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Author |
Jose Carlos Rubio; Joan Serrat; Antonio Lopez; N. Paragios |
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Title |
Image Contextual Representation and Matching through Hierarchies and Higher Order Graphs |
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Conference Article |
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Year |
2012 |
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21st International Conference on Pattern Recognition |
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2664 - 2667 |
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We present a region matching algorithm which establishes correspondences between regions from two segmented images. An abstract graph-based representation conceals the image in a hierarchical graph, exploiting the scene properties at two levels. First, the similarity and spatial consistency of the image semantic objects is encoded in a graph of commute times. Second, the cluttered regions of the semantic objects are represented with a shape descriptor. Many-to-many matching of regions is specially challenging due to the instability of the segmentation under slight image changes, and we explicitly handle it through high order potentials. We demonstrate the matching approach applied to images of world famous buildings, captured under different conditions, showing the robustness of our method to large variations in illumination and viewpoint. |
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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 @ RSL2012a; |
Serial |
2032 |
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Author |
Idoia Ruiz; Lorenzo Porzi; Samuel Rota Bulo; Peter Kontschieder; Joan Serrat |
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Title |
Weakly Supervised Multi-Object Tracking and Segmentation |
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Conference Article |
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Year |
2021 |
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IEEE Winter Conference on Applications of Computer Vision Workshops |
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125-133 |
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We introduce the problem of weakly supervised MultiObject Tracking and Segmentation, i.e. joint weakly supervised instance segmentation and multi-object tracking, in which we do not provide any kind of mask annotation.
To address it, we design a novel synergistic training strategy by taking advantage of multi-task learning, i.e. classification and tracking tasks guide the training of the unsupervised instance segmentation. For that purpose, we extract weak foreground localization information, provided by
Grad-CAM heatmaps, to generate a partial ground truth to learn from. Additionally, RGB image level information is employed to refine the mask prediction at the edges of the
objects. We evaluate our method on KITTI MOTS, the most representative benchmark for this task, reducing the performance gap on the MOTSP metric between the fully supervised and weakly supervised approach to just 12% and 12.7 % for cars and pedestrians, respectively. |
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Virtual; January 2021 |
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WACVW |
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ADAS; 600.118; 600.124 |
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Admin @ si @ RPR2021 |
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3548 |
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Author |
Mohammad Rouhani; Angel Sappa |
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Title |
Non-Rigid Shape Registration: A Single Linear Least Squares Framework |
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Conference Article |
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Year |
2012 |
Publication |
12th European Conference on Computer Vision |
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7578 |
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Pages |
264-277 |
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This paper proposes a non-rigid registration formulation capturing both global and local deformations in a single framework. This formulation is based on a quadratic estimation of the registration distance together with a quadratic regularization term. Hence, the optimal transformation parameters are easily obtained by solving a liner system of equations, which guarantee a fast convergence. Experimental results with challenging 2D and 3D shapes are presented to show the validity of the proposed framework. Furthermore, comparisons with the most relevant approaches are provided. |
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Florencia |
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Springer Berlin Heidelberg |
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LNCS |
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0302-9743 |
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978-3-642-33785-7 |
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ECCV |
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ADAS |
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no |
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Admin @ si @ RoS2012a |
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2158 |
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Author |
Mohammad Rouhani; Angel Sappa |
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Title |
Correspondence Free Registration through a Point-to-Model Distance Minimization |
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Conference Article |
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Year |
2011 |
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13th IEEE International Conference on Computer Vision |
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2150-2157 |
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This paper presents a novel formulation, which derives in a smooth minimization problem, to tackle the rigid registration between a given point set and a model set. Unlike most of the existing works, which are based on minimizing a point-wise correspondence term, we propose to describe the model set by means of an implicit representation. It allows a new definition of the registration error, which works beyond the point level representation. Moreover, it could be used in a gradient-based optimization framework. The proposed approach consists of two stages. Firstly, a novel formulation is proposed that relates the registration parameters with the distance between the model and data set. Secondly, the registration parameters are obtained by means of the Levengberg-Marquardt algorithm. Experimental results and comparisons with state of the art show the validity of the proposed framework. |
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Barcelona |
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1550-5499 |
