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Author |
A.F. Sole; S. Ngan; G. Sapiro; X. Hu; Antonio Lopez |
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Title |
Anisotropic 2-D and 3-D Averaging of fMRI Signals |
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2001 |
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IEEE Transactions on Medical Imaging |
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2020 |
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2 |
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86-93 |
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ADAS @ adas @ SNS2001 |
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165 |
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Author |
Ferran Diego; Joan Serrat; Antonio Lopez |
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Title |
Joint spatio-temporal alignment of sequences |
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2013 |
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IEEE Transactions on Multimedia |
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TMM |
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15 |
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6 |
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1377-1387 |
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Keywords |
video alignment |
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Video alignment is important in different areas of computer vision such as wide baseline matching, action recognition, change detection, video copy detection and frame dropping prevention. Current video alignment methods usually deal with a relatively simple case of fixed or rigidly attached cameras or simultaneous acquisition. Therefore, in this paper we propose a joint video alignment for bringing two video sequences into a spatio-temporal alignment. Specifically, the novelty of the paper is to formulate the video alignment to fold the spatial and temporal alignment into a single alignment framework. This simultaneously satisfies a frame-correspondence and frame-alignment similarity; exploiting the knowledge among neighbor frames by a standard pairwise Markov random field (MRF). This new formulation is able to handle the alignment of sequences recorded at different times by independent moving cameras that follows a similar trajectory, and also generalizes the particular cases that of fixed geometric transformation and/or linear temporal mapping. We conduct experiments on different scenarios such as sequences recorded simultaneously or by moving cameras to validate the robustness of the proposed approach. The proposed method provides the highest video alignment accuracy compared to the state-of-the-art methods on sequences recorded from vehicles driving along the same track at different times. |
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1520-9210 |
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ADAS |
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Admin @ si @ DSL2013; ADAS @ adas @ |
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2228 |
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Author |
Katerine Diaz; Francesc J. Ferri; W. Diaz |
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Title |
Incremental Generalized Discriminative Common Vectors for Image Classification |
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2015 |
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IEEE Transactions on Neural Networks and Learning Systems |
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TNNLS |
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26 |
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8 |
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1761 - 1775 |
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Subspace-based methods have become popular due to their ability to appropriately represent complex data in such a way that both dimensionality is reduced and discriminativeness is enhanced. Several recent works have concentrated on the discriminative common vector (DCV) method and other closely related algorithms also based on the concept of null space. In this paper, we present a generalized incremental formulation of the DCV methods, which allows the update of a given model by considering the addition of new examples even from unseen classes. Having efficient incremental formulations of well-behaved batch algorithms allows us to conveniently adapt previously trained classifiers without the need of recomputing them from scratch. The proposed generalized incremental method has been empirically validated in different case studies from different application domains (faces, objects, and handwritten digits) considering several different scenarios in which new data are continuously added at different rates starting from an initial model. |
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2162-237X |
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ADAS; 600.076 |
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Admin @ si @ DFD2015 |
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2547 |
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Author |
Daniel Hernandez; Antonio Espinosa; David Vazquez; Antonio Lopez; Juan C. Moure |
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Title |
3D Perception With Slanted Stixels on GPU |
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Year |
2021 |
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IEEE Transactions on Parallel and Distributed Systems |
Abbreviated Journal |
TPDS |
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Volume |
32 |
Issue |
10 |
Pages |
2434-2447 |
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Daniel Hernandez-Juarez; Antonio Espinosa; David Vazquez; Antonio M. Lopez; Juan C. Moure |
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This article presents a GPU-accelerated software design of the recently proposed model of Slanted Stixels, which represents the geometric and semantic information of a scene in a compact and accurate way. We reformulate the measurement depth model to reduce the computational complexity of the algorithm, relying on the confidence of the depth estimation and the identification of invalid values to handle outliers. The proposed massively parallel scheme and data layout for the irregular computation pattern that corresponds to a Dynamic Programming paradigm is described and carefully analyzed in performance terms. Performance is shown to scale gracefully on current generation embedded GPUs. We assess the proposed methods in terms of semantic and geometric accuracy as well as run-time performance on three publicly available benchmark datasets. Our approach achieves real-time performance with high accuracy for 2048 × 1024 image sizes and 4 × 4 Stixel resolution on the low-power embedded GPU of an NVIDIA Tegra Xavier. |
