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Angel Sappa; Cristhian A. Aguilera-Carrasco; Juan A. Carvajal Ayala; Miguel Oliveira; Dennis Romero; Boris X. Vintimilla; Ricardo Toledo |
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Title |
Monocular visual odometry: A cross-spectral image fusion based approach |
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Journal Article |
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2016 |
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Robotics and Autonomous Systems |
Abbreviated Journal ![sorted by Abbreviated Journal field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
RAS |
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85 |
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26-36 |
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Monocular visual odometry; LWIR-RGB cross-spectral imaging; Image fusion |
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This manuscript evaluates the usage of fused cross-spectral images in a monocular visual odometry approach. Fused images are obtained through a Discrete Wavelet Transform (DWT) scheme, where the best setup is empirically obtained by means of a mutual information based evaluation metric. The objective is to have a flexible scheme where fusion parameters are adapted according to the characteristics of the given images. Visual odometry is computed from the fused monocular images using an off the shelf approach. Experimental results using data sets obtained with two different platforms are presented. Additionally, comparison with a previous approach as well as with monocular-visible/infrared spectra are also provided showing the advantages of the proposed scheme. |
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Elsevier B.V. |
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ADAS;600.086; 600.076 |
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Admin @ si @SAC2016 |
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2811 |
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Miguel Oliveira; Victor Santos; Angel Sappa; P. Dias; A. Moreira |
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Title |
Incremental texture mapping for autonomous driving |
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Journal Article |
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2016 |
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Robotics and Autonomous Systems |
Abbreviated Journal ![sorted by Abbreviated Journal field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
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84 |
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113-128 |
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Scene reconstruction; Autonomous driving; Texture mapping |
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Autonomous vehicles have a large number of on-board sensors, not only for providing coverage all around the vehicle, but also to ensure multi-modality in the observation of the scene. Because of this, it is not trivial to come up with a single, unique representation that feeds from the data given by all these sensors. We propose an algorithm which is capable of mapping texture collected from vision based sensors onto a geometric description of the scenario constructed from data provided by 3D sensors. The algorithm uses a constrained Delaunay triangulation to produce a mesh which is updated using a specially devised sequence of operations. These enforce a partial configuration of the mesh that avoids bad quality textures and ensures that there are no gaps in the texture. Results show that this algorithm is capable of producing fine quality textures. |
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ADAS; 600.086 |
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Admin @ si @ OSS2016b |
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2912 |
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M. Altillawi; S. Li; S.M. Prakhya; Z. Liu; Joan Serrat |
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Title |
Implicit Learning of Scene Geometry From Poses for Global Localization |
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Journal Article |
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2024 |
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IEEE Robotics and Automation Letters |
Abbreviated Journal ![sorted by Abbreviated Journal field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
ROBOTAUTOMLET |
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9 |
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2 |
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955-962 |
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Localization; Localization and mapping; Deep learning for visual perception; Visual learning |
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Global visual localization estimates the absolute pose of a camera using a single image, in a previously mapped area. Obtaining the pose from a single image enables many robotics and augmented/virtual reality applications. Inspired by latest advances in deep learning, many existing approaches directly learn and regress 6 DoF pose from an input image. However, these methods do not fully utilize the underlying scene geometry for pose regression. The challenge in monocular relocalization is the minimal availability of supervised training data, which is just the corresponding 6 DoF poses of the images. In this letter, we propose to utilize these minimal available labels (i.e., poses) to learn the underlying 3D geometry of the scene and use the geometry to estimate the 6 DoF camera pose. We present a learning method that uses these pose labels and rigid alignment to learn two 3D geometric representations ( X, Y, Z coordinates ) of the scene, one in camera coordinate frame and the other in global coordinate frame. Given a single image, it estimates these two 3D scene representations, which are then aligned to estimate a pose that matches the pose label. This formulation allows for the active inclusion of additional learning constraints to minimize 3D alignment errors between the two 3D scene representations, and 2D re-projection errors between the 3D global scene representation and 2D image pixels, resulting in improved localization accuracy. During inference, our model estimates the 3D scene geometry in camera and global frames and aligns them rigidly to obtain pose in real-time. We evaluate our work on three common visual localization datasets, conduct ablation studies, and show that our method exceeds state-of-the-art regression methods' pose accuracy on all datasets. |
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2377-3766 |
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Admin @ si @ |
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3857 |
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Cristhian Aguilera; Fernando Barrera; Felipe Lumbreras; Angel Sappa; Ricardo Toledo |
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Title |
Multispectral Image Feature Points |
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Journal Article |
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2012 |
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Sensors |
Abbreviated Journal ![sorted by Abbreviated Journal field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
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12 |
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9 |
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12661-12672 |
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multispectral image descriptor; color and infrared images; feature point descriptor |
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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. |
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no |
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Admin @ si @ ABL2012 |
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2154 |
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P. Ricaurte ; C. Chilan; Cristhian A. Aguilera-Carrasco; Boris X. Vintimilla; Angel Sappa |
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Title |
Feature Point Descriptors: Infrared and Visible Spectra |
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2014 |
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Sensors |
Abbreviated Journal ![sorted by Abbreviated Journal field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
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14 |
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2 |
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3690-3701 |
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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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ADAS;600.055; 600.076 |
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Admin @ si @ RCA2014a |
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2474 |
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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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Journal Article |
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2016 |
