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Author |
Daniel Ponsa; Antonio Lopez |
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Title |
Vehicle Trajectory Estimation based on Monocular Vision |
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Conference Article |
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Year |
2007 |
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3rd Iberian Conference on Pattern Recognition and Image Analysis, LNCS 4477 |
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587-594 |
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Keywords |
vehicle detection |
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Girona (Spain) |
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ADAS |
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Call Number |
ADAS @ adas @ PoL2007a |
Serial |
785 |
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Author |
Daniel Ponsa; Antonio Lopez |
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Title |
Feature Selection Based on a New Formulation of the Minimal-Redundancy-Maximal-Relevance Criterion |
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Conference Article |
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Year |
2007 |
Publication |
3rd Iberian Conference on Pattern Recognition and Image Analysis, LNCS 4477 |
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47-54 |
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Girona (Spain) |
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ADAS |
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no |
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ADAS @ adas @ PoL2007b |
Serial |
787 |
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Author |
David Geronimo; Antonio Lopez; Daniel Ponsa; Angel Sappa |
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Title |
Haar Wavelets and Edge Orientation Histograms for On-Board Pedestrian Detection |
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Conference Article |
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Year |
2007 |
Publication |
3rd Iberian Conference on Pattern Recognition and Image Analysis, LNCS 4477 |
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Volume |
1 |
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Pages |
418–425 |
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Keywords |
Pedestrian detection |
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Girona (Spain) |
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J. Marti et al. |
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ADAS |
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Call Number |
ADAS @ adas @ GLP2007a |
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805 |
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Author |
David Geronimo; Antonio Lopez; Angel Sappa |
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Title |
Computer Vision Approaches for Pedestrian Detection: Visible Spectrum Survey |
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Conference Article |
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Year |
2007 |
Publication |
3rd Iberian Conference on Pattern Recognition and Image Analysis, LNCS 4477 |
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1 |
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547–554 |
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Keywords |
Pedestrian detection |
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Abstract |
Pedestrian detection from images of the visible spectrum is a high relevant area of research given its potential impact in the design of pedestrian protection systems. There are many proposals in the literature but they lack a comparative viewpoint. According to this, in this paper we first propose a common framework where we fit the different approaches, and second we use this framework to provide a comparative point of view of the details of such different approaches, pointing out also the main challenges to be solved in the future. In summary, we expect
this survey to be useful for both novel and experienced researchers in the field. In the first case, as a clarifying snapshot of the state of the art; in the second, as a way to unveil trends and to take conclusions from the comparative study. |
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Girona (Spain) |
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J. Marti et al. |
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ADAS |
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Call Number |
ADAS @ adas @ GLS2007 |
Serial |
804 |
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Author |
Jose Manuel Alvarez; Antonio Lopez; Ramon Baldrich |
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Title |
Shadow Resistant Road Segmentation from a Mobile Monocular System |
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Conference Article |
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Year |
2007 |
Publication |
3rd Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA 2007), J. Marti et al. (Eds.) LNCS 4477:9–16 |
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road detection |
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Address |
Gerona (Spain) |
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ADAS;CIC |
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no |
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Call Number |
ADAS @ adas @ ALB2007 |
Serial |
943 |
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Author |
Joan Serrat; Ferran Diego; Felipe Lumbreras; Jose Manuel Alvarez |
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Title |
Synchronization of Video Sequences from Free-moving Cameras |
Type |
Conference Article |
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Year |
2007 |
Publication |
3rd Iberian Conference on Pattern Recognition and Image Analysis |
Abbreviated Journal |
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Volume |
4477 |
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Pages |
620–627 |
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Girona (Spain) |
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J. Marti et al. |
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LNCS |
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IbPRIA |
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ADAS |
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Call Number |
ADAS @ adas @ SDL2007 |
Serial |
880 |
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Author |
Antonio Lopez; Joan Serrat; Cristina Cañero; Felipe Lumbreras |
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Title |
Robust Lane Lines Detection and Quantitative Assessment |
Type |
Conference Article |
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Year |
2007 |
Publication |
3rd Iberian Conference on Pattern Recognition and Image Analysis |
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Volume |
4477 |
