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Author |
Akhil Gurram; Onay Urfalioglu; Ibrahim Halfaoui; Fahd Bouzaraa; Antonio Lopez |
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Title |
Semantic Monocular Depth Estimation Based on Artificial Intelligence |
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Journal Article |
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Year |
2020 |
Publication |
IEEE Intelligent Transportation Systems Magazine |
Abbreviated Journal |
ITSM |
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Volume |
13 |
Issue |
4 |
Pages |
99-103 |
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Abstract |
Depth estimation provides essential information to perform autonomous driving and driver assistance. A promising line of work consists of introducing additional semantic information about the traffic scene when training CNNs for depth estimation. In practice, this means that the depth data used for CNN training is complemented with images having pixel-wise semantic labels where the same raw training data is associated with both types of ground truth, i.e., depth and semantic labels. The main contribution of this paper is to show that this hard constraint can be circumvented, i.e., that we can train CNNs for depth estimation by leveraging the depth and semantic information coming from heterogeneous datasets. In order to illustrate the benefits of our approach, we combine KITTI depth and Cityscapes semantic segmentation datasets, outperforming state-of-the-art results on monocular depth estimation. |
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ADAS; 600.124; 600.118 |
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no |
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Admin @ si @ GUH2019 |
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3306 |
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Author |
Xinhang Song; Shuqiang Jiang; Luis Herranz |
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Title |
Combining Models from Multiple Sources for RGB-D Scene Recognition |
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Conference Article |
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Year |
2017 |
Publication |
26th International Joint Conference on Artificial Intelligence |
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4523-4529 |
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Keywords |
Robotics and Vision; Vision and Perception |
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Abstract |
Depth can complement RGB with useful cues about object volumes and scene layout. However, RGB-D image datasets are still too small for directly training deep convolutional neural networks (CNNs), in contrast to the massive monomodal RGB datasets. Previous works in RGB-D recognition typically combine two separate networks for RGB and depth data, pretrained with a large RGB dataset and then fine tuned to the respective target RGB and depth datasets. These approaches have several limitations: 1) only use low-level filters learned from RGB data, thus not being able to exploit properly depth-specific patterns, and 2) RGB and depth features are only combined at high-levels but rarely at lower-levels. In this paper, we propose a framework that leverages both knowledge acquired from large RGB datasets together with depth-specific cues learned from the limited depth data, obtaining more effective multi-source and multi-modal representations. We propose a multi-modal combination method that selects discriminative combinations of layers from the different source models and target modalities, capturing both high-level properties of the task and intrinsic low-level properties of both modalities. |
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Melbourne; Australia; August 2017 |
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IJCAI |
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LAMP; 600.120 |
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no |
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Admin @ si @ SJH2017b |
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2966 |
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Author |
Anders Skaarup Johansen; Kamal Nasrollahi; Sergio Escalera; Thomas B. Moeslund |
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Title |
Who Cares about the Weather? Inferring Weather Conditions for Weather-Aware Object Detection in Thermal Images |
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Journal Article |
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Year |
2023 |
Publication |
Applied Sciences |
Abbreviated Journal |
AS |
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13 |
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18 |
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Keywords |
thermal; object detection; concept drift; conditioning; weather recognition |
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Abstract |
Deployments of real-world object detection systems often experience a degradation in performance over time due to concept drift. Systems that leverage thermal cameras are especially susceptible because the respective thermal signatures of objects and their surroundings are highly sensitive to environmental changes. In this study, two types of weather-aware latent conditioning methods are investigated. The proposed method aims to guide two object detectors, (YOLOv5 and Deformable DETR) to become weather-aware. This is achieved by leveraging an auxiliary branch that predicts weather-related information while conditioning intermediate layers of the object detector. While the conditioning methods proposed do not directly improve the accuracy of baseline detectors, it can be observed that conditioned networks manage to extract a weather-related signal from the thermal images, thus resulting in a decreased miss rate at the cost of increased false positives. The extracted signal appears noisy and is thus challenging to regress accurately. This is most likely a result of the qualitative nature of the thermal sensor; thus, further work is needed to identify an ideal method for optimizing the conditioning branch, as well as to further improve the accuracy of the system. |
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HUPBA |
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no |
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Admin @ si @ SNE2023 |
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3983 |
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Author |
Daniel Hernandez; Alejandro Chacon; Antonio Espinosa; David Vazquez; Juan Carlos Moure; Antonio Lopez |
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Title |
Embedded real-time stereo estimation via Semi-Global Matching on the GPU |
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Conference Article |
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Year |
2016 |
Publication |
16th International Conference on Computational Science |
