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Author | Cesar Isaza; Joaquin Salas; Bogdan Raducanu | ||||
Title | Evaluation of Intrinsic Image Algorithms to Detect the Shadows Cast by Static Objects Outdoors | Type | Journal Article | ||
Year | 2012 | Publication ![]() |
Sensors | Abbreviated Journal | SENS |
Volume | 12 | Issue | 10 | Pages | 13333-13348 |
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Abstract | In some automatic scene analysis applications, the presence of shadows becomes a nuisance that is necessary to deal with. As a consequence, a preliminary stage in many computer vision algorithms is to attenuate their effect. In this paper, we focus our attention on the detection of shadows cast by static objects outdoors, as the scene is viewed for extended periods of time (days, weeks) from a fixed camera and considering daylight intervals where the main source of light is the sun. In this context, we report two contributions. First, we introduce the use of synthetic images for which ground truth can be generated automatically, avoiding the tedious effort of manual annotation. Secondly, we report a novel application of the intrinsic image concept to the automatic detection of shadows cast by static objects in outdoors. We make both a quantitative and a qualitative evaluation of several algorithms based on this image representation. For the quantitative evaluation, we used the synthetic data set, while for the qualitative evaluation we used both data sets. Our experimental results show that the evaluated methods can partially solve the problem of shadow detection. | ||||
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Notes | OR;MV | Approved | no | ||
Call Number | Admin @ si @ ISR2012b | Serial | 2173 | ||
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Author | Xavier Perez Sala; Sergio Escalera; Cecilio Angulo; Jordi Gonzalez | ||||
Title | A survey on model based approaches for 2D and 3D visual human pose recovery | Type | Journal Article | ||
Year | 2014 | Publication ![]() |
Sensors | Abbreviated Journal | SENS |
Volume | 14 | Issue | 3 | Pages | 4189-4210 |
Keywords | human pose recovery; human body modelling; behavior analysis; computer vision | ||||
Abstract | Human Pose Recovery has been studied in the field of Computer Vision for the last 40 years. Several approaches have been reported, and significant improvements have been obtained in both data representation and model design. However, the problem of Human Pose Recovery in uncontrolled environments is far from being solved. In this paper, we define a general taxonomy to group model based approaches for Human Pose Recovery, which is composed of five main modules: appearance, viewpoint, spatial relations, temporal consistence, and behavior. Subsequently, a methodological comparison is performed following the proposed taxonomy, evaluating current SoA approaches in the aforementioned five group categories. As a result of this comparison, we discuss the main advantages and drawbacks of the reviewed literature. | ||||
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Notes | HuPBA; ISE; 600.046; 600.063; 600.078;MILAB | Approved | no | ||
Call Number | Admin @ si @ PEA2014 | Serial | 2443 | ||
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Author | P. Ricaurte ; C. Chilan; Cristhian A. Aguilera-Carrasco; Boris X. Vintimilla; Angel Sappa | ||||
Title | Feature Point Descriptors: Infrared and Visible Spectra | Type | Journal Article | ||
Year | 2014 | Publication ![]() |
Sensors | Abbreviated Journal | SENS |
Volume | 14 | Issue | 2 | Pages | 3690-3701 |
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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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Notes | ADAS;600.055; 600.076 | Approved | no | ||
Call Number | Admin @ si @ RCA2014a | Serial | 2474 | ||
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Author | Alejandro Gonzalez Alzate; Zhijie Fang; Yainuvis Socarras; Joan Serrat; David Vazquez; Jiaolong Xu; Antonio Lopez | ||||
Title | Pedestrian Detection at Day/Night Time with Visible and FIR Cameras: A Comparison | Type | Journal Article | ||
Year | 2016 | Publication ![]() |
Sensors | Abbreviated Journal | SENS |
Volume | 16 | Issue | 6 | Pages | 820 |
Keywords | Pedestrian Detection; FIR | ||||
Abstract | 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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ISSN | 1424-8220 | ISBN | Medium | ||
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Notes | ADAS; 600.085; 600.076; 600.082; 601.281 | Approved | no | ||
Call Number | ADAS @ adas @ GFS2016 | Serial | 2754 | ||
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Author | Angel Sappa; P. Carvajal; Cristhian A. Aguilera-Carrasco; Miguel Oliveira; Dennis Romero; Boris X. Vintimilla | ||||
Title | Wavelet based visible and infrared image fusion: a comparative study | Type | Journal Article | ||
Year | 2016 | Publication ![]() |
Sensors | Abbreviated Journal | SENS |
Volume | 16 | Issue | 6 | Pages | 1-15 |
