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Author |
Naveen Onkarappa; Angel Sappa |
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Title |
On-Board Monocular Vision System Pose Estimation through a Dense Optical Flow |
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Conference Article |
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Year |
2010 |
Publication |
7th International Conference on Image Analysis and Recognition |
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6111 |
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230-239 |
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Abstract |
This paper presents a robust technique for estimating on-board monocular vision system pose. The proposed approach is based on a dense optical flow that is robust against shadows, reflections and illumination changes. A RANSAC based scheme is used to cope with the outliers in the optical flow. The proposed technique is intended to be used in driver assistance systems for applications such as obstacle or pedestrian detection. Experimental results on different scenarios, both from synthetic and real sequences, shows usefulness of the proposed approach. |
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Povoa de Varzim (Portugal) |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-13771-6 |
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ICIAR |
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ADAS |
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no |
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ADAS @ adas @ OnS2010 |
Serial |
1342 |
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Author |
Diego Cheda; Daniel Ponsa; Antonio Lopez |
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Title |
Monocular Egomotion Estimation based on Image Matching |
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Conference Article |
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Year |
2012 |
Publication |
1st International Conference on Pattern Recognition Applications and Methods |
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425-430 |
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SLAM |
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Portugal |
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ICPRAM |
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ADAS |
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no |
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Admin @ si @ CPL2012a;; ADAS @ adas @ |
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2011 |
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Author |
Cristhian Aguilera; Xavier Soria; Angel Sappa; Ricardo Toledo |
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Title |
RGBN Multispectral Images: a Novel Color Restoration Approach |
Type |
Conference Article |
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Year |
2017 |
Publication |
15th International Conference on Practical Applications of Agents and Multi-Agent System |
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Keywords |
Multispectral Imaging; Free Sensor Model; Neural Network |
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Abstract |
This paper describes a color restoration technique used to remove NIR information from single sensor cameras where color and near-infrared images are simultaneously acquired|referred to in the literature as RGBN images. The proposed approach is based on a neural network architecture that learns the NIR information contained in the RGBN images. The proposed approach is evaluated on real images obtained by using a pair of RGBN cameras. Additionally, qualitative comparisons with a nave color correction technique based on mean square
error minimization are provided. |
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Porto; Portugal; June 2017 |
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PAAMS |
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ADAS; MSIAU; 600.118; 600.122 |
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no |
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Call Number |
Admin @ si @ ASS2017 |
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2918 |
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Author |
Patricia Suarez; Angel Sappa; Boris X. Vintimilla |
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Title |
Learning to Colorize Infrared Images |
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Conference Article |
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Year |
2017 |
Publication |
15th International Conference on Practical Applications of Agents and Multi-Agent System |
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Keywords |
CNN in multispectral imaging; Image colorization |
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This paper focuses on near infrared (NIR) image colorization by using a Generative Adversarial Network (GAN) architecture model. The proposed architecture consists of two stages. Firstly, it learns to colorize the given input, resulting in a RGB image. Then, in the second stage, a discriminative model is used to estimate the probability that the generated image came from the training dataset, rather than the image automatically generated. The proposed model starts the learning process from scratch, because our set of images is very di erent from the dataset used in existing pre-trained models, so transfer learning strategies cannot be used. Infrared image colorization is an important problem when human perception need to be considered, e.g, in remote sensing applications. Experimental results with a large set of real images are provided showing the validity of the proposed approach. |
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Porto; Portugal; June 2017 |
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ADAS; MSIAU; 600.086; 600.122; 600.118 |
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no |
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Call Number |
Admin @ si @ |
Serial |
2919 |
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Author |
David Vazquez; Jiaolong Xu; Sebastian Ramos; Antonio Lopez; Daniel Ponsa |
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Title |
Weakly Supervised Automatic Annotation of Pedestrian Bounding Boxes |
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Conference Article |
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Year |
2013 |
Publication |
CVPR Workshop on Ground Truth – What is a good dataset? |
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706 - 711 |
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Keywords |
Pedestrian Detection; Domain Adaptation |
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Among the components of a pedestrian detector, its trained pedestrian classifier is crucial for achieving the desired performance. The initial task of the training process consists in collecting samples of pedestrians and background, which involves tiresome manual annotation of pedestrian bounding boxes (BBs). Thus, recent works have assessed the use of automatically collected samples from photo-realistic virtual worlds. However, learning from virtual-world samples and testing in real-world images may suffer the dataset shift problem. Accordingly, in this paper we assess an strategy to collect samples from the real world and retrain with them, thus avoiding the dataset shift, but in such a way that no BBs of real-world pedestrians have to be provided. In particular, we train a pedestrian classifier based on virtual-world samples (no human annotation required). Then, using such a classifier we collect pedestrian samples from real-world images by detection. After, a human oracle rejects the false detections efficiently (weak annotation). Finally, a new classifier is trained with the accepted detections. We show that this classifier is competitive with respect to the counterpart trained with samples collected by manually annotating hundreds of pedestrian BBs. |
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Portland; Oregon; June 2013 |
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Publisher |
IEEE |
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English |
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English |
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CVPRW |
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Notes |
ADAS; 600.054; 600.057; 601.217 |
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no |
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Call Number |
ADAS @ adas @ VXR2013a |
Serial |
2219 |
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Author |
Jiaolong Xu; David Vazquez; Sebastian Ramos; Antonio Lopez; Daniel Ponsa |
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Title |
Adapting a Pedestrian Detector by Boosting LDA Exemplar Classifiers |
Type |
Conference Article |
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Year |
2013 |
Publication |
CVPR Workshop on Ground Truth – What is a good dataset? |
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688 - 693 |
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Keywords |