Abbreviated Journal |
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Volume |
80 |
Issue |
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Pages |
143-153 |
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Keywords |
Autonomous Driving; Stereo; CUDA; 3d reconstruction |
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Dense, robust and real-time computation of depth information from stereo-camera systems is a computationally demanding requirement for robotics, advanced driver assistance systems (ADAS) and autonomous vehicles. Semi-Global Matching (SGM) is a widely used algorithm that propagates consistency constraints along several paths across the image. This work presents a real-time system producing reliable disparity estimation results on the new embedded energy-efficient GPU devices. Our design runs on a Tegra X1 at 41 frames per second for an image size of 640x480, 128 disparity levels, and using 4 path directions for the SGM method. |
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San Diego; CA; USA; June 2016 |
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ADAS; 600.085; 600.082; 600.076 |
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no |
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Call Number |
ADAS @ adas @ HCE2016a |
Serial |
2740 |
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Author |
Daniel Hernandez; Alejandro Chacon; Antonio Espinosa; David Vazquez; Juan Carlos Moure; Antonio Lopez |
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Title |
Stereo Matching using SGM on the GPU |
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Report |
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2016 |
Publication |
Programming and Tuning Massively Parallel Systems |
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PUMPS |
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Keywords |
CUDA; Stereo; Autonomous Vehicle |
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Abstract |
Dense, robust and real-time computation of depth information from stereo-camera systems is a computationally demanding requirement for robotics, advanced driver assistance systems (ADAS) and autonomous vehicles. Semi-Global Matching (SGM) is a widely used algorithm that propagates consistency constraints along several paths across the image. This work presents a real-time system producing reliable disparity estimation results on the new embedded energy efficient GPU devices. Our design runs on a Tegra X1 at 42 frames per second (fps) for an image size of 640x480, 128 disparity levels, and using 4 path directions for the SGM method. |
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PUMPS |
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ADAS; 600.085; 600.087; 600.076 |
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no |
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ADAS @ adas @ HCE2016b |
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2776 |
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Author |
Jose Manuel Alvarez; Felipe Lumbreras; Antonio Lopez; Theo Gevers |
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Title |
Understanding Road Scenes using Visual Cues |
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Miscellaneous |
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2012 |
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European Conference on Computer Vision |
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DEMO |
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Florence; Italy |
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ISE |
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no |
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Admin @ si @ ALL2012 |
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2795 |
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Author |
Antoni Gurgui; Debora Gil; Enric Marti |
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Title |
Laplacian Unitary Domain for Texture Morphing |
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Conference Article |
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Year |
2015 |
Publication |
Proceedings of the 10th International Conference on Computer Vision Theory and Applications VISIGRAPP2015 |
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1 |
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693-699 |
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Keywords |
Facial; metamorphosis;LaplacianMorphing |
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Deformation of expressive textures is the gateway to realistic computer synthesis of expressions. By their good mathematical properties and flexible formulation on irregular meshes, most texture mappings rely on solutions to the Laplacian in the cartesian space. In the context of facial expression morphing, this approximation can be seen from the opposite point of view by neglecting the metric. In this paper, we use the properties of the Laplacian in manifolds to present a novel approach to warping expressive facial images in order to generate a morphing between them. |
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Munich; Germany; February 2015 |
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SciTePress |
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978-989-758-089-5 |
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VISAPP |
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Notes |
IAM; 600.075 |
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no |
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Call Number |
Admin @ si @ GGM2015 |
Serial |
2614 |
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Author |
Josep M. Gonfaus; Marco Pedersoli; Jordi Gonzalez; Andrea Vedaldi; Xavier Roca |
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Title |
Factorized appearances for object detection |
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Journal Article |
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Year |
2015 |
Publication |
Computer Vision and Image Understanding |
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CVIU |
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138 |
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92–101 |
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Object recognition; Deformable part models; Learning and sharing parts; Discovering discriminative parts |
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Deformable object models capture variations in an object’s appearance that can be represented as image deformations. Other effects such as out-of-plane rotations, three-dimensional articulations, and self-occlusions are often captured by considering mixture of deformable models, one per object aspect. A more scalable approach is representing instead the variations at the level of the object parts, applying the concept of a mixture locally. Combining a few part variations can in fact cheaply generate a large number of global appearances.