Keywords | Image fusion; fusion evaluation metrics; visible and infrared imaging; discrete wavelet transform | ||||
Abstract | 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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Notes | ADAS; 600.086; 600.076 | Approved | no | ||
Call Number | Admin @ si @SCA2016 | Serial | 2807 | ||
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Author | Cristhian A. Aguilera-Carrasco; Angel Sappa; Cristhian Aguilera; Ricardo Toledo | ||||
Title | Cross-Spectral Local Descriptors via Quadruplet Network | Type | Journal Article | ||
Year | 2017 | Publication ![]() |
Sensors | Abbreviated Journal | SENS |
Volume | 17 | Issue | 4 | Pages | 873 |
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Abstract | 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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Notes | ADAS; 600.086; 600.118 | Approved | no | ||
Call Number | Admin @ si @ ASA2017 | Serial | 2914 | ||
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Author | Wenjuan Gong; Xuena Zhang; Jordi Gonzalez; Andrews Sobral; Thierry Bouwmans; Changhe Tu; El-hadi Zahzah | ||||
Title | Human Pose Estimation from Monocular Images: A Comprehensive Survey | Type | Journal Article | ||
Year | 2016 | Publication ![]() |
Sensors | Abbreviated Journal | SENS |
Volume | 16 | Issue | 12 | Pages | 1966 |
Keywords | human pose estimation; human bodymodels; generativemethods; discriminativemethods; top-down methods; bottom-up methods | ||||
Abstract | Human pose estimation refers to the estimation of the location of body parts and how they are connected in an image. Human pose estimation from monocular images has wide applications (e.g., image indexing). Several surveys on human pose estimation can be found in the literature, but they focus on a certain category; for example, model-based approaches or human motion analysis, etc. As far as we know, an overall review of this problem domain has yet to be provided. Furthermore, recent advancements based on deep learning have brought novel algorithms for this problem. In this paper, a comprehensive survey of human pose estimation from monocular images is carried out including milestone works and recent advancements. Based on one standard pipeline for the solution of computer vision problems, this survey splits the problem into several modules: feature extraction and description, human body models, and modeling
methods. Problem modeling methods are approached based on two means of categorization in this survey. One way to categorize includes top-down and bottom-up methods, and another way includes generative and discriminative methods. Considering the fact that one direct application of human pose estimation is to provide initialization for automatic video surveillance, there are additional sections for motion-related methods in all modules: motion features, motion models, and motion-based methods. Finally, the paper also collects 26 publicly available data sets for validation and provides error measurement methods that are frequently used. |
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Notes | ISE; 600.098; 600.119 | Approved | no | ||
Call Number | Admin @ si @ GZG2016 | Serial | 2933 | ||
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Author | Zhijie Fang; David Vazquez; Antonio Lopez | ||||
Title | On-Board Detection of Pedestrian Intentions | Type | Journal Article | ||
Year | 2017 | Publication ![]() |
Sensors | Abbreviated Journal | SENS |
Volume | 17 | Issue | 10 | Pages | 2193 |
Keywords | pedestrian intention; ADAS; self-driving | ||||
Abstract | 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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Notes | ADAS; 600.085; 600.076; 601.223; 600.116; 600.118 | Approved | no | ||
Call Number | Admin @ si @ FVL2017 | Serial | 2983 | ||
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Author | Mark Philip Philipsen; Jacob Velling Dueholm; Anders Jorgensen; Sergio Escalera; Thomas B. Moeslund | ||||
Title | Organ Segmentation in Poultry Viscera Using RGB-D | Type | Journal Article | ||
Year | 2018 | Publication ![]() |
Sensors | Abbreviated Journal | SENS |
Volume | 18 | Issue | 1 | Pages | 117 |
Keywords | semantic segmentation; RGB-D; random forest; conditional random field; 2D; 3D; CNN | ||||
Abstract | We present a pattern recognition framework for semantic segmentation of visual structures, that is, multi-class labelling at pixel level, and apply it to the task of segmenting organs in the eviscerated viscera from slaughtered poultry in RGB-D images. This is a step towards replacing the current strenuous manual inspection at poultry processing plants. Features are extracted from feature maps such as activation maps from a convolutional neural network (CNN). A random forest classifier assigns class probabilities, which are further refined by utilizing context in a conditional random field. The presented method is compatible with both 2D and 3D features, which allows us to explore the value of adding 3D and CNN-derived features. The dataset consists of 604 RGB-D images showing 151 unique sets of eviscerated viscera from four different perspectives. A mean Jaccard index of 78.11% is achieved across the four classes of organs by using features derived from 2D, 3D and a CNN, compared to 74.28% using only basic 2D image features. | ||||
