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Authors: Shahram Jafari and Ray Jarvis

Affiliation: Intelligent Robotics Research Centre (IRRC), Monash University, Australia

Abstract: This paper introduces a genetic tuner implemented for optimizing the image scene analysis prior to the grasping of the objects, to realize a concrete working robot named COERSU1. Firstly, different architectures of the adaptive neuro-fuzzy inference system, multi-layer perceptron and K-nearest neighborhood classifiers are compared to perform scene analysis and object recognition. Following on, the MLP classifier is chosen due to its accuracy and flexibility to be tuned by genetic algorithm. The real-time experiments (after tuning) show that the performance of the genetically tuned MLP classifier is improved in terms of accuracy due to this hybridization. Finally, snapshots of the experimental results from COERSU in a table-top scenario to manipulate some soft objects (e.g. fruit/egg) are provided to validate the methods.

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Paper citation in several formats:
Jafari, S. and Jarvis, R. (2005). Genetic Tuner for Image Classification with Robotic Applications. In Proceedings of the 1st International Workshop on Artificial Neural Networks and Intelligent Information Processing (ICINCO 2005) - ANNIIP; ISBN 972-8865-36-8, SciTePress, pages 12-22. DOI: 10.5220/0001195100120022

@conference{anniip05,
author={Shahram Jafari. and Ray Jarvis.},
title={Genetic Tuner for Image Classification with Robotic Applications},
booktitle={Proceedings of the 1st International Workshop on Artificial Neural Networks and Intelligent Information Processing (ICINCO 2005) - ANNIIP},
year={2005},
pages={12-22},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001195100120022},
isbn={972-8865-36-8},
}

TY - CONF

JO - Proceedings of the 1st International Workshop on Artificial Neural Networks and Intelligent Information Processing (ICINCO 2005) - ANNIIP
TI - Genetic Tuner for Image Classification with Robotic Applications
SN - 972-8865-36-8
AU - Jafari, S.
AU - Jarvis, R.
PY - 2005
SP - 12
EP - 22
DO - 10.5220/0001195100120022
PB - SciTePress

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