Pedestrian Detection; Domain Adaptation |
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Abstract |
Training vision-based pedestrian detectors using synthetic datasets (virtual world) is a useful technique to collect automatically the training examples with their pixel-wise ground truth. However, as it is often the case, these detectors must operate in real-world images, experiencing a significant drop of their performance. In fact, this effect also occurs among different real-world datasets, i.e. detectors' accuracy drops when the training data (source domain) and the application scenario (target domain) have inherent differences. Therefore, in order to avoid this problem, it is required to adapt the detector trained with synthetic data to operate in the real-world scenario. In this paper, we propose a domain adaptation approach based on boosting LDA exemplar classifiers from both virtual and real worlds. We evaluate our proposal on multiple real-world pedestrian detection datasets. The results show that our method can efficiently adapt the exemplar classifiers from virtual to real world, avoiding drops in average precision over the 15%. |
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Portland; oregon; June 2013 |
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English |
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English |
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CVPRW |
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Notes |
ADAS; 600.054; 600.057; 601.217 |
Approved |
yes |
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Call Number |
XVR2013; ADAS @ adas @ xvr2013a |
Serial |
2220 |
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Author |
Gioacchino Vino; Angel Sappa |
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Title |
Revisiting Harris Corner Detector Algorithm: a Gradual Thresholding Approach |
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Conference Article |
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Year |
2013 |
Publication |
10th International Conference on Image Analysis and Recognition |
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7950 |
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354-363 |
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This paper presents an adaptive thresholding approach intended to increase the number of detected corners, while reducing the amount of those ones corresponding to noisy data. The proposed approach works by using the classical Harris corner detector algorithm and overcome the difficulty in finding a general threshold that work well for all the images in a given data set by proposing a novel adaptive thresholding scheme. Initially, two thresholds are used to discern between strong corners and flat regions. Then, a region based criteria is used to discriminate between weak corners and noisy points in the midway interval. Experimental results show that the proposed approach has a better capability to reject false corners and, at the same time, to detect weak ones. Comparisons with the state of the art are provided showing the validity of the proposed approach. |
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Póvoa de Varzim; Portugal; June 2013 |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-39093-7 |
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ICIAR |
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Notes |
ADAS; 600.055 |
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no |
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Call Number |
Admin @ si @ ViS2013 |
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2562 |
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Author |
Arnau Ramisa; Adriana Tapus; Ramon Lopez de Mantaras; Ricardo Toledo |
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Title |
Mobile Robot Localization using Panoramic Vision and Combination of Feature Region Detectors |
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Conference Article |
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2008 |
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IEEE International Conference on Robotics and Automation, |
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538–543 |
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Pasadena; CA; USA |
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ICRA |
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RV;ADAS |
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no |
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Admin @ si @ RTL2008 |
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1144 |
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Author |
Jiaolong Xu; Sebastian Ramos; David Vazquez; Antonio Lopez |
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Title |
Incremental Domain Adaptation of Deformable Part-based Models |
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Conference Article |
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2014 |
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25th British Machine Vision Conference |
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Pedestrian Detection; Part-based models; Domain Adaptation |
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Nowadays, classifiers play a core role in many computer vision tasks. The underlying assumption for learning classifiers is that the training set and the deployment environment (testing) follow the same probability distribution regarding the features used by the classifiers. However, in practice, there are different reasons that can break this constancy assumption. Accordingly, reusing existing classifiers by adapting them from the previous training environment (source domain) to the new testing one (target domain)
is an approach with increasing acceptance in the computer vision community. In this paper we focus on the domain adaptation of deformable part-based models (DPMs) for object detection. In particular, we focus on a relatively unexplored scenario, i.e. incremental domain adaptation for object detection assuming weak-labeling. Therefore, our algorithm is ready to improve existing source-oriented DPM-based detectors as soon as a little amount of labeled target-domain training data is available, and keeps improving as more of such data arrives in a continuous fashion. For achieving this, we follow a multiple
instance learning (MIL) paradigm that operates in an incremental per-image basis. As proof of concept, we address the challenging scenario of adapting a DPM-based pedestrian detector trained with synthetic pedestrians to operate in real-world scenarios. The obtained results show that our incremental adaptive models obtain equally good accuracy results as the batch learned models, while being more flexible for handling continuously arriving target-domain data. |
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Nottingham; uk; September 2014 |
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BMVA Press |
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Valstar, Michel and French, Andrew and Pridmore, Tony |
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BMVC |
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ADAS; 600.057; 600.054; 600.076 |
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XRV2014c; ADAS @ adas @ xrv2014c |
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2455 |
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Author |
Angel Sappa; Fadi Dornaika; David Geronimo; Antonio Lopez |
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Title |
Registration-based Moving Object Detection from a Moving Camera |
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Conference Article |
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2008 |
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IROS2008 2nd Workshop on Perception, Planning and Navigation for Intelligent Vehicles |
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65–69 |
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This paper presents a robust approach for detecting moving objects from on-board stereo vision systems. It relies on a feature point quaternion-based registration, which avoids common problems that appear when computationally expensive iterative-based algorithms are used on dynamic environments. The proposed approach consists of three stages. Initially, feature points are extracted and tracked through consecutive frames. Then, a RANSAC based approach is used for registering
two 3D point sets with known correspondences by means of the quaternion method. Finally, the computed 3D rigid displacement is used to map two consecutive frames into the same coordinate system. Moving objects correspond to those areas with large registration errors. Experimental results, in different scenarios, show the viability of the proposed approach. |
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Nice (France) |
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ADAS |
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ADAS @ adas @ SDG2008 |
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1017 |
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