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Notes | HUPBA; no proj | Approved | no | ||
Call Number | Admin @ si @ PVJ2018 | Serial | 3072 | ||
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Author | Xavier Soria; Angel Sappa; Riad I. Hammoud | ||||
Title | Wide-Band Color Imagery Restoration for RGB-NIR Single Sensor Images | Type | Journal Article | ||
Year | 2018 | Publication ![]() |
Sensors | Abbreviated Journal | SENS |
Volume | 18 | Issue | 7 | Pages | 2059 |
Keywords | RGB-NIR sensor; multispectral imaging; deep learning; CNNs | ||||
Abstract | 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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Notes | ADAS; MSIAU; 600.086; 600.130; 600.122; 600.118 | Approved | no | ||
Call Number | Admin @ si @ SSH2018 | Serial | 3145 | ||
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Author | Cristhian A. Aguilera-Carrasco; C. Aguilera; Angel Sappa | ||||
Title | Melamine Faced Panels Defect Classification beyond the Visible Spectrum | Type | Journal Article | ||
Year | 2018 | Publication ![]() |
Sensors | Abbreviated Journal | SENS |
Volume | 18 | Issue | 11 | Pages | 1-10 |
Keywords | industrial application; infrared; machine learning | ||||
Abstract | In this work, we explore the use of images from different spectral bands to classify defects in melamine faced panels, which could appear through the production process. Through experimental evaluation, we evaluate the use of images from the visible (VS), near-infrared (NIR), and long wavelength infrared (LWIR), to classify the defects using a feature descriptor learning approach together with a support vector machine classifier. Two descriptors were evaluated, Extended Local Binary Patterns (E-LBP) and SURF using a Bag of Words (BoW) representation. The evaluation was carried on with an image set obtained during this work, which contained five different defect categories that currently occurs in the industry. Results show that using images from beyond the visual spectrum helps to improve classification performance in contrast with a single visible spectrum solution. | ||||
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Notes | MSIAU; 600.122 | Approved | no | ||
Call Number | Admin @ si @ AAS2018 | Serial | 3191 | ||
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Author | Gabriel Villalonga; Joost Van de Weijer; Antonio Lopez | ||||
Title | Recognizing new classes with synthetic data in the loop: application to traffic sign recognition | Type | Journal Article | ||
Year | 2020 | Publication ![]() |
Sensors | Abbreviated Journal | SENS |
Volume | 20 | Issue | 3 | Pages | 583 |
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Abstract | On-board vision systems may need to increase the number of classes that can be recognized in a relatively short period. For instance, a traffic sign recognition system may suddenly be required to recognize new signs. Since collecting and annotating samples of such new classes may need more time than we wish, especially for uncommon signs, we propose a method to generate these samples by combining synthetic images and Generative Adversarial Network (GAN) technology. In particular, the GAN is trained on synthetic and real-world samples from known classes to perform synthetic-to-real domain adaptation, but applied to synthetic samples of the new classes. Using the Tsinghua dataset with a synthetic counterpart, SYNTHIA-TS, we have run an extensive set of experiments. The results show that the proposed method is indeed effective, provided that we use a proper Convolutional Neural Network (CNN) to perform the traffic sign recognition (classification) task as well as a proper GAN to transform the synthetic images. Here, a ResNet101-based classifier and domain adaptation based on CycleGAN performed extremely well for a ratio∼ 1/4 for new/known classes; even for more challenging ratios such as∼ 4/1, the results are also very positive. | ||||
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Notes | LAMP; ADAS; 600.118; 600.120 | Approved | no | ||
Call Number | Admin @ si @ VWL2020 | Serial | 3405 | ||
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Author | Cristhian A. Aguilera-Carrasco; Cristhian Aguilera; Cristobal A. Navarro; Angel Sappa | ||||
Title | Fast CNN Stereo Depth Estimation through Embedded GPU Devices | Type | Journal Article | ||
Year | 2020 | Publication ![]() |
Sensors | Abbreviated Journal | SENS |
Volume | 20 | Issue | 11 | Pages | 3249 |
Keywords | stereo matching; deep learning; embedded GPU | ||||
