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Author  |
G. Roig; Xavier Boix; F. de la Torre; Joan Serrat; C. Vilella |
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Title |
Hierarchical CRF with product label spaces for parts-based Models |
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Conference Article |
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2011 |
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IEEE Conference on Automatic Face and Gesture Recognition |
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Non-rigid object detection is a challenging an open research problem in computer vision. It is a critical part in many applications such as image search, surveillance, human-computer interaction or image auto-annotation. Most successful approaches to non-rigid object detection make use of part-based models. In particular, Conditional Random Fields (CRF) have been successfully embedded into a discriminative parts-based model framework due to its effectiveness for learning and inference (usually based on a tree structure). However, CRF-based approaches do not incorporate global constraints and only model pairwise interactions. This is especially important when modeling object classes that may have complex parts interactions (e.g. facial features or body articulations), because neglecting them yields an oversimplified model with suboptimal performance. To overcome this limitation, this paper proposes a novel hierarchical CRF (HCRF). The main contribution is to build a hierarchy of part combinations by extending the label set to a hierarchy of product label spaces. In order to keep the inference computation tractable, we propose an effective method to reduce the new label set. We test our method on two applications: facial feature detection on the Multi-PIE database and human pose estimation on the Buffy dataset. |
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no |
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Admin @ si @ RBT2011 |
Serial |
1862 |
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Author |
G.D. Evangelidis; Ferran Diego; Joan Serrat; Antonio Lopez |
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Title |
Slice Matching for Accurate Spatio-Temporal Alignment |
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2011 |
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In ICCV Workshop on Visual Surveillance |
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video alignment |
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Video synchronization and alignment is a rather recent topic in computer vision. It usually deals with the problem of aligning sequences recorded simultaneously by static, jointly- or independently-moving cameras. In this paper, we investigate the more difficult problem of matching videos captured at different times from independently-moving cameras, whose trajectories are approximately coincident or parallel. To this end, we propose a novel method that pixel-wise aligns videos and allows thus to automatically highlight their differences. This primarily aims at visual surveillance but the method can be adopted as is by other related video applications, like object transfer (augmented reality) or high dynamic range video. We build upon a slice matching scheme to first synchronize the sequences, while we develop a spatio-temporal alignment scheme to spatially register corresponding frames and refine the temporal mapping. We investigate the performance of the proposed method on videos recorded from vehicles driven along different types of roads and compare with related previous works. |
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ADAS |
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Admin @ si @ EDS2011; ADAS @ adas @ eds2011a |
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1861 |
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Author |
Gema Rotger; Felipe Lumbreras; Francesc Moreno-Noguer; Antonio Agudo |
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Title |
2D-to-3D Facial Expression Transfer |
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Conference Article |
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2018 |
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24th International Conference on Pattern Recognition |
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2008 - 2013 |
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Automatically changing the expression and physical features of a face from an input image is a topic that has been traditionally tackled in a 2D domain. In this paper, we bring this problem to 3D and propose a framework that given an
input RGB video of a human face under a neutral expression, initially computes his/her 3D shape and then performs a transfer to a new and potentially non-observed expression. For this purpose, we parameterize the rest shape –obtained from standard factorization approaches over the input video– using a triangular
mesh which is further clustered into larger macro-segments. The expression transfer problem is then posed as a direct mapping between this shape and a source shape, such as the blend shapes of an off-the-shelf 3D dataset of human facial expressions. The mapping is resolved to be geometrically consistent between 3D models by requiring points in specific regions to map on semantic
equivalent regions. We validate the approach on several synthetic and real examples of input faces that largely differ from the source shapes, yielding very realistic expression transfers even in cases with topology changes, such as a synthetic video sequence of a single-eyed cyclops. |
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ICPR |
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ADAS; 600.086; 600.130; 600.118 |
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no |
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Call Number |
Admin @ si @ RLM2018 |
Serial |
3232 |
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Author |
Gemma Roig; Xavier Boix; R. de Nijs; Sebastian Ramos; K. Kühnlenz; Luc Van Gool |
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Title |
Active MAP Inference in CRFs for Efficient Semantic Segmentation |
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Conference Article |
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Year |
2013 |
Publication |
15th IEEE International Conference on Computer Vision |
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2312 - 2319 |
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Semantic Segmentation |
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Most MAP inference algorithms for CRFs optimize an energy function knowing all the potentials. In this paper, we focus on CRFs where the computational cost of instantiating the potentials is orders of magnitude higher than MAP inference. This is often the case in semantic image segmentation, where most potentials are instantiated by slow classifiers fed with costly features. We introduce Active MAP inference 1) to on-the-fly select a subset of potentials to be instantiated in the energy function, leaving the rest of the parameters of the potentials unknown, and 2) to estimate the MAP labeling from such incomplete energy function. Results for semantic segmentation benchmarks, namely PASCAL VOC 2010 [5] and MSRC-21 [19], show that Active MAP inference achieves similar levels of accuracy but with major efficiency gains. |
