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Angel Sappa and M.A. Garcia. 2004. Hierarchical Clustering of 3D Objects and its Application to Minimum Distance Computation. IEEE International Conference on Robotics & Automation, 5287–5292, New Orleans, LA (USA), ISBN: 0–7803–8232–3.
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Angel Sappa and Boris X. Vintimilla. 2006. Edge Point Linking by Means of Global and Local Schemes. IEEE Int. Conf. on Signal-Image Technology and Internet-Based Systems, Hammamet, Tunisia, December 2006, pp. 551-560..
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German Ros, J. Guerrero, Angel Sappa, Daniel Ponsa and Antonio Lopez. 2013. Fast and Robust l1-averaging-based Pose Estimation for Driving Scenarios. 24th British Machine Vision Conference.
Abstract: 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.
Keywords: SLAM
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David Geronimo, Angel Sappa, Antonio Lopez and Daniel Ponsa. 2007. Adaptive Image Sampling and Windows Classification for On-board Pedestrian Detection. Proceedings of the 5th International Conference on Computer Vision Systems.
Abstract: On–board pedestrian detection is in the frontier of the state–of–the–art since it implies processing outdoor scenarios from a mobile platform and searching for aspect–changing objects in cluttered urban environments. Most promising approaches include the development of classifiers based on feature selection and machine learning. However, they use a large number of features which compromises real–time. Thus, methods for running the classifiers in only a few image windows must be provided. In this paper we contribute in both aspects, proposing a camera
pose estimation method for adaptive sparse image sampling, as well as a classifier for pedestrian detection based on Haar wavelets and edge orientation histograms as features and AdaBoost as learning machine. Both proposals are compared with relevant approaches in the literature, showing comparable results but reducing processing time by four for the sampling tasks and by ten for the classification one.
Keywords: Pedestrian Detection
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Jaume Amores, N. Sebe and Petia Radeva. 2007. Class-Specific Binaryy Correlograms for Object Recognition. British Machine Vision Conference.
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Daniel Ponsa and Antonio Lopez. 2007. Cascade of Classifiers for Vehicle Detection. Advanced Concepts for Intelligent Vision Systems, LNCS 4678, volume 1, pp. 980–989.
Keywords: vehicle detection
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Jose Manuel Alvarez, Antonio Lopez and Ramon Baldrich. 2007. Shadow Resistant Road Segmentation from a Mobile Monocular System. 3rd Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA 2007), J. Marti et al. (Eds.) LNCS 4477:9–16.
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Xavier Boix and 7 others. 2009. Combining local and global bag-of-word representations for semantic segmentation. Workshop on The PASCAL Visual Object Classes Challenge.
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Jaume Amores, David Geronimo and Antonio Lopez. 2010. Multiple instance and active learning for weakly-supervised object-class segmentation. 3rd IEEE International Conference on Machine Vision.
Abstract: In object-class segmentation, one of the most tedious tasks is to manually segment many object examples in order to learn a model of the object category. Yet, there has been little research on reducing the degree of manual annotation for
object-class segmentation. In this work we explore alternative strategies which do not require full manual segmentation of the object in the training set. In particular, we study the use of bounding boxes as a coarser and much cheaper form of segmentation and we perform a comparative study of several Multiple-Instance Learning techniques that allow to obtain a model with this type of weak annotation. We show that some of these methods can be competitive, when used with coarse
segmentations, with methods that require full manual segmentation of the objects. Furthermore, we show how to use active learning combined with this weakly supervised strategy.
As we see, this strategy permits to reduce the amount of annotation and optimize the number of examples that require full manual segmentation in the training set.
Keywords: Multiple Instance Learning; Active Learning; Object-class segmentation.
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Cristina Cañero and 16 others. 1999. Optimal Stent Implantation: Three-dimensional Evaluation of the Mutual Position of Stent and Vessel via Intracoronary Ecography. Proceedings of International Conference on Computer in Cardiology (CIC´99).
Abstract: We present a new automatic technique to visualize and quantify the mutual position between the stent and the vessel wall by considering their three-dimensional reconstruction. Two deformable generalized cylinders adapt to the image features in all IVUS planes corresponding to the vessel wall and the stent in order to reconstruct the boundaries of the stent and the vessel in space. The image features that characterize the stent and the vessel wall are determined in terms of edge and ridge image detectors taking into account the gray level of the image pixels. We show that the 30 reconstruction by deformable cylinders is accurate and robust due to the spatial data coherence in the considered volumetric IVUS image. The main clinic utility of the stent and vessel reconstruction by deformable’ cylinders consists of its possibility to visualize and to assess the optimal stent introduction.
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