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Joan Serrat, J. Argemi and Juan J. Villanueva. 1991. Automatization of TW2 method using a knowledge-based image analysis system. VIth International Congress of Auxology..
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A. Dupuy, Joan Serrat, Jordi Vitria and J. Pladellorens. 1991. Analysis of gammagraphic images by mathematical morphology. Pattern Recognition and image Analysis: IV Spanish Symposium of Pattern Recognition and image Analysis, World Scientific Pub..
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Joan Serrat, Jordi Vitria and J. Pladellorens. 1991. Morphological Segmentation of Heart Scintigraphic image Sequences. Computer Assisted Radiology..
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Judit Martinez, Eva Costa, P. Herreros, Antonio Lopez and Juan J. Villanueva. 2003. TV-Screen Quality Inspection by Artificial Vision. Proceedings SPIE 5132, Sixth International Conference on Quality Control by Artificial Vision (QCAV 2003).
Abstract: A real-time vision system for TV screen quality inspection is introduced. The whole system consists of eight cameras and one processor per camera. It acquires and processes 112 images in 6 seconds. The defects to be inspected can be grouped into four main categories (bubble, line-out, line reduction and landing) although there exists a large variability among each particular type of defect. The complexity of the whole inspection process has been reduced by dividing images into smaller ones and grouping the defects into frequency and intensity relevant ones. Tools such as mathematical morphology, Fourier transform, profile analysis and classification have been used. The performance of the system has been successfully proved against human operators in normal production conditions.
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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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