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Sydney; Australia; December 2013 |
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1550-5499 |
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ICCV |
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Notes |
ADAS; 600.057 |
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no |
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Call Number |
ADAS @ adas @ RBN2013 |
Serial |
2377 |
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Author |
Gemma Rotger; Francesc Moreno-Noguer; Felipe Lumbreras; Antonio Agudo |
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Title |
Single view facial hair 3D reconstruction |
Type |
Conference Article |
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Year |
2019 |
Publication |
9th Iberian Conference on Pattern Recognition and Image Analysis |
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Volume |
11867 |
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423-436 |
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Keywords |
3D Vision; Shape Reconstruction; Facial Hair Modeling |
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n this work, we introduce a novel energy-based framework that addresses the challenging problem of 3D reconstruction of facial hair from a single RGB image. To this end, we identify hair pixels over the image via texture analysis and then determine individual hair fibers that are modeled by means of a parametric hair model based on 3D helixes. We propose to minimize an energy composed of several terms, in order to adapt the hair parameters that better fit the image detections. The final hairs respond to the resulting fibers after a post-processing step where we encourage further realism. The resulting approach generates realistic facial hair fibers from solely an RGB image without assuming any training data nor user interaction. We provide an experimental evaluation on real-world pictures where several facial hair styles and image conditions are observed, showing consistent results and establishing a comparison with respect to competing approaches. |
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Madrid; July 2019 |
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LNCS |
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IbPRIA |
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Notes |
ADAS; 600.086; 600.130; 600.122 |
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no |
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Call Number |
Admin @ si @ |
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3707 |
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Author |
German Ros; Angel Sappa; Daniel Ponsa; Antonio Lopez |
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Title |
Visual SLAM for Driverless Cars: A Brief Survey |
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Conference Article |
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2012 |
Publication |
IEEE Workshop on Navigation, Perception, Accurate Positioning and Mapping for Intelligent Vehicles |
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SLAM |
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Alcalá de Henares |
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IVW |
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ADAS |
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no |
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Admin @ si @ RSP2012; ADAS @ adas |
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2019 |
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Author |
German Ros; J. Guerrero; Angel Sappa; Antonio Lopez |
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Title |
VSLAM pose initialization via Lie groups and Lie algebras optimization |
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Conference Article |
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2013 |
Publication |
Proceedings of IEEE International Conference on Robotics and Automation |
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5740 - 5747 |
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SLAM |
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We present a novel technique for estimating initial 3D poses in the context of localization and Visual SLAM problems. The presented approach can deal with noise, outliers and a large amount of input data and still performs in real time in a standard CPU. Our method produces solutions with an accuracy comparable to those produced by RANSAC but can be much faster when the percentage of outliers is high or for large amounts of input data. On the current work we propose to formulate the pose estimation as an optimization problem on Lie groups, considering their manifold structure as well as their associated Lie algebras. This allows us to perform a fast and simple optimization at the same time that conserve all the constraints imposed by the Lie group SE(3). Additionally, we present several key design concepts related with the cost function and its Jacobian; aspects that are critical for the good performance of the algorithm. |
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Karlsruhe; Germany; May 2013 |
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1050-4729 |
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978-1-4673-5641-1 |
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ICRA |
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ADAS; 600.054; 600.055; 600.057 |
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Admin @ si @ RGS2013a; ADAS @ adas @ |
Serial |
2225 |
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Author |
German Ros; J. Guerrero; Angel Sappa; Daniel Ponsa; Antonio Lopez |
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Title |
Fast and Robust l1-averaging-based Pose Estimation for Driving Scenarios |
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Conference Article |
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2013 |
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24th British Machine Vision Conference |
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SLAM |
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Robust visual pose estimation is at the core of many computer vision applications, being fundamental for Visual SLAM and Visual Odometry problems. During the last decades, many approaches have been proposed to solve these problems, being RANSAC one of the most accepted and used. However, with the arrival of new challenges, such as large driving scenarios for autonomous vehicles, along with the improvements in the data gathering frameworks, new issues must be considered. One of these issues is the capability of a technique to deal with very large amounts of data while meeting the realtime
constraint. With this purpose in mind, we present a novel technique for the problem of robust camera-pose estimation that is more suitable for dealing with large amount of data, which additionally, helps improving the results. The method is based on a combination of a very fast coarse-evaluation function and a robust ℓ1-averaging procedure. Such scheme leads to high-quality results while taking considerably less time than RANSAC.
