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Angel Sappa; Boris X. Vintimilla |
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Title |
Cost-Based Closed Contour Representations |
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2007 |
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Journal of Electronic Imaging, 16(2), 023009 (9 pages) |
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ADAS @ adas @ SaV2007 |
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803 |
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Author |
Angel Sappa |
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Title |
Unsupervised Contour Closure Algorithm for Range Image Edge-Based Segmentation |
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2006 |
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IEEE Transactions on Image Processing, 15(2):377–384 |
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ADAS @ adas @ Sap2006a |
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637 |
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Author |
Angel Sappa |
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Title |
Splitting up Panoramic Range Images into Compact 2½D Representations |
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2006 |
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International Journal of Imaging Systems and Technology, 16(3): 85–91 |
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ADAS @ adas @ Sap2006b |
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721 |
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Author |
Alejandro Gonzalez Alzate; Zhijie Fang; Yainuvis Socarras; Joan Serrat; David Vazquez; Jiaolong Xu; Antonio Lopez |
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Title |
Pedestrian Detection at Day/Night Time with Visible and FIR Cameras: A Comparison |
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2016 |
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Sensors |
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SENS |
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16 |
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6 |
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820 |
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Pedestrian Detection; FIR |
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Despite all the significant advances in pedestrian detection brought by computer vision for driving assistance, it is still a challenging problem. One reason is the extremely varying lighting conditions under which such a detector should operate, namely day and night time. Recent research has shown that the combination of visible and non-visible imaging modalities may increase detection accuracy, where the infrared spectrum plays a critical role. The goal of this paper is to assess the accuracy gain of different pedestrian models (holistic, part-based, patch-based) when training with images in the far infrared spectrum. Specifically, we want to compare detection accuracy on test images recorded at day and nighttime if trained (and tested) using (a) plain color images, (b) just infrared images and (c) both of them. In order to obtain results for the last item we propose an early fusion approach to combine features from both modalities. We base the evaluation on a new dataset we have built for this purpose as well as on the publicly available KAIST multispectral dataset. |
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1424-8220 |
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ADAS; 600.085; 600.076; 600.082; 601.281 |
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ADAS @ adas @ GFS2016 |
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2754 |
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Author |
Alejandro Gonzalez Alzate; David Vazquez; Antonio Lopez; Jaume Amores |
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Title |
On-Board Object Detection: Multicue, Multimodal, and Multiview Random Forest of Local Experts |
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2017 |
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IEEE Transactions on cybernetics |
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Cyber |
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47 |
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11 |
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3980 - 3990 |
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Multicue; multimodal; multiview; object detection |
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Despite recent significant advances, object detection continues to be an extremely challenging problem in real scenarios. In order to develop a detector that successfully operates under these conditions, it becomes critical to leverage upon multiple cues, multiple imaging modalities, and a strong multiview (MV) classifier that accounts for different object views and poses. In this paper, we provide an extensive evaluation that gives insight into how each of these aspects (multicue, multimodality, and strong MV classifier) affect accuracy both individually and when integrated together. In the multimodality component, we explore the fusion of RGB and depth maps obtained by high-definition light detection and ranging, a type of modality that is starting to receive increasing attention. As our analysis reveals, although all the aforementioned aspects significantly help in improving the accuracy, the fusion of visible spectrum and depth information allows to boost the accuracy by a much larger margin. The resulting detector not only ranks among the top best performers in the challenging KITTI benchmark, but it is built upon very simple blocks that are easy to implement and computationally efficient. |
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2168-2267 |
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ADAS; 600.085; 600.082; 600.076; 600.118 |
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Admin @ si @ |
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2810 |
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