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ADAS; 600.124; 600.118 |
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Admin @ si @ HEV2021 |
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3561 |
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Author |
Jiaolong Xu; Sebastian Ramos; David Vazquez; Antonio Lopez |
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Title |
Domain Adaptation of Deformable Part-Based Models |
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Year |
2014 |
Publication |
IEEE Transactions on Pattern Analysis and Machine Intelligence |
Abbreviated Journal |
TPAMI |
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Volume |
36 |
Issue |
12 |
Pages |
2367-2380 |
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Keywords |
Domain Adaptation; Pedestrian Detection |
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The accuracy of object classifiers can significantly drop when the training data (source domain) and the application scenario (target domain) have inherent differences. Therefore, adapting the classifiers to the scenario in which they must operate is of paramount importance. We present novel domain adaptation (DA) methods for object detection. As proof of concept, we focus on adapting the state-of-the-art deformable part-based model (DPM) for pedestrian detection. We introduce an adaptive structural SVM (A-SSVM) that adapts a pre-learned classifier between different domains. By taking into account the inherent structure in feature space (e.g., the parts in a DPM), we propose a structure-aware A-SSVM (SA-SSVM). Neither A-SSVM nor SA-SSVM needs to revisit the source-domain training data to perform the adaptation. Rather, a low number of target-domain training examples (e.g., pedestrians) are used. To address the scenario where there are no target-domain annotated samples, we propose a self-adaptive DPM based on a self-paced learning (SPL) strategy and a Gaussian Process Regression (GPR). Two types of adaptation tasks are assessed: from both synthetic pedestrians and general persons (PASCAL VOC) to pedestrians imaged from an on-board camera. Results show that our proposals avoid accuracy drops as high as 15 points when comparing adapted and non-adapted detectors. |
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0162-8828 |
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ADAS; 600.057; 600.054; 601.217; 600.076 |
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no |
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ADAS @ adas @ XRV2014b |
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2436 |
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Author |
David Vazquez; Javier Marin; Antonio Lopez; Daniel Ponsa; David Geronimo |
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Title |
Virtual and Real World Adaptation for Pedestrian Detection |
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Journal Article |
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Year |
2014 |
Publication |
IEEE Transactions on Pattern Analysis and Machine Intelligence |
Abbreviated Journal |
TPAMI |
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Volume |
36 |
Issue |
4 |
Pages |
797-809 |
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Domain Adaptation; Pedestrian Detection |
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Pedestrian detection is of paramount interest for many applications. Most promising detectors rely on discriminatively learnt classifiers, i.e., trained with annotated samples. However, the annotation step is a human intensive and subjective task worth to be minimized. By using virtual worlds we can automatically obtain precise and rich annotations. Thus, we face the question: can a pedestrian appearance model learnt in realistic virtual worlds work successfully for pedestrian detection in realworld images?. Conducted experiments show that virtual-world based training can provide excellent testing accuracy in real world, but it can also suffer the dataset shift problem as real-world based training does. Accordingly, we have designed a domain adaptation framework, V-AYLA, in which we have tested different techniques to collect a few pedestrian samples from the target domain (real world) and combine them with the many examples of the source domain (virtual world) in order to train a domain adapted pedestrian classifier that will operate in the target domain. V-AYLA reports the same detection accuracy than when training with many human-provided pedestrian annotations and testing with real-world images of the same domain. To the best of our knowledge, this is the first work demonstrating adaptation of virtual and real worlds for developing an object detector. |
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0162-8828 |
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ADAS; 600.057; 600.054; 600.076 |
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ADAS @ adas @ VML2014 |
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2275 |
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Author |
Jaume Amores; N. Sebe; Petia Radeva |
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Title |
Context-Based Object-Class Recognition and Retrieval by Generalized Correlograms |
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2007 |
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IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29(10):1818–1833, (ISI 3,81) |
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ADAS;MILAB |
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no |
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ADAS @ adas @ ASR2007b |
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922 |
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Javier Marin; David Vazquez; Antonio Lopez; Jaume Amores; Ludmila I. Kuncheva |
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Title |
Occlusion handling via random subspace classifiers for human detection |
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2014 |
