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Antonio Lopez; Felipe Lumbreras; Joan Serrat |
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Creaseness form level set extrinsec curvature. |
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1998 |
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5th European Conference on Computer Vision (ECCV’98), Lecture Notes in Computer Science,vol 1407, pgs. 156–169 |
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Freiburg, Germany. |
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ADAS @ adas @ LLS1998b |
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12 |
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Antonio Lopez; Felipe Lumbreras; Joan Serrat |
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Title |
Efficient computation of local creaseness |
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1997 |
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CVC Technical Report |
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15 |
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CVC, Bellaterra (Spain) |
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ADAS @ adas @ LLS1997b |
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527 |
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Antonio Lopez; Felipe Lumbreras; Joan Serrat; Juan J. Villanueva |
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Title |
Evaluation of Methods for Ridge and Valley Detection |
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1999 |
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IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 21:327–335 |
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ADAS @ adas @ LLS1999b |
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483 |
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Antonio Lopez; Gabriel Villalonga; Laura Sellart; German Ros; David Vazquez; Jiaolong Xu; Javier Marin; Azadeh S. Mozafari |
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Title |
Training my car to see using virtual worlds |
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2017 |
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Image and Vision Computing |
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IMAVIS |
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38 |
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102-118 |
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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. |
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ADAS; 600.118 |
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Admin @ si @ LVS2017 |
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2985 |
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Antonio Lopez; J. Hilgenstock; A. Busse; Ramon Baldrich; Felipe Lumbreras; Joan Serrat |
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Title |
Temporal Coherence Analysis for Intelligent Headlight Control |
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2008 |
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2nd Workshop on Perception, Planning and Navigation for Intelligent Vehicles |
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59–64 |
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Intelligent Headlights |
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IROS |
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ADAS;CIC |
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ADAS @ adas @ LHB2008b |
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1112 |
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Antonio Lopez; J. Hilgenstock; A. Busse; Ramon Baldrich; Felipe Lumbreras; Joan Serrat |
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Title |
Nightime Vehicle Detecion for Intelligent Headlight Control |
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2008 |
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Advanced Concepts for Intelligent Vision Systems, 10th International Conference, Proceedings, |
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5259 |
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113–124 |
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Intelligent Headlights; vehicle detection |
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Juan-les-Pins, France |
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ADAS;CIC |
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ADAS @ adas @ LHB2008a |
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1098 |
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Antonio Lopez; Jiaolong Xu; Jose Luis Gomez; David Vazquez; German Ros |
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From Virtual to Real World Visual Perception using Domain Adaptation -- The DPM as Example |
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2017 |
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Domain Adaptation in Computer Vision Applications |
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243-258 |
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Domain Adaptation |
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Supervised learning tends to produce more accurate classifiers than unsupervised learning in general. This implies that training data is preferred with annotations. When addressing visual perception challenges, such as localizing certain object classes within an image, the learning of the involved classifiers turns out to be a practical bottleneck. The reason is that, at least, we have to frame object examples with bounding boxes in thousands of images. A priori, the more complex the model is regarding its number of parameters, the more annotated examples are required. This annotation task is performed by human oracles, which ends up in inaccuracies and errors in the annotations (aka ground truth) since the task is inherently very cumbersome and sometimes ambiguous. As an alternative we have pioneered the use of virtual worlds for collecting such annotations automatically and with high precision. However, since the models learned with virtual data must operate in the real world, we still need to perform domain adaptation (DA). In this chapter we revisit the DA of a deformable part-based model (DPM) as an exemplifying case of virtual- to-real-world DA. As a use case, we address the challenge of vehicle detection for driver assistance, using different publicly available virtual-world data. While doing so, we investigate questions such as: how does the domain gap behave due to virtual-vs-real data with respect to dominant object appearance per domain, as well as the role of photo-realism in the virtual world. |
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Springer |
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Gabriela Csurka |
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ADAS; 600.085; 601.223; 600.076; 600.118 |
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ADAS @ adas @ LXG2017 |
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2872 |
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Antonio Lopez; Joan Serrat |
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Title |
Ridges and Valleys in Image Analysis |
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1998 |
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CVC Technical Report #22 |
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CVC (UAB) |
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ADAS @ adas @ LoS1998 |
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533 |
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Antonio Lopez; Joan Serrat |
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Title |
Ridge/Valley-like structures: Creases, separatrices and drainage patterns |
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1997 |
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CVC Technical Report #21 |
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ADAS @ adas @ LoS1997 |
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Antonio Lopez; Joan Serrat |
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Tracing crease curves by solving a system of differential equations. |
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1996 |
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ADAS @ adas @ LoS1996 |
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84 |
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Antonio Lopez; Joan Serrat |
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Image Analysis through Surface Geometric Descriptors |
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1995 |
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VI National Simposium on Pattern Recognition and image Analysis. |
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Cordoba |
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ADAS @ adas @ LoS1995 |
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133 |
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Antonio Lopez; Joan Serrat; Cristina Cañero; Felipe Lumbreras |
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Title |
Robust Lane Lines Detection and Quantitative Assessment |
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2007 |
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3rd Iberian Conference on Pattern Recognition and Image Analysis |
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4477 |
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274–281 |
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lane markings |
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Girona (Spain) |
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J. Marti et al |
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IbPRIA |
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ADAS @ adas @ LSC2007 |
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881 |
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Antonio Lopez; Joan Serrat; Cristina Cañero; Felipe Lumbreras; T. Graf |
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Robust lane markings detection and road geometry computation |
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2010 |
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International Journal of Automotive Technology |
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395–407 |
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lane markings |
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Detection of lane markings based on a camera sensor can be a low-cost solution to lane departure and curve-over-speed warnings. A number of methods and implementations have been reported in the literature. However, reliable detection is still an issue because of cast shadows, worn and occluded markings, variable ambient lighting conditions, for example. We focus on increasing detection reliability in two ways. First, we employed an image feature other than the commonly used edges: ridges, which we claim addresses this problem better. Second, we adapted RANSAC, a generic robust estimation method, to fit a parametric model of a pair of lane lines to the image features, based on both ridgeness and ridge orientation. In addition, the model was fitted for the left and right lane lines simultaneously to enforce a consistent result. Four measures of interest for driver assistance applications were directly computed from the fitted parametric model at each frame: lane width, lane curvature, and vehicle yaw angle and lateral offset with regard the lane medial axis. We qualitatively assessed our method in video sequences captured on several road types and under very different lighting conditions. We also quantitatively assessed it on synthetic but realistic video sequences for which road geometry and vehicle trajectory ground truth are known. |
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The Korean Society of Automotive Engineers |
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ADAS @ adas @ LSC2010 |
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1300 |
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Antonio Lopez; Joan Serrat; J. Saludes; Cristina Cañero; Felipe Lumbreras; T. Graf |
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Ridgeness for Detecting Lane Markings |
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2005 |
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2nd International Workshop on Intelligent Transportation Systems (WIT2005), Conference Proceedings (Sponsored by the IEEE Communication Society, Germany Chapter) |
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Hamburg (Germany) |
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ADAS @ adas @ LSS2005 |
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548 |
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Antonio Lopez; Ricardo Toledo; Joan Serrat; Juan J. Villanueva |
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Extraction of vessel centerlines from 2D coronary angiographies |
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1999 |
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Proceedings of the VIII Symposium Nacional de Reconocimiento de Formas y Analisis de Imagenes. pgs. 489–496, volume I |
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