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Fahad Shahbaz Khan; Muhammad Anwer Rao; Joost Van de Weijer; Michael Felsberg; J.Laaksonen |
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Title |
Compact color texture description for texture classification |
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Journal Article |
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2015 |
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Pattern Recognition Letters |
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PRL |
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51 |
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16-22 |
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Describing textures is a challenging problem in computer vision and pattern recognition. The classification problem involves assigning a category label to the texture class it belongs to. Several factors such as variations in scale, illumination and viewpoint make the problem of texture description extremely challenging. A variety of histogram based texture representations exists in literature.
However, combining multiple texture descriptors and assessing their complementarity is still an open research problem. In this paper, we first show that combining multiple local texture descriptors significantly improves the recognition performance compared to using a single best method alone. This
gain in performance is achieved at the cost of high-dimensional final image representation. To counter this problem, we propose to use an information-theoretic compression technique to obtain a compact texture description without any significant loss in accuracy. In addition, we perform a comprehensive
evaluation of pure color descriptors, popular in object recognition, for the problem of texture classification. Experiments are performed on four challenging texture datasets namely, KTH-TIPS-2a, KTH-TIPS-2b, FMD and Texture-10. The experiments clearly demonstrate that our proposed compact multi-texture approach outperforms the single best texture method alone. In all cases, discriminative color names outperforms other color features for texture classification. Finally, we show that combining discriminative color names with compact texture representation outperforms state-of-the-art methods by 7:8%, 4:3% and 5:0% on KTH-TIPS-2a, KTH-TIPS-2b and Texture-10 datasets respectively. |
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LAMP; 600.068; 600.079;ADAS |
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Admin @ si @ KRW2015a |
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2587 |
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Author |
Meysam Madadi; Sergio Escalera; Jordi Gonzalez; Xavier Roca; Felipe Lumbreras |
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Title |
Multi-part body segmentation based on depth maps for soft biometry analysis |
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2015 |
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Pattern Recognition Letters |
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PRL |
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56 |
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14-21 |
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3D shape context; 3D point cloud alignment; Depth maps; Human body segmentation; Soft biometry analysis |
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This paper presents a novel method extracting biometric measures using depth sensors. Given a multi-part labeled training data, a new subject is aligned to the best model of the dataset, and soft biometrics such as lengths or circumference sizes of limbs and body are computed. The process is performed by training relevant pose clusters, defining a representative model, and fitting a 3D shape context descriptor within an iterative matching procedure. We show robust measures by applying orthogonal plates to body hull. We test our approach in a novel full-body RGB-Depth data set, showing accurate estimation of soft biometrics and better segmentation accuracy in comparison with random forest approach without requiring large training data. |
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HuPBA; ISE; ADAS; 600.076;600.049; 600.063; 600.054; 302.018;MILAB |
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Admin @ si @ MEG2015 |
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2588 |
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Author |
Miguel Oliveira; Victor Santos; Angel Sappa; P. Dias; A. Moreira |
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Title |
Incremental Scenario Representations for Autonomous Driving using Geometric Polygonal Primitives |
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Journal Article |
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2016 |
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Robotics and Autonomous Systems |
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RAS |
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83 |
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312-325 |
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Incremental scene reconstruction; Point clouds; Autonomous vehicles; Polygonal primitives |
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When an autonomous vehicle is traveling through some scenario it receives a continuous stream of sensor data. This sensor data arrives in an asynchronous fashion and often contains overlapping or redundant information. Thus, it is not trivial how a representation of the environment observed by the vehicle can be created and updated over time. This paper presents a novel methodology to compute an incremental 3D representation of a scenario from 3D range measurements. We propose to use macro scale polygonal primitives to model the scenario. This means that the representation of the scene is given as a list of large scale polygons that describe the geometric structure of the environment. Furthermore, we propose mechanisms designed to update the geometric polygonal primitives over time whenever fresh sensor data is collected. Results show that the approach is capable of producing accurate descriptions of the scene, and that it is computationally very efficient when compared to other reconstruction techniques. |
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Elsevier B.V. |
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ADAS; 600.086, 600.076 |
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Admin @ si @OSS2016a |
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2806 |
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Author |
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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Year |
2016 |
Publication |
Robotics and Autonomous Systems |
Abbreviated Journal |
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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no |
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Admin @ si @SAC2016 |
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2811 |
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Author |
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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Year |
2016 |
Publication |
Robotics and Autonomous Systems |
Abbreviated Journal |
RAS |
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Volume |
84 |
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Pages |
113-128 |
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Keywords |
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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no |
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Admin @ si @ OSS2016b |
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2912 |
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Author |
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 |
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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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ADAS |
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no |
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Admin @ si @ |
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3857 |
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Author |
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 |
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SENS |
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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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ADAS |
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Admin @ si @ ABL2012 |
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2154 |
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Author |
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 |
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SENS |
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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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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 |
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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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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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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