Abstract | Current CNN-based stereo depth estimation models can barely run under real-time constraints on embedded graphic processing unit (GPU) devices. Moreover, state-of-the-art evaluations usually do not consider model optimization techniques, being that it is unknown what is the current potential on embedded GPU devices. In this work, we evaluate two state-of-the-art models on three different embedded GPU devices, with and without optimization methods, presenting performance results that illustrate the actual capabilities of embedded GPU devices for stereo depth estimation. More importantly, based on our evaluation, we propose the use of a U-Net like architecture for postprocessing the cost-volume, instead of a typical sequence of 3D convolutions, drastically augmenting the runtime speed of current models. In our experiments, we achieve real-time inference speed, in the range of 5–32 ms, for 1216 × 368 input stereo images on the Jetson TX2, Jetson Xavier, and Jetson Nano embedded devices. | ||||
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Notes | MSIAU; 600.122 | Approved | no | ||
Call Number | Admin @ si @ AAN2020 | Serial | 3428 | ||
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Author | Angel Morera; Angel Sanchez; A. Belen Moreno; Angel Sappa; Jose F. Velez | ||||
Title | SSD vs. YOLO for Detection of Outdoor Urban Advertising Panels under Multiple Variabilities | Type | Journal Article | ||
Year | 2020 | Publication ![]() |
Sensors | Abbreviated Journal | SENS |
Volume | 20 | Issue | 16 | Pages | 4587 |
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Abstract | This work compares Single Shot MultiBox Detector (SSD) and You Only Look Once (YOLO) deep neural networks for the outdoor advertisement panel detection problem by handling multiple and combined variabilities in the scenes. Publicity panel detection in images offers important advantages both in the real world as well as in the virtual one. For example, applications like Google Street View can be used for Internet publicity and when detecting these ads panels in images, it could be possible to replace the publicity appearing inside the panels by another from a funding company. In our experiments, both SSD and YOLO detectors have produced acceptable results under variable sizes of panels, illumination conditions, viewing perspectives, partial occlusion of panels, complex background and multiple panels in scenes. Due to the difficulty of finding annotated images for the considered problem, we created our own dataset for conducting the experiments. The major strength of the SSD model was the almost elimination of False Positive (FP) cases, situation that is preferable when the publicity contained inside the panel is analyzed after detecting them. On the other side, YOLO produced better panel localization results detecting a higher number of True Positive (TP) panels with a higher accuracy. Finally, a comparison of the two analyzed object detection models with different types of semantic segmentation networks and using the same evaluation metrics is also included. | ||||
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Notes | MSIAU; 600.130; 601.349; 600.122 | Approved | no | ||
Call Number | Admin @ si @ MSM2020 | Serial | 3452 | ||
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Author | Jose Luis Gomez; Gabriel Villalonga; Antonio Lopez | ||||
Title | Co-Training for Deep Object Detection: Comparing Single-Modal and Multi-Modal Approaches | Type | Journal Article | ||
Year | 2021 | Publication ![]() |
Sensors | Abbreviated Journal | SENS |
Volume | 21 | Issue | 9 | Pages | 3185 |
Keywords | co-training; multi-modality; vision-based object detection; ADAS; self-driving | ||||
Abstract | Top-performing computer vision models are powered by convolutional neural networks (CNNs). Training an accurate CNN highly depends on both the raw sensor data and their associated ground truth (GT). Collecting such GT is usually done through human labeling, which is time-consuming and does not scale as we wish. This data-labeling bottleneck may be intensified due to domain shifts among image sensors, which could force per-sensor data labeling. In this paper, we focus on the use of co-training, a semi-supervised learning (SSL) method, for obtaining self-labeled object bounding boxes (BBs), i.e., the GT to train deep object detectors. In particular, we assess the goodness of multi-modal co-training by relying on two different views of an image, namely, appearance (RGB) and estimated depth (D). Moreover, we compare appearance-based single-modal co-training with multi-modal. Our results suggest that in a standard SSL setting (no domain shift, a few human-labeled data) and under virtual-to-real domain shift (many virtual-world labeled data, no human-labeled data) multi-modal co-training outperforms single-modal. In the latter case, by performing GAN-based domain translation both co-training modalities are on par, at least when using an off-the-shelf depth estimation model not specifically trained on the translated images. | ||||
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Notes | ADAS; 600.118 | Approved | no | ||
Call Number | Admin @ si @ GVL2021 | Serial | 3562 | ||
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