loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Yuichi Kobayashi 1 ; Ryosuke Matsui 2 and Toru Kaneko 1

Affiliations: 1 Shizuoka University, Japan ; 2 Nippon Systemware Co. Ltd., Japan

Keyword(s): Developmental Robotics, Humanoid Robot, Manifold Learning, Image Features.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Bio-Inspired and Humanoid Robotics ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Image Processing and Artificial Vision Applications ; Methodologies and Methods ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Theory and Methods

Abstract: This paper presents a bottom-up approach to building internal representation of an autonomous robot under a stand point that the robot create its state space for planning and generating actions only by itself. For this purpose, image-feature-based state space construction method is proposed using LLE (locally linear embedding). The visual feature is extracted from