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Author |
Katerine Diaz; Francesc J. Ferri; W. Diaz |
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Title |
Fast Approximated Discriminative Common Vectors using rank-one SVD updates |
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Conference Article |
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Year |
2013 |
Publication |
20th International Conference On Neural Information Processing |
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Volume |
8228 |
Issue |
III |
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368-375 |
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Abstract |
An efficient incremental approach to the discriminative common vector (DCV) method for dimensionality reduction and classification is presented. The proposal consists of a rank-one update along with an adaptive restriction on the rank of the null space which leads to an approximate but convenient solution. The algorithm can be implemented very efficiently in terms of matrix operations and space complexity, which enables its use in large-scale dynamic application domains. Deep comparative experimentation using publicly available high dimensional image datasets has been carried out in order to properly assess the proposed algorithm against several recent incremental formulations.
K. Diaz-Chito, F.J. Ferri, W. Diaz |
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Daegu; Korea; November 2013 |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-42050-4 |
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ICONIP |
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ADAS |
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Admin @ si @ DFD2013 |
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2439 |
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Author |
Alejandro Gonzalez Alzate; Sebastian Ramos; David Vazquez; Antonio Lopez; Jaume Amores |
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Title |
Spatiotemporal Stacked Sequential Learning for Pedestrian Detection |
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Conference Article |
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Year |
2015 |
Publication |
Pattern Recognition and Image Analysis, Proceedings of 7th Iberian Conference , ibPRIA 2015 |
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3-12 |
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Keywords |
SSL; Pedestrian Detection |
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Pedestrian classifiers decide which image windows contain a pedestrian. In practice, such classifiers provide a relatively high response at neighbor windows overlapping a pedestrian, while the responses around potential false positives are expected to be lower. An analogous reasoning applies for image sequences. If there is a pedestrian located within a frame, the same pedestrian is expected to appear close to the same location in neighbor frames. Therefore, such a location has chances of receiving high classification scores during several frames, while false positives are expected to be more spurious. In this paper we propose to exploit such correlations for improving the accuracy of base pedestrian classifiers. In particular, we propose to use two-stage classifiers which not only rely on the image descriptors required by the base classifiers but also on the response of such base classifiers in a given spatiotemporal neighborhood. More specifically, we train pedestrian classifiers using a stacked sequential learning (SSL) paradigm. We use a new pedestrian dataset we have acquired from a car to evaluate our proposal at different frame rates. We also test on a well known dataset: Caltech. The obtained results show that our SSL proposal boosts detection accuracy significantly with a minimal impact on the computational cost. Interestingly, SSL improves more the accuracy at the most dangerous situations, i.e. when a pedestrian is close to the camera. |
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Santiago de Compostela; España; June 2015 |
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IbPRIA |
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ADAS; 600.057; 600.054; 600.076 |
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GRV2015; ADAS @ adas @ GRV2015 |
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2454 |
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Author |
Jiaolong Xu; Sebastian Ramos; David Vazquez; Antonio Lopez |
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Title |
Incremental Domain Adaptation of Deformable Part-based Models |
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Conference Article |
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Year |
2014 |
Publication |
25th British Machine Vision Conference |
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Keywords |
Pedestrian Detection; Part-based models; Domain Adaptation |
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Nowadays, classifiers play a core role in many computer vision tasks. The underlying assumption for learning classifiers is that the training set and the deployment environment (testing) follow the same probability distribution regarding the features used by the classifiers. However, in practice, there are different reasons that can break this constancy assumption. Accordingly, reusing existing classifiers by adapting them from the previous training environment (source domain) to the new testing one (target domain)
is an approach with increasing acceptance in the computer vision community. In this paper we focus on the domain adaptation of deformable part-based models (DPMs) for object detection. In particular, we focus on a relatively unexplored scenario, i.e. incremental domain adaptation for object detection assuming weak-labeling. Therefore, our algorithm is ready to improve existing source-oriented DPM-based detectors as soon as a little amount of labeled target-domain training data is available, and keeps improving as more of such data arrives in a continuous fashion. For achieving this, we follow a multiple
instance learning (MIL) paradigm that operates in an incremental per-image basis. As proof of concept, we address the challenging scenario of adapting a DPM-based pedestrian detector trained with synthetic pedestrians to operate in real-world scenarios. The obtained results show that our incremental adaptive models obtain equally good accuracy results as the batch learned models, while being more flexible for handling continuously arriving target-domain data. |
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Nottingham; uk; September 2014 |
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BMVA Press |
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Valstar, Michel and French, Andrew and Pridmore, Tony |
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BMVC |