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Sensors |
Abbreviated Journal ![sorted by Abbreviated Journal field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
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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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Angel Sappa; P. Carvajal; Cristhian A. Aguilera-Carrasco; Miguel Oliveira; Dennis Romero; Boris X. Vintimilla |
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Title |
Wavelet based visible and infrared image fusion: a comparative study |
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Journal Article |
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2016 |
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Sensors |
Abbreviated Journal ![sorted by Abbreviated Journal field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
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16 |
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6 |
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1-15 |
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Image fusion; fusion evaluation metrics; visible and infrared imaging; discrete wavelet transform |
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This paper evaluates different wavelet-based cross-spectral image fusion strategies adopted to merge visible and infrared images. The objective is to find the best setup independently of the evaluation metric used to measure the performance. Quantitative performance results are obtained with state of the art approaches together with adaptations proposed in the current work. The options evaluated in the current work result from the combination of different setups in the wavelet image decomposition stage together with different fusion strategies for the final merging stage that generates the resulting representation. Most of the approaches evaluate results according to the application for which they are intended for. Sometimes a human observer is selected to judge the quality of the obtained results. In the current work, quantitative values are considered in order to find correlations between setups and performance of obtained results; these correlations can be used to define a criteria for selecting the best fusion strategy for a given pair of cross-spectral images. The whole procedure is evaluated with a large set of correctly registered visible and infrared image pairs, including both Near InfraRed (NIR) and Long Wave InfraRed (LWIR). |
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ADAS; 600.086; 600.076 |
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Admin @ si @SCA2016 |
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2807 |
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Cristhian A. Aguilera-Carrasco; Angel Sappa; Cristhian Aguilera; Ricardo Toledo |
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Title |
Cross-Spectral Local Descriptors via Quadruplet Network |
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Journal Article |
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2017 |
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Sensors |
Abbreviated Journal ![sorted by Abbreviated Journal field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
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17 |
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4 |
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873 |
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This paper presents a novel CNN-based architecture, referred to as Q-Net, to learn local feature descriptors that are useful for matching image patches from two different spectral bands. Given correctly matched and non-matching cross-spectral image pairs, a quadruplet network is trained to map input image patches to a common Euclidean space, regardless of the input spectral band. Our approach is inspired by the recent success of triplet networks in the visible spectrum, but adapted for cross-spectral scenarios, where, for each matching pair, there are always two possible non-matching patches: one for each spectrum. Experimental evaluations on a public cross-spectral VIS-NIR dataset shows that the proposed approach improves the state-of-the-art. Moreover, the proposed technique can also be used in mono-spectral settings, obtaining a similar performance to triplet network descriptors, but requiring less training data. |
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ADAS; 600.086; 600.118 |
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Admin @ si @ ASA2017 |
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2914 |
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Zhijie Fang; David Vazquez; Antonio Lopez |
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Title |
On-Board Detection of Pedestrian Intentions |
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Journal Article |
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2017 |
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Sensors |
Abbreviated Journal ![sorted by Abbreviated Journal field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
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17 |
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10 |
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2193 |
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pedestrian intention; ADAS; self-driving |
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Avoiding vehicle-to-pedestrian crashes is a critical requirement for nowadays advanced driver assistant systems (ADAS) and future self-driving vehicles. Accordingly, detecting pedestrians from raw sensor data has a history of more than 15 years of research, with vision playing a central role.
During the last years, deep learning has boosted the accuracy of image-based pedestrian detectors.
However, detection is just the first step towards answering the core question, namely is the vehicle going to crash with a pedestrian provided preventive actions are not taken? Therefore, knowing as soon as possible if a detected pedestrian has the intention of crossing the road ahead of the vehicle is
essential for performing safe and comfortable maneuvers that prevent a crash. However, compared to pedestrian detection, there is relatively little literature on detecting pedestrian intentions. This paper aims to contribute along this line by presenting a new vision-based approach which analyzes the
pose of a pedestrian along several frames to determine if he or she is going to enter the road or not. We present experiments showing 750 ms of anticipation for pedestrians crossing the road, which at a typical urban driving speed of 50 km/h can provide 15 additional meters (compared to a pure pedestrian detector) for vehicle automatic reactions or to warn the driver. Moreover, in contrast with state-of-the-art methods, our approach is monocular, neither requiring stereo nor optical flow information. |
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ADAS; 600.085; 600.076; 601.223; 600.116; 600.118 |
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Admin @ si @ FVL2017 |
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2983 |
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Xavier Soria; Angel Sappa; Riad I. Hammoud |
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Title |
Wide-Band Color Imagery Restoration for RGB-NIR Single Sensor Images |
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Journal Article |
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2018 |
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Sensors |
Abbreviated Journal ![sorted by Abbreviated Journal field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
SENS |
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18 |
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7 |
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2059 |
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RGB-NIR sensor; multispectral imaging; deep learning; CNNs |
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Multi-spectral RGB-NIR sensors have become ubiquitous in recent years. These sensors allow the visible and near-infrared spectral bands of a given scene to be captured at the same time. With such cameras, the acquired imagery has a compromised RGB color representation due to near-infrared bands (700–1100 nm) cross-talking with the visible bands (400–700 nm).
This paper proposes two deep learning-based architectures to recover the full RGB color images, thus removing the NIR information from the visible bands. The proposed approaches directly restore the high-resolution RGB image by means of convolutional neural networks. They are evaluated with several outdoor images; both architectures reach a similar performance when evaluated in different
scenarios and using different similarity metrics. Both of them improve the state of the art approaches. |
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ADAS; MSIAU; 600.086; 600.130; 600.122; 600.118 |
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Admin @ si @ SSH2018 |
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3145 |
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