A limited version of this idea was proposed by Yang and Ramanan [1], for human pose dectection. In this paper we apply it to the task of generic object category detection and extend it in several ways. First, we propose a model for the relationship between part appearances more general than the tree of Yang and Ramanan [1], which is more suitable for generic categories. Second, we treat part locations as well as their appearance as latent variables so that training does not need part annotations but only the object bounding boxes. Third, we modify the weakly-supervised learning of Felzenszwalb et al. and Girshick et al. [2], [3] to handle a significantly more complex latent structure.
Our model is evaluated on standard object detection benchmarks and is found to improve over existing approaches, yielding state-of-the-art results for several object categories. |
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ISE; 600.063; 600.078 |
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no |
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Call Number |
Admin @ si @ GPG2015 |
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2705 |
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Author |
Petia Radeva; Enric Marti |
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Title |
Facial Features Segmentation by Model-Based Snakes |
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Conference Article |
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Year |
1995 |
Publication |
International Conference on Computing Analysis and Image Processing |
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Deformable models have recently been accepted as a standard technique to segment different features in facial images. Despite they give a good approximation of the salient features in a facial image, the resulting shapes of the segmentation process seem somewhat artificial with respect to the natural feature shapes. In this paper we show that active contour models (in particular, rubber snakes) give more close and natural representation of the detected feature shape. Besides, using snakes for facial segmentation frees us from the problem of determination of the numerous weigths of deformable models. Another advantage of rubber snakes is their reduced computational cost. Our experiments using rubber snakes for segmentation of facial snapshots have shown a significant improvement compared to deformable models. |
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Bellaterra (Barcelona), Spain |
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MILAB;IAM |
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no |
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IAM @ iam @ RAM1995a |
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1633 |
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Author |
Oriol Pujol; Petia Radeva |
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Texture Segmentation by Statistical Deformable Models |
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2004 |
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International Journal of Image and Graphics |
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IJIG |
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4 |
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3 |
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433-452 |
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Texture segmentation, parametric active contours, statistic snakes |
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Deformable models have received much popularity due to their ability to include high-level knowledge on the application domain into low-level image processing. Still, most proposed active contour models do not sufficiently profit from the application information and they are too generalized, leading to non-optimal final results of segmentation, tracking or 3D reconstruction processes. In this paper we propose a new deformable model defined in a statistical framework to segment objects of natural scenes. We perform a supervised learning of local appearance of the textured objects and construct a feature space using a set of co-occurrence matrix measures. Linear Discriminant Analysis allows us to obtain an optimal reduced feature space where a mixture model is applied to construct a likelihood map. Instead of using a heuristic potential field, our active model is deformed on a regularized version of the likelihood map in order to segment objects characterized by the same texture pattern. Different tests on synthetic images, natural scene and medical images show the advantages of our statistic deformable model. |
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MILAB;HuPBA |
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no |
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BCNPCL @ bcnpcl @ PuR2004a |
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505 |
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Author |
Cesar de Souza; Adrien Gaidon; Yohann Cabon; Naila Murray; Antonio Lopez |
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Title |
Generating Human Action Videos by Coupling 3D Game Engines and Probabilistic Graphical Models |
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Journal Article |
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2020 |
Publication |
International Journal of Computer Vision |
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IJCV |
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128 |
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1505–1536 |
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Procedural generation; Human action recognition; Synthetic data; Physics |