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ADAS; 600.057; 600.054; 600.076 |
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XRV2014c; ADAS @ adas @ xrv2014c |
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2455 |
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Author |
P. Ricaurte; C. Chilan; Cristhian A. Aguilera-Carrasco; Boris X. Vintimilla; Angel Sappa |
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Title |
Performance Evaluation of Feature Point Descriptors in the Infrared Domain |
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Conference Article |
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Year |
2014 |
Publication |
9th International Conference on Computer Vision Theory and Applications |
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1 |
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545-550 |
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Keywords |
Infrared Imaging; Feature Point Descriptors |
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This paper presents a comparative evaluation of classical feature point descriptors when they are used in the long-wave infrared spectral band. Robustness to changes in rotation, scaling, blur, and additive noise are evaluated using a state of the art framework. Statistical results using an outdoor image data set are presented together with a discussion about the differences with respect to the results obtained when images from the visible spectrum are considered. |
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Lisboa; Portugal; January 2014 |
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VISAPP |
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Notes |
ADAS; 600.055; 600.076 |
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Call Number |
Admin @ si @ RCA2014b |
Serial |
2476 |
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Author |
Naveen Onkarappa; Cristhian A. Aguilera-Carrasco; Boris X. Vintimilla; Angel Sappa |
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Title |
Cross-spectral Stereo Correspondence using Dense Flow Fields |
Type |
Conference Article |
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Year |
2014 |
Publication |
9th International Conference on Computer Vision Theory and Applications |
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3 |
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Pages |
613-617 |
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Keywords |
Cross-spectral Stereo Correspondence; Dense Optical Flow; Infrared and Visible Spectrum |
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This manuscript addresses the cross-spectral stereo correspondence problem. It proposes the usage of a dense flow field based representation instead of the original cross-spectral images, which have a low correlation. In this way, working in the flow field space, classical cost functions can be used as similarity measures. Preliminary experimental results on urban environments have been obtained showing the validity of the proposed approach. |
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Lisboa; Portugal; January 2014 |
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VISAPP |
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ADAS; 600.055; 600.076 |
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Admin @ si @ OAV2014 |
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2477 |
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Author |
Ariel Amato; Felipe Lumbreras; Angel Sappa |
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Title |
A General-purpose Crowdsourcing Platform for Mobile Devices |
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Conference Article |
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Year |
2014 |
Publication |
9th International Conference on Computer Vision Theory and Applications |
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3 |
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211-215 |
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Keywords |
Crowdsourcing Platform; Mobile Crowdsourcing |
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This paper presents details of a general purpose micro-task on-demand platform based on the crowdsourcing philosophy. This platform was specifically developed for mobile devices in order to exploit the strengths of such devices; namely: i) massivity, ii) ubiquity and iii) embedded sensors. The combined use of mobile platforms and the crowdsourcing model allows to tackle from the simplest to the most complex tasks. Users experience is the highlighted feature of this platform (this fact is extended to both task-proposer and tasksolver). Proper tools according with a specific task are provided to a task-solver in order to perform his/her job in a simpler, faster and appealing way. Moreover, a task can be easily submitted by just selecting predefined templates, which cover a wide range of possible applications. Examples of its usage in computer vision and computer games are provided illustrating the potentiality of the platform. |
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Lisboa; Portugal; January 2014 |
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VISAPP |
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ISE; ADAS; 600.054; 600.055; 600.076; 600.078 |
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Admin @ si @ ALS2014 |
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2478 |
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Author |
Patricia Marquez; H. Kause; A. Fuster; Aura Hernandez-Sabate; L. Florack; Debora Gil; Hans van Assen |
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Title |
Factors Affecting Optical Flow Performance in Tagging Magnetic Resonance Imaging |
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Conference Article |
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Year |
2014 |
Publication |
17th International Conference on Medical Image Computing and Computer Assisted Intervention |
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Volume |
8896 |
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231-238 |
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Optical flow; Performance Evaluation; Synthetic Database; ANOVA; Tagging Magnetic Resonance Imaging |
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Abstract |
Changes in cardiac deformation patterns are correlated with cardiac pathologies. Deformation can be extracted from tagging Magnetic Resonance Imaging (tMRI) using Optical Flow (OF) techniques. For applications of OF in a clinical setting it is important to assess to what extent the performance of a particular OF method is stable across di erent clinical acquisition artifacts. This paper presents a statistical validation framework, based on ANOVA, to assess the motion and appearance factors that have the largest in uence on OF accuracy drop.
