@inproceedings{af416d28615448c4be806f56de7d3e91,
title = "Automatic detection and classification of obstacles with applications in autonomous mobile robots",
abstract = "Hardware implementation of an automatic detection and classification of objects that can represent an obstacle for an autonomous mobile robot using stereo vision algorithms is presented. We propose and evaluate a new method to detect and classify objects for a mobile robot in outdoor conditions. This method is divided in two parts, the first one is the object detection step based on the distance from the objects to the camera and a BLOB analysis. The second part is the classification step that is based on visuals primitives and a SVM classifier. The proposed method is performed in GPU in order to reduce the processing time values. This is performed with help of hardware based on multi-core processors and GPU platform, using a NVIDIA R GeForce R GT640 graphic card and Matlab over a PC with Windows 10.",
keywords = "Classication, Detection, Disparity Map, Features Descriptors, Mobile Robot, Obstacles, Video Sequence",
author = "Ponomaryov, {Volodymyr I.} and Rosas-Miranda, {Dario I.}",
note = "Publisher Copyright: {\textcopyright} 2016 SPIE.; Real-Time Image and Video Processing 2016 ; Conference date: 07-04-2016",
year = "2016",
doi = "10.1117/12.2227758",
language = "Ingl{\'e}s",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Carlsohn, {Matthias F.} and Nasser Kehtarnavaz",
booktitle = "Real-Time Image and Video Processing 2016",
address = "Estados Unidos",
}