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Deep video action recognition models have been highly successful in recent years but require large quantities of manually-annotated data, which are expensive and laborious to obtain. In this work, we investigate the generation of synthetic training data for video action recognition, as synthetic data have been successfully used to supervise models for a variety of other computer vision tasks. We propose an interpretable parametric generative model of human action videos that relies on procedural generation, physics models and other components of modern game engines. With this model we generate a diverse, realistic, and physically plausible dataset of human action videos, called PHAV for “Procedural Human Action Videos”. PHAV contains a total of 39,982 videos, with more than 1000 examples for each of 35 action categories. Our video generation approach is not limited to existing motion capture sequences: 14 of these 35 categories are procedurally-defined synthetic actions. In addition, each video is represented with 6 different data modalities, including RGB, optical flow and pixel-level semantic labels. These modalities are generated almost simultaneously using the Multiple Render Targets feature of modern GPUs. In order to leverage PHAV, we introduce a deep multi-task (i.e. that considers action classes from multiple datasets) representation learning architecture that is able to simultaneously learn from synthetic and real video datasets, even when their action categories differ. Our experiments on the UCF-101 and HMDB-51 benchmarks suggest that combining our large set of synthetic videos with small real-world datasets can boost recognition performance. Our approach also significantly outperforms video representations produced by fine-tuning state-of-the-art unsupervised generative models of videos. |
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ADAS; 600.124; 600.118 |
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no |
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Admin @ si @ SGC2019 |
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3303 |
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Author |
Debora Gil; Agnes Borras; Manuel Ballester; Francesc Carreras; Ruth Aris; Manuel Vazquez; Enric Marti; Ferran Poveda |
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MIOCARDIA: Integrating cardiac function and muscular architecture for a better diagnosis |
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Conference Article |
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2011 |
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14th International Symposium on Applied Sciences in Biomedical and Communication Technologies |
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Deep understanding of myocardial structure of the heart would unravel crucial knowledge for clinical and medical procedures. The MIOCARDIA project is a multidisciplinary project in cooperation with l'Hospital de la Santa Creu i de Sant Pau, Clinica la Creu Blanca and Barcelona Supercomputing Center. The ultimate goal of this project is defining a computational model of the myocardium. The model takes into account the deep interrelation between the anatomy and the mechanics of the heart. The paper explains the workflow of the MIOCARDIA project. It also introduces a multiresolution reconstruction technique based on DT-MRI streamlining for simplified global myocardial model generation. Our reconstructions can restore the most complex myocardial structures and provides evidences of a global helical organization. |
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Barcelona; Spain |
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Association for Computing Machinery |
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Barcelona, Spain |
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Association for Computing Machinery |
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english |
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english |
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978-1-4503-0913-4 |
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ISABEL |
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IAM |
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no |
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IAM @ iam @ GGB2011 |
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1691 |
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Permanent link to this record |
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Author |
Ferran Poveda; Jaume Garcia; Enric Marti; Debora Gil |
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Title |
Validation of the myocardial architecture in DT-MRI tractography |
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Conference Article |
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2010 |
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Medical Image Computing in Catalunya: Graduate Student Workshop |
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29-30 |
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Deep understanding of myocardial structure may help to link form and funcion of the heart unraveling crucial knowledge for medical and surgical clinical procedures and studies. In this work we introduce two visualization techniques based on DT-MRI streamlining able to decipher interesting properties of the architectural organization of the heart. |
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Girona (Spain) |
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MICCAT |
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IAM @ iam @ PGM2010 |
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1626 |
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Author |
Ferran Poveda; Debora Gil; Enric Marti; Albert Andaluz; Manel Ballester;Francesc Carreras Costa |
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Helical structure of the cardiac ventricular anatomy assessed by Diffusion Tensor Magnetic Resonance Imaging multi-resolution tractography |
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Journal Article |
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2013 |
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Revista Española de Cardiología |
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REC |
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66 |
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10 |
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782-790 |
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Heart;Diffusion magnetic resonance imaging;Diffusion tractography;Helical heart;Myocardial ventricular band. |
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Abstract |
Deep understanding of myocardial structure linking morphology and function of the heart would unravel crucial knowledge for medical and surgical clinical procedures and studies. Several conceptual models of myocardial fiber organization have been proposed but the lack of an automatic and objective methodology prevented an agreement. We sought to deepen in this knowledge through advanced computer graphic representations of the myocardial fiber architecture by diffusion tensor magnetic resonance imaging (DT-MRI).