In order to validate this framework, we created a database of simulated tMRI data including the most common artifacts of MRI and test three di erent OF methods, including HARP. |
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Boston; USA; September 2014 |
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Springer International Publishing |
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0302-9743 |
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978-3-319-14677-5 |
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STACOM |
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IAM; ADAS; 600.060; 601.145; 600.076; 600.075 |
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Admin @ si @ MKF2014 |
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2495 |
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Author |
German Ros; Sebastian Ramos; Manuel Granados; Amir Bakhtiary; David Vazquez; Antonio Lopez |
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Title |
Vision-based Offline-Online Perception Paradigm for Autonomous Driving |
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2015 |
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IEEE Winter Conference on Applications of Computer Vision |
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231 - 238 |
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Autonomous Driving; Scene Understanding; SLAM; Semantic Segmentation |
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Autonomous driving is a key factor for future mobility. Properly perceiving the environment of the vehicles is essential for a safe driving, which requires computing accurate geometric and semantic information in real-time. In this paper, we challenge state-of-the-art computer vision algorithms for building a perception system for autonomous driving. An inherent drawback in the computation of visual semantics is the trade-off between accuracy and computational cost. We propose to circumvent this problem by following an offline-online strategy. During the offline stage dense 3D semantic maps are created. In the online stage the current driving area is recognized in the maps via a re-localization process, which allows to retrieve the pre-computed accurate semantics and 3D geometry in realtime. Then, detecting the dynamic obstacles we obtain a rich understanding of the current scene. We evaluate quantitatively our proposal in the KITTI dataset and discuss the related open challenges for the computer vision community. |
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Hawaii; January 2015 |
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WACV |
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ADAS; 600.076 |
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ADAS @ adas @ RRG2015 |
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2499 |
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Author |
Mohammad Rouhani; E. Boyer; Angel Sappa |
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Title |
Non-Rigid Registration meets Surface Reconstruction |
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Conference Article |
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2014 |
Publication |
International Conference on 3D Vision |
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617-624 |
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Non rigid registration is an important task in computer vision with many applications in shape and motion modeling. A fundamental step of the registration is the data association between the source and the target sets. Such association proves difficult in practice, due to the discrete nature of the information and its corruption by various types of noise, e.g. outliers and missing data. In this paper we investigate the benefit of the implicit representations for the non-rigid registration of 3D point clouds. First, the target points are described with small quadratic patches that are blended through partition of unity weighting. Then, the discrete association between the source and the target can be replaced by a continuous distance field induced by the interface. By combining this distance field with a proper deformation term, the registration energy can be expressed in a linear least square form that is easy and fast to solve. This significantly eases the registration by avoiding direct association between points. Moreover, a hierarchical approach can be easily implemented by employing coarse-to-fine representations. Experimental results are provided for point clouds from multi-view data sets. The qualitative and quantitative comparisons show the outperformance and robustness of our framework. %in presence of noise and outliers. |
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Tokyo; Japan; December 2014 |
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3DV |
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ADAS; 600.055; 600.076 |
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Admin @ si @ RBS2014 |
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2534 |
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Author |
Marcelo D. Pistarelli; Angel Sappa; Ricardo Toledo |
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Title |
Multispectral Stereo Image Correspondence |
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Conference Article |
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2013 |
Publication |
15th International Conference on Computer Analysis of Images and Patterns |
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8048 |
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217-224 |
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This paper presents a novel multispectral stereo image correspondence approach. It is evaluated using a stereo rig constructed with a visible spectrum camera and a long wave infrared spectrum camera. The novelty of the proposed approach lies on the usage of Hough space as a correspondence search domain. In this way it avoids searching for correspondence in the original multispectral image domains, where information is low correlated, and a common domain is used. The proposed approach is intended to be used in outdoor urban scenarios, where images contain large amount of edges. These edges are used as distinctive characteristics for the matching in the Hough space. Experimental results are provided showing the validity of the proposed approach. |
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York; uk; August 2013 |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-40245-6 |
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CAIP |
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ADAS; 600.055 |
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Admin @ si @ PST2013 |
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2561 |
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