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IEEE Transactions on Systems, Man, and Cybernetics (Part B) |
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TSMCB |
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44 |
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3 |
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342-354 |
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Pedestriand Detection; occlusion handling |
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This paper describes a general method to address partial occlusions for human detection in still images. The Random Subspace Method (RSM) is chosen for building a classifier ensemble robust against partial occlusions. The component classifiers are chosen on the basis of their individual and combined performance. The main contribution of this work lies in our approach’s capability to improve the detection rate when partial occlusions are present without compromising the detection performance on non occluded data. In contrast to many recent approaches, we propose a method which does not require manual labelling of body parts, defining any semantic spatial components, or using additional data coming from motion or stereo. Moreover, the method can be easily extended to other object classes. The experiments are performed on three large datasets: the INRIA person dataset, the Daimler Multicue dataset, and a new challenging dataset, called PobleSec, in which a considerable number of targets are partially occluded. The different approaches are evaluated at the classification and detection levels for both partially occluded and non-occluded data. The experimental results show that our detector outperforms state-of-the-art approaches in the presence of partial occlusions, while offering performance and reliability similar to those of the holistic approach on non-occluded data. The datasets used in our experiments have been made publicly available for benchmarking purposes |
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2168-2267 |
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ADAS; 605.203; 600.057; 600.054; 601.042; 601.187; 600.076 |
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ADAS @ adas @ MVL2014 |
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2213 |
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Aura Hernandez-Sabate; Debora Gil; Jaume Garcia; Enric Marti |
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Image-based Cardiac Phase Retrieval in Intravascular Ultrasound Sequences |
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2011 |
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IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control |
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T-UFFC |
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58 |
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1 |
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60-72 |
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3-D exploring; ECG; band-pass filter; cardiac motion; cardiac phase retrieval; coronary arteries; electrocardiogram signal; image intensity local mean evolution; image-based cardiac phase retrieval; in vivo pullbacks acquisition; intravascular ultrasound sequences; longitudinal motion; signal extrema; time 36 ms; band-pass filters; biomedical ultrasonics; cardiovascular system; electrocardiography; image motion analysis; image retrieval; image sequences; medical image processing; ultrasonic imaging |
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Longitudinal motion during in vivo pullbacks acquisition of intravascular ultrasound (IVUS) sequences is a major artifact for 3-D exploring of coronary arteries. Most current techniques are based on the electrocardiogram (ECG) signal to obtain a gated pullback without longitudinal motion by using specific hardware or the ECG signal itself. We present an image-based approach for cardiac phase retrieval from coronary IVUS sequences without an ECG signal. A signal reflecting cardiac motion is computed by exploring the image intensity local mean evolution. The signal is filtered by a band-pass filter centered at the main cardiac frequency. Phase is retrieved by computing signal extrema. The average frame processing time using our setup is 36 ms. Comparison to manually sampled sequences encourages a deeper study comparing them to ECG signals. |
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0885-3010 |
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IAM;ADAS |
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IAM @ iam @ HGG2011 |
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1546 |
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Fadi Dornaika; Angel Sappa |
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A Featureless and Stochastic Approach to On-board Stereo Vision System Pose |
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2009 |
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Image and Vision Computing |
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IMAVIS |
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27 |
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9 |
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1382–1393 |
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On-board stereo vision system; Pose estimation; Featureless approach; Particle filtering; Image warping |
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This paper presents a direct and stochastic technique for real-time estimation of on-board stereo head’s position and orientation. Unlike existing works which rely on feature extraction either in the image domain or in 3D space, our proposed approach directly estimates the unknown parameters from the stream of stereo pairs’ brightness. The pose parameters are tracked using the particle filtering framework which implicitly enforces the smoothness constraints on the estimated parameters. The proposed technique can be used with a driver assistance applications as well as with augmented reality applications. Extended experiments on urban environments with different road geometries are presented. Comparisons with a 3D data-based approach are presented. Moreover, we provide a performance study aiming at evaluating the accuracy of the proposed approach. |
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ADAS @ adas @ DoS2009b |
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1152 |
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