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Author |
Joan Marti; Jose Miguel Benedi; Ana Maria Mendonça; Joan Serrat |
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Title |
Pattern Recognition and Image Analysis |
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2007 |
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3rd Iberian Conference |
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6669 |
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4477-4478 |
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Girona (Spain) |
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IbPRIA |
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ADAS @ adas @ MBM2007 |
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994 |
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Author |
Antonio Lopez; Jiaolong Xu; Jose Luis Gomez; David Vazquez; German Ros |
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Title |
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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13 |
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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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Author |
Antonio Lopez; Ernest Valveny; Juan J. Villanueva |
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Title |
Real-time quality control of surgical material packaging by artificial vision |
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2005 |
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Assembly Automation |
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25 |
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3 |
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ADAS @ adas @ LVV2005 |
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552 |
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Author |
Felipe Lumbreras; Joan Serrat |
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Title |
Wavelet filtering for the segmentation of marble images |
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1996 |
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CVC Technical Report #05 |
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CVC (UAB) |
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ADAS @ adas @ LuS1996d |
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92 |
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Felipe Lumbreras; Joan Serrat |
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Title |
Segmentation of petrographical image of marbles |
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1996 |
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CVC Technical Report #04 |
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ADAS @ adas @ LuS1996c |
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93 |
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Author |
Felipe Lumbreras; Joan Serrat |
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Title |
Segmentation of petrographical images of marbles |
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1996 |
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Computers and Geosciences. 22(5):547–558 |
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ADAS @ adas @ LuS1996b |
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82 |
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Author |
Felipe Lumbreras; Joan Serrat |
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Title |
Wavelet filtering for the segmentation of marble images. |
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Miscellaneous |
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1996 |
Publication |
Optical Engineering |
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ADAS @ adas @ LuS1996a |
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77 |
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Author |
Felipe Lumbreras |
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Title |
Segmentation, classification and modelization of textures by means of multiresolution decomposition techniques. |
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2001 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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ADAS @ adas @ Lum2001 |
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188 |
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Author |
Antonio Lopez; Ricardo Toledo; Joan Serrat; Juan J. Villanueva |
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Title |
Extraction of vessel centerlines from 2D coronary angiographies |
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Miscellaneous |
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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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Bilbao |
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ADAS @ adas @ LTS1999 |
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14 |
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Author |
Antonio Lopez; Joan Serrat; J. Saludes; Cristina Cañero; Felipe Lumbreras; T. Graf |
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Title |
Ridgeness for Detecting Lane Markings |
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Miscellaneous |
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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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lane markings |
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Hamburg (Germany) |
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ADAS |
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ADAS @ adas @ LSS2005 |
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548 |
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David Lloret; Joan Serrat; Antonio Lopez; Juan J. Villanueva |
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Title |
Ultrasound to magnetic resonance volume registration for brain sinking measurement |
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Miscellaneous |
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2003 |
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1rst. Iberian Conference on Pattern Recognition and Image Analysis IbPRIA 2003 |
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Palma de Mallorca |
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ADAS @ adas @ LSL2003b |
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391 |
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David Lloret; Joan Serrat; Antonio Lopez; Juan J. Villanueva |
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Ultrasound to MR Volume Registration for Brain Sinking Measurement |
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2003 |
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In Pattern Recognition and Image Analysis, Lecture Notes in Computer Science, 2652:420–427 |
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Springer-Verlag |
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ADAS @ adas @ LSL2003a |
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384 |
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David Lloret; Joan Serrat; Antonio Lopez; Juan J. Villanueva |
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Motion-induced error correction in ultrasound imaging. |
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2002 |
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1st. International Symposium on 3D Data Processing Visualization and Transmission 3DPTV. |
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ADAS @ adas @ LSL2002 |
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295 |
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David Lloret; Joan Serrat; Antonio Lopez; A. Soler; Juan J. Villanueva |
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Retinal image registration using creases as anatomical landmarks. |
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2000 |
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15 th International Conference on Pattern Recognition |
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3 |
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207-2010 |
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Retinal images are routinely used in ophthalmology to study the optical nerve head and the retina. To assess objectively the evolution of an illness, images taken at different times must be registered. Most methods so far have been designed specifically for a single image modality, like temporal series or stereo pairs of angiographies, fluorescein angiographies or scanning laser ophthalmoscope (SLO) images, which makes them prone to fail when conditions vary. In contrast, the method we propose has shown to be accurate and reliable on all the former modalities. It has been adapted from the 3D registration of CT and MR image to 2D. Relevant features (also known as landmarks) are extracted by means of a robust creaseness operator, and resulting images are iteratively transformed until a maximum in their correlation is achieved. Our method has succeeded in more than 100 pairs tried so far, in all cases including also the scaling as a parameter to be optimized |
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Barcelona. |
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ICPR |
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ADAS @ adas @ LSL2000 c |
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233 |
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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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IJAT |
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11 |
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3 |
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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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1229-9138 |
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ADAS @ adas @ LSC2010 |
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1300 |
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