Iterative Closest Point

vignette|Illustration de l'idée derrière l'algorithme itératif du point le plus proche L'Iterative Closest Point (ICP) est un algorithme utilisé dans le but de mettre en correspondance deux jeux de données, le plus souvent sous la forme de nuages de points ou maillages correspondant à deux vues partielles d'un même objet. Une vue étant constituée d'un ensemble de points (reliés ou non par des arêtes), l'objectif est de minimiser itérativement la distance entre ces points. Les nombreuses évolutions qui ont été apportées à l'algorithme, et notamment l'abandon du critère d'appairage des données basé sur la distance pure, lui valent parfois le nom de Iterative Corresponding Point. L'ICP est utilisé dans de très nombreux domaines nécessitant la reconstruction d'objets 3D à partir de vues partielles : vision par ordinateur, robotique ou encore rétro-conception. Principe L'algorithme de base est conceptuellement simple : à partir de deux nuages de points, ce dernier va itérative

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Imaging in radio interferometry by iterative subset scanning using a modified amp algorithm

Giovanni Cherubini, Paul Hurley, Sanaz Kazemi, Matthieu Martin Jean-André Simeoni

Imaging techniques in radio interferometry often face a significant challenge posed by the large number of antenna signals received, from which the image information needs to be extracted. Beam- forming is envisaged to reduce the rate required for transporting data from groups of antennas to a central site for further processing. We propose a novel method for image reconstruction based on the iterative scanning of a region of interest, combined with randomized beamforming. A modified approximate message-passing algorithm is adopted to extract relevant image information from beamformed signals received at the antenna stations. The method is illustrated by simulations, with reference to the LOFAR radio interferometer, and compared with the CLEAN algorithm.

2016

Chargement

Iterative image subset processing for image reconstruction

Giovanni Cherubini, Paul Hurley, Sanaz Kazemi, Matthieu Martin Jean-André Simeoni

The present invention is notably directed to computer-implemented methods and systems for recovering an image. Present methods comprise: accessing signal data representing signals; identifying subsets of points arranged so as to span a region of interest as current subsets of points; reconstructing an image based on current subsets of points, by combining signal data associated to the current subsets of points; detecting one or more signal features in a last image reconstructed; for each of the detected one or more signal features, modifying one or more subsets of the current subsets, so as to increase, for each of the modified one or more subsets, a relative number of points at a location of said each of the detected one or more signal features. The relative number of points of a given subset at a given location may be defined as the number of points of said given subset at the given location divided by the total number of points of said given subset, whereby new current subsets of points are obtained; and repeating the above steps of reconstructing, detecting and modifying, as necessary to obtain a reconstructed image that satisfies a given condition.

2017

Chargement

A Combined Approach Toward Consistent Reconstructions of Indoor Spaces Based on 6D RGB-D Odometry and KinectFusion

Nadia Barbara Figueroa Fernandez

We propose a 6D RGB-D odometry approach that finds the relative camera pose between consecutive RGB-D frames by keypoint extraction and feature matching both on the RGB and depth image planes. Furthermore, we feed the estimated pose to the highly accurate KinectFusion algorithm, which uses a fast ICP (Iterative Closest Point) to fine-tune the frame-to-frame relative pose and fuse the depth data into a global implicit surface. We evaluate our method on a publicly available RGB-D SLAM benchmark dataset by Sturm et al. The experimental results show that our proposed reconstruction method solely based on visual odometry and KinectFusion outperforms the state-of-the-art RGB-D SLAM system accuracy. Moreover, our algorithm outputs a ready-to-use polygon mesh (highly suitable for creating 3D virtual worlds) without any postprocessing steps.

Assoc Computing Machinery2015

Chargement

DH-405: Foundations of digital humanities

This course gives an introduction to the fundamental concepts and methods of the Digital Humanities, both from a theoretical and applied point of view. The course introduces the Digital Humanities circle of processing and interpretation, from data acquisition to new understandings.