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Naveen Onkarappa, Cristhian A. Aguilera-Carrasco, Boris X. Vintimilla and Angel Sappa. 2014. Cross-spectral Stereo Correspondence using Dense Flow Fields. 9th International Conference on Computer Vision Theory and Applications.613–617.
Abstract: 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.
Keywords: Cross-spectral Stereo Correspondence; Dense Optical Flow; Infrared and Visible Spectrum
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Ariel Amato, Felipe Lumbreras and Angel Sappa. 2014. A General-purpose Crowdsourcing Platform for Mobile Devices. 9th International Conference on Computer Vision Theory and Applications.211–215.
Abstract: 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.
Keywords: Crowdsourcing Platform; Mobile Crowdsourcing
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Patricia Marquez and 6 others. 2014. Factors Affecting Optical Flow Performance in Tagging Magnetic Resonance Imaging. 17th International Conference on Medical Image Computing and Computer Assisted Intervention. Springer International Publishing, 231–238. (LNCS.)
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 dierent 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 dierent OF methods, including HARP.
Keywords: Optical flow; Performance Evaluation; Synthetic Database; ANOVA; Tagging Magnetic Resonance Imaging
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German Ros, Sebastian Ramos, Manuel Granados, Amir Bakhtiary, David Vazquez and Antonio Lopez. 2015. Vision-based Offline-Online Perception Paradigm for Autonomous Driving. IEEE Winter Conference on Applications of Computer Vision.231–238.
Abstract: 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.
Keywords: Autonomous Driving; Scene Understanding; SLAM; Semantic Segmentation
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Mohammad Rouhani, E. Boyer and Angel Sappa. 2014. Non-Rigid Registration meets Surface Reconstruction. International Conference on 3D Vision.617–624.
Abstract: 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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Marcelo D. Pistarelli, Angel Sappa and Ricardo Toledo. 2013. Multispectral Stereo Image Correspondence. 15th International Conference on Computer Analysis of Images and Patterns. Springer Berlin Heidelberg, 217–224. (LNCS.)
Abstract: 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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Gioacchino Vino and Angel Sappa. 2013. Revisiting Harris Corner Detector Algorithm: a Gradual Thresholding Approach. 10th International Conference on Image Analysis and Recognition. Springer Berlin Heidelberg, 354–363. (LNCS.)
Abstract: This paper presents an adaptive thresholding approach intended to increase the number of detected corners, while reducing the amount of those ones corresponding to noisy data. The proposed approach works by using the classical Harris corner detector algorithm and overcome the difficulty in finding a general threshold that work well for all the images in a given data set by proposing a novel adaptive thresholding scheme. Initially, two thresholds are used to discern between strong corners and flat regions. Then, a region based criteria is used to discriminate between weak corners and noisy points in the midway interval. Experimental results show that the proposed approach has a better capability to reject false corners and, at the same time, to detect weak ones. Comparisons with the state of the art are provided showing the validity of the proposed approach.
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Alejandro Gonzalez Alzate, Gabriel Villalonga, Jiaolong Xu, David Vazquez, Jaume Amores and Antonio Lopez. 2015. Multiview Random Forest of Local Experts Combining RGB and LIDAR data for Pedestrian Detection. IEEE Intelligent Vehicles Symposium IV2015.356–361.
Abstract: Despite recent significant advances, pedestrian detection continues to be an extremely challenging problem in real scenarios. In order to develop a detector that successfully operates under these conditions, it becomes critical to leverage upon multiple cues, multiple imaging modalities and a strong multi-view classifier that accounts for different pedestrian views and poses. In this paper we provide an extensive evaluation that gives insight into how each of these aspects (multi-cue, multimodality and strong multi-view classifier) affect performance both individually and when integrated together. In the multimodality component we explore the fusion of RGB and depth maps obtained by high-definition LIDAR, a type of modality that is only recently starting to receive attention. As our analysis reveals, although all the aforementioned aspects significantly help in improving the performance, the fusion of visible spectrum and depth information allows to boost the accuracy by a much larger margin. The resulting detector not only ranks among the top best performers in the challenging KITTI benchmark, but it is built upon very simple blocks that are easy to implement and computationally efficient. These simple blocks can be easily replaced with more sophisticated ones recently proposed, such as the use of convolutional neural networks for feature representation, to further improve the accuracy.
Keywords: Pedestrian Detection
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Jiaolong Xu, Sebastian Ramos, David Vazquez and Antonio Lopez. 2013. DA-DPM Pedestrian Detection. ICCV Workshop on Reconstruction meets Recognition.
Keywords: Domain Adaptation; Pedestrian Detection
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Alejandro Gonzalez Alzate, Gabriel Villalonga, German Ros, David Vazquez and Antonio Lopez. 2015. 3D-Guided Multiscale Sliding Window for Pedestrian Detection. Pattern Recognition and Image Analysis, Proceedings of 7th Iberian Conference , ibPRIA 2015.560–568.
Abstract: The most relevant modules of a pedestrian detector are the candidate generation and the candidate classification. The former aims at presenting image windows to the latter so that they are classified as containing a pedestrian or not. Much attention has being paid to the classification module, while candidate generation has mainly relied on (multiscale) sliding window pyramid. However, candidate generation is critical for achieving real-time. In this paper we assume a context of autonomous driving based on stereo vision. Accordingly, we evaluate the effect of taking into account the 3D information (derived from the stereo) in order to prune the hundred of thousands windows per image generated by classical pyramidal sliding window. For our study we use a multimodal (RGB, disparity) and multi-descriptor (HOG, LBP, HOG+LBP) holistic ensemble based on linear SVM. Evaluation on data from the challenging KITTI benchmark suite shows the effectiveness of using 3D information to dramatically reduce the number of candidate windows, even improving the overall pedestrian detection accuracy.
Keywords: Pedestrian Detection
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