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Recognizing point clouds using conditional random fields

By Farzad Husain, Babette Dellen and Carme Torras

Abstract

Trabajo presentado a la 22nd International Conference on Pattern Recognition (ICPR-2014), celebrada en Estocolmo (Suecia) del 24 al 28 de agosto.Detecting objects in cluttered scenes is a necessary step for many robotic tasks and facilitates the interaction of the robot with its environment. Because of the availability of efficient 3D sensing devices as the Kinect, methods for the recognition of objects in 3D point clouds have gained importance during the last years. In this paper, we propose a new supervised learning approach for the recognition of objects from 3D point clouds using Conditional Random Fields, a type of discriminative, undirected probabilistic graphical model. The various features and contextual relations of the objects are described by the potential functions in the graph. Our method allows for learning and inference from unorganized point clouds of arbitrary sizes and shows significant benefit in terms of computational speed during prediction when compared to a state-of-the-art approach based on constrained optimization.This work was supported by the EU project (IntellAct FP7-269959), the project PAU+ (DPI2011-27510), and the CSIC project CINNOVA (201150E088). B. Dellen was supported by the Spanish Ministry for Science and Innovation via a Ramon y Cajal fellowship.Peer Reviewe

Topics: Proceedings 22nd International Conference on Pattern Recognition ICPR 2014 24–28 August 2014 Stockholm, Sweden
Publisher: Institute of Electrical and Electronics Engineers
Year: 2016
DOI identifier: 10.1109/ICPR.2014.730
OAI identifier: oai:digital.csic.es:10261/127416
Provided by: Digital.CSIC

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