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978-1-4577-1101-5 |
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ICCV |
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ADAS |
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no |
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Admin @ si @ RoS2011b; ADAS @ adas @ |
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1832 |
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Author |
Mohammad Rouhani; Angel Sappa |
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Title |
Implicit B-Spline Fitting Using the 3L Algorithm |
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Conference Article |
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Year |
2011 |
Publication |
18th IEEE International Conference on Image Processing |
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893-896 |
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Brussels, Belgium |
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ICIP |
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ADAS |
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Admin @ si @ RoS2011a; ADAS @ adas @ |
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1782 |
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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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Conference Article |
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Year |
2012 |
Publication |
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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Call Number |
Admin @ si @ RMG2012 |
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2031 |
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Author |
Youssef El Rhabi; Simon Loic; Brun Luc; Josep Llados; Felipe Lumbreras |
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Title |
Information Theoretic Rotationwise Robust Binary Descriptor Learning |
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Conference Article |
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Year |
2016 |
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Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR) |
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368-378 |
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In this paper, we propose a new data-driven approach for binary descriptor selection. In order to draw a clear analysis of common designs, we present a general information-theoretic selection paradigm. It encompasses several standard binary descriptor construction schemes, including a recent state-of-the-art one named BOLD. We pursue the same endeavor to increase the stability of the produced descriptors with respect to rotations. To achieve this goal, we have designed a novel offline selection criterion which is better adapted to the online matching procedure. The effectiveness of our approach is demonstrated on two standard datasets, where our descriptor is compared to BOLD and to several classical descriptors. In particular, it emerges that our approach can reproduce equivalent if not better performance as BOLD while relying on twice shorter descriptors. Such an improvement can be influential for real-time applications. |
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Mérida; Mexico; November 2016 |
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S+SSPR |
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DAG; ADAS; 600.097; 600.086 |
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Admin @ si @ RLL2016 |
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2871 |
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Author |
Arnau Ramisa; Ramon Lopez de Mantaras; Ricardo Toledo |
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Title |
Comparing Combinations of Feature Regions for Panoramic VSLAM |
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Conference Article |
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2007 |
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4th International Conference on Informatics in Control, Automation and Robotics |
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292–297 |
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Angers (France) |
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ICINCO |
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RV;ADAS |
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Admin @ si @ RLA2007 |
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900 |
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Muhammad Anwer Rao; Fahad Shahbaz Khan; Joost Van de Weijer; Jorma Laaksonen |
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Title |
Combining Holistic and Part-based Deep Representations for Computational Painting Categorization |
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Conference Article |
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2016 |
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6th International Conference on Multimedia Retrieval |
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Automatic analysis of visual art, such as paintings, is a challenging inter-disciplinary research problem. Conventional approaches only rely on global scene characteristics by encoding holistic information for computational painting categorization.We argue that such approaches are sub-optimal and that discriminative common visual structures provide complementary information for painting classification. We present an approach that encodes both the global scene layout and discriminative latent common structures for computational painting categorization. The region of interests are automatically extracted, without any manual part labeling, by training class-specific deformable part-based models. Both holistic and region-of-interests are then described using multi-scale dense convolutional features. These features are pooled separately using Fisher vector encoding and concatenated afterwards in a single image representation. Experiments are performed on a challenging dataset with 91 different painters and 13 diverse painting styles. Our approach outperforms the standard method, which only employs the global scene characteristics. Furthermore, our method achieves state-of-the-art results outperforming a recent multi-scale deep features based approach [11] by 6.4% and 3.8% respectively on artist and style classification. |
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New York; USA; June 2016 |
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ICMR |
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LAMP; 600.068; 600.079;ADAS |
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Admin @ si @ RKW2016 |
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2763 |
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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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Admin @ si @ RGS2013b; ADAS @ adas @ |
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2274 |
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