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Pages |
274–281 |
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Keywords |
lane markings |
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Girona (Spain) |
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J. Marti et al |
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LNCS |
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IbPRIA |
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ADAS |
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no |
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Call Number |
ADAS @ adas @ LSC2007 |
Serial |
881 |
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Author |
Lorenzo Porzi; Markus Hofinger; Idoia Ruiz; Joan Serrat; Samuel Rota Bulo; Peter Kontschieder |
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Title |
Learning Multi-Object Tracking and Segmentation from Automatic Annotations |
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Conference Article |
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Year |
2020 |
Publication |
33rd IEEE Conference on Computer Vision and Pattern Recognition |
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6845-6854 |
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Abstract |
In this work we contribute a novel pipeline to automatically generate training data, and to improve over state-of-the-art multi-object tracking and segmentation (MOTS) methods. Our proposed track mining algorithm turns raw street-level videos into high-fidelity MOTS training data, is scalable and overcomes the need of expensive and time-consuming manual annotation approaches. We leverage state-of-the-art instance segmentation results in combination with optical flow predictions, also trained on automatically harvested training data. Our second major contribution is MOTSNet – a deep learning, tracking-by-detection architecture for MOTS – deploying a novel mask-pooling layer for improved object association over time. Training MOTSNet with our automatically extracted data leads to significantly improved sMOTSA scores on the novel KITTI MOTS dataset (+1.9%/+7.5% on cars/pedestrians), and MOTSNet improves by +4.1% over previously best methods on the MOTSChallenge dataset. Our most impressive finding is that we can improve over previous best-performing works, even in complete absence of manually annotated MOTS training data. |
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virtual; June 2020 |
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CVPR |
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ADAS; 600.124; 600.118 |
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Call Number |
Admin @ si @ PHR2020 |
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3402 |
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Author |
Marçal Rusiñol; David Aldavert; Dimosthenis Karatzas; Ricardo Toledo; Josep Llados |
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Title |
Interactive Trademark Image Retrieval by Fusing Semantic and Visual Content. Advances in Information Retrieval |
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Conference Article |
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2011 |
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33rd European Conference on Information Retrieval |
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6611 |
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314-325 |
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In this paper we propose an efficient queried-by-example retrieval system which is able to retrieve trademark images by similarity from patent and trademark offices' digital libraries. Logo images are described by both their semantic content, by means of the Vienna codes, and their visual contents, by using shape and color as visual cues. The trademark descriptors are then indexed by a locality-sensitive hashing data structure aiming to perform approximate k-NN search in high dimensional spaces in sub-linear time. The resulting ranked lists are combined by using the Condorcet method and a relevance feedback step helps to iteratively revise the query and refine the obtained results. The experiments demonstrate the effectiveness and efficiency of this system on a realistic and large dataset. |
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Dublin, Ireland |
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Springer |
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Berlin |
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P. Clough; C. Foley; C. Gurrin; G.J.F. Jones; W. Kraaij; H. Lee; V. Murdoch |
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978-3-642-20160-8 |
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ECIR |
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DAG; RV;ADAS |
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Admin @ si @ RAK2011 |
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1737 |
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David Vazquez; Jorge Bernal; F. Javier Sanchez; Gloria Fernandez Esparrach; Antonio Lopez; Adriana Romero; Michal Drozdzal; Aaron Courville |
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Title |
A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images |
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Conference Article |
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2017 |
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31st International Congress and Exhibition on Computer Assisted Radiology and Surgery |
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Deep Learning; Medical Imaging |
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Colorectal cancer (CRC) is the third cause of cancer death worldwide. Currently, the standard approach to reduce CRC-related mortality is to perform regular screening in search for polyps and colonoscopy is the screening tool of choice. The main limitations of this screening procedure are polyp miss-rate and inability to perform visual assessment of polyp malignancy. These drawbacks can be reduced by designing Decision Support Systems (DSS) aiming to help clinicians in the different stages of the procedure by providing endoluminal scene segmentation. Thus, in this paper, we introduce an extended benchmark of colonoscopy image, with the hope of establishing a new strong benchmark for colonoscopy image analysis research. We provide new baselines on this dataset by training standard fully convolutional networks (FCN) for semantic segmentation and significantly outperforming, without any further post-processing, prior results in endoluminal scene segmentation. |
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CARS |
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ADAS; MV; 600.075; 600.085; 600.076; 601.281; 600.118 |
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ADAS @ adas @ VBS2017a |
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2880 |
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