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Joan Serrat, Ferran Diego and Felipe Lumbreras. 2008. Los faros delanteros a traves del objetivo.
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Carme Julia, Angel Sappa and Felipe Lumbreras. 2008. Aprendiendo a recrear la realidad en 3D.
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Daniel Ponsa, Joan Serrat and Antonio Lopez. 2011. On-board image-based vehicle detection and tracking. TIM, 33(7), 783–805.
Abstract: In this paper we present a computer vision system for daytime vehicle detection and localization, an essential step in the development of several types of advanced driver assistance systems. It has a reduced processing time and high accuracy thanks to the combination of vehicle detection with lane-markings estimation and temporal tracking of both vehicles and lane markings. Concerning vehicle detection, our main contribution is a frame scanning process that inspects images according to the geometry of image formation, and with an Adaboost-based detector that is robust to the variability in the different vehicle types (car, van, truck) and lighting conditions. In addition, we propose a new method to estimate the most likely three-dimensional locations of vehicles on the road ahead. With regards to the lane-markings estimation component, we have two main contributions. First, we employ a different image feature to the other commonly used edges: we use ridges, which are better suited to this problem. Second, we adapt RANSAC, a generic robust estimation method, to fit a parametric model of a pair of lane markings to the image features. We qualitatively assess our vehicle detection system in sequences captured on several road types and under very different lighting conditions. The processed videos are available on a web page associated with this paper. A quantitative evaluation of the system has shown quite accurate results (a low number of false positives and negatives) at a reasonable computation time.
Keywords: vehicle detection
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Angel Sappa and M.A. Garcia. 2007. Generating compact representations of static scenes by means of 3D object hierarchies.
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Sergio Vera, Debora Gil, Antonio Lopez and Miguel Angel Gonzalez Ballester. 2012. Multilocal Creaseness Measure.
Abstract: This document describes the implementation using the Insight Toolkit of an algorithm for detecting creases (ridges and valleys) in N-dimensional images, based on the Local Structure Tensor of the image. In addition to the filter used to calculate the creaseness image, a filter for the computation of the structure tensor is also included in this submission.
Keywords: Ridges, Valley, Creaseness, Structure Tensor, Skeleton,
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Angel Sappa and M.A. Garcia. 2007. Incremental Integration of Multiresolution Range Images.
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A. Restrepo, Angel Sappa and M. Devy. 2005. Edge registration versus triangular mesh registration, a comparative study.
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Jose Luis Gomez, Gabriel Villalonga and Antonio Lopez. 2023. Co-Training for Unsupervised Domain Adaptation of Semantic Segmentation Models. SENS, 23(2), 621.
Abstract: Semantic image segmentation is a central and challenging task in autonomous driving, addressed by training deep models. Since this training draws to a curse of human-based image labeling, using synthetic images with automatically generated labels together with unlabeled real-world images is a promising alternative. This implies to address an unsupervised domain adaptation (UDA) problem. In this paper, we propose a new co-training procedure for synth-to-real UDA of semantic
segmentation models. It consists of a self-training stage, which provides two domain-adapted models, and a model collaboration loop for the mutual improvement of these two models. These models are then used to provide the final semantic segmentation labels (pseudo-labels) for the real-world images. The overall
procedure treats the deep models as black boxes and drives their collaboration at the level of pseudo-labeled target images, i.e., neither modifying loss functions is required, nor explicit feature alignment. We test our proposal on standard synthetic and real-world datasets for on-board semantic segmentation. Our
procedure shows improvements ranging from ∼13 to ∼26 mIoU points over baselines, so establishing new state-of-the-art results.
Keywords: Domain adaptation; semi-supervised learning; Semantic segmentation; Autonomous driving
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Cristhian Aguilera, Fernando Barrera, Felipe Lumbreras, Angel Sappa and Ricardo Toledo. 2012. Multispectral Image Feature Points. SENS, 12(9), 12661–12672.
Abstract: Far-Infrared and Visible Spectrum images. It allows matching interest points on images of the same scene but acquired in different spectral bands. Initially, points of interest are detected on both images through a SIFT-like based scale space representation. Then, these points are characterized using an Edge Oriented Histogram (EOH) descriptor. Finally, points of interest from multispectral images are matched by finding nearest couples using the information from the descriptor. The provided experimental results and comparisons with similar methods show both the validity of the proposed approach as well as the improvements it offers with respect to the current state-of-the-art.
Keywords: multispectral image descriptor; color and infrared images; feature point descriptor
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P. Ricaurte, C. Chilan, Cristhian A. Aguilera-Carrasco, Boris X. Vintimilla and Angel Sappa. 2014. Feature Point Descriptors: Infrared and Visible Spectra. SENS, 14(2), 3690–3701.
Abstract: This manuscript evaluates the behavior of classical feature point descriptors when they are used in images from long-wave infrared spectral band and compare them with the results obtained in the visible spectrum. Robustness to changes in rotation, scaling, blur, and additive noise are analyzed using a state of the art framework. Experimental results using a cross-spectral outdoor image data set are presented and conclusions from these experiments are given.
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