caltech, ETH, Daimler, and INRIA. The method proposed in this work consistently ranks among the top performers in all the datasets, being either the best method or having a small difference with the best one. |
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Sydney; Australia; December 2013 |
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IEEE |
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1550-5499 |
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ICCV |
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ADAS; 600.057; 600.054 |
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ADAS @ adas @ MVL2013 |
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2333 |
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Hanne Kause; Patricia Marquez; Andrea Fuster; Aura Hernandez-Sabate; Luc Florack; Debora Gil; Hans van Assen |
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Quality Assessment of Optical Flow in Tagging MRI |
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2015 |
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5th Dutch Bio-Medical Engineering Conference BME2015 |
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The Netherlands; January 2015 |
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BME |
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IAM; ADAS; 600.076; 600.075 |
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Admin @ si @ KMF2015 |
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2616 |
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Cesar de Souza; Adrien Gaidon; Yohann Cabon; Antonio Lopez |
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Procedural Generation of Videos to Train Deep Action Recognition Networks |
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2017 |
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30th IEEE Conference on Computer Vision and Pattern Recognition |
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2594-2604 |
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Deep learning for human action recognition in videos is making significant progress, but is slowed down by its dependency on expensive manual labeling of large video collections. In this work, we investigate the generation of synthetic training data for action recognition, as it has recently shown promising results for a variety of other computer vision tasks. We propose an interpretable parametric generative model of human action videos that relies on procedural generation and other computer graphics techniques of modern game engines. We generate a diverse, realistic, and physically plausible dataset of human action videos, called PHAV for ”Procedural Human Action Videos”. It contains a total of 39, 982 videos, with more than 1, 000 examples for each action of 35 categories. Our approach is not limited to existing motion capture sequences, and we procedurally define 14 synthetic actions. We introduce a deep multi-task representation learning architecture to mix synthetic and real videos, even if the action categories differ. Our experiments on the UCF101 and HMDB51 benchmarks suggest that combining our large set of synthetic videos with small real-world datasets can boost recognition performance, significantly
outperforming fine-tuning state-of-the-art unsupervised generative models of videos. |
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Honolulu; Hawaii; July 2017 |
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CVPR |
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ADAS; 600.076; 600.085; 600.118 |
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Admin @ si @ SGC2017 |
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3051 |
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