Experimental results on the challenging KITTI Vision Benchmark Suite are provided, showing the validity of the proposed approach. |
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Bristol; UK; September 2013 |
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BMVC |
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ADAS |
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no |
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Admin @ si @ RGS2013b; ADAS @ adas @ |
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2274 |
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Author |
German Ros; Jesus Martinez del Rincon; Gines Garcia-Mateos |
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Title |
Articulated Particle Filter for Hand Tracking |
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2012 |
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21st International Conference on Pattern Recognition |
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3581 - 3585 |
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This paper proposes a new version of Particle Filter, called Articulated Particle Filter – ArPF -, which has been specifically designed for an efficient sampling of hierarchical spaces, generated by articulated objects. Our approach decomposes the articulated motion into layers for efficiency purposes, making use of a careful modeling of the diffusion noise along with its propagation through the articulations. This produces an increase of accuracy and prevent for divergences. The algorithm is tested on hand tracking due to its complex hierarchical articulated nature. With this purpose, a new dataset generation tool for quantitative evaluation is also presented in this paper. |
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Tsukuba Science City, Japan |
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1051-4651 |
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978-1-4673-2216-4 |
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ICPR |
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ADAS |
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no |
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Admin @ si @ RMG2012 |
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2031 |
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Author |
German Ros; Laura Sellart; Joanna Materzynska; David Vazquez; Antonio Lopez |
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Title |
The SYNTHIA Dataset: A Large Collection of Synthetic Images for Semantic Segmentation of Urban Scenes |
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Conference Article |
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2016 |
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29th IEEE Conference on Computer Vision and Pattern Recognition |
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3234-3243 |
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Domain Adaptation; Autonomous Driving; Virtual Data; Semantic Segmentation |
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Vision-based semantic segmentation in urban scenarios is a key functionality for autonomous driving. The irruption of deep convolutional neural networks (DCNNs) allows to foresee obtaining reliable classifiers to perform such a visual task. However, DCNNs require to learn many parameters from raw images; thus, having a sufficient amount of diversified images with this class annotations is needed. These annotations are obtained by a human cumbersome labour specially challenging for semantic segmentation, since pixel-level annotations are required. In this paper, we propose to use a virtual world for automatically generating realistic synthetic images with pixel-level annotations. Then, we address the question of how useful can be such data for the task of semantic segmentation; in particular, when using a DCNN paradigm. In order to answer this question we have generated a synthetic diversified collection of urban images, named SynthCity, with automatically generated class annotations. We use SynthCity in combination with publicly available real-world urban images with manually provided annotations. Then, we conduct experiments on a DCNN setting that show how the inclusion of SynthCity in the training stage significantly improves the performance of the semantic segmentation task |
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Las Vegas; USA; June 2016 |
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CVPR |
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ADAS; 600.085; 600.082; 600.076 |
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ADAS @ adas @ RSM2016 |
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2739 |
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