We performed automatic tractography reconstruction of unsegmented DT-MRI canine heart datasets coming from the public database of the Johns Hopkins University. Full scale tractographies have been build with 200 seeds and are composed by streamlines computed on the vectorial field of primary eigenvectors given at the diffusion tensor volumes. Also, we introduced a novel multi-scale visualization technique in order to obtain a simplified tractography. This methodology allowed to keep the main geometric features of the fiber tracts, making easier to decipher the main properties of the architectural organization of the heart.
On the analysis of the output from our tractographic representations we found exact correlation with low-level details of myocardial architecture, but also with the more abstract conceptualization of a continuous helical ventricular myocardial fiber array.
Objective analysis of myocardial architecture by an automated method, including the entire myocardium and using several 3D levels of complexity, reveals a continuous helical myocardial fiber arrangement of both right and left ventricles, supporting the anatomical model of the helical ventricular myocardial band described by Torrent-Guasp. |
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Elsevier |
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IAM; 600.044; 600.060 |
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IAM @ iam @ PGM2013 |
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2194 |
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Alberto Hidalgo; Ferran Poveda; Enric Marti;Debora Gil;Albert Andaluz; Francesc Carreras; Manuel Ballester |
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Evidence of continuous helical structure of the cardiac ventricular anatomy assessed by diffusion tensor imaging magnetic resonance multiresolution tractography |
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2012 |
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European Radiology |
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ECR |
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3 |
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1 |
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361-362 |
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Abstract |
Deep understanding of myocardial structure linking morphology and func- tion of the heart would unravel crucial knowledge for medical and surgical clinical procedures and studies. Diffusion tensor MRI provides a discrete measurement of the 3D arrangement of myocardial fibres by the observation of local anisotropic
diffusion of water molecules in biological tissues. In this work, we present a multi- scale visualisation technique based on DT-MRI streamlining capable of uncovering additional properties of the architectural organisation of the heart. Methods and Materials: We selected the John Hopkins University (JHU) Canine Heart Dataset, where the long axis cardiac plane is aligned with the scanner’s Z- axis. Their equipment included a 4-element passed array coil emitting a 1.5 T. For DTI acquisition, a 3D-FSE sequence is apply. We used 200 seeds for full-scale tractography, while we applied a MIP mapping technique for simplified tractographic reconstruction. In this case, we reduced each DTI 3D volume dimensions by order- two magnitude before streamlining.
Our simplified tractographic reconstruction method keeps the main geometric features of fibres, allowing for an easier identification of their global morphological disposition, including the ventricular basal ring. Moreover, we noticed a clearly visible helical disposition of the myocardial fibres, in line with the helical myocardial band ventricular structure described by Torrent-Guasp. Finally, our simplified visualisation with single tracts identifies the main segments of the helical ventricular architecture.
DT-MRI makes possible the identification of a continuous helical architecture of the myocardial fibres, which validates Torrent-Guasp’s helical myocardial band ventricular anatomical model. |
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Viena, Austria |
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Springer Link |
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1869-4101 |
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IAM @ iam @ HPM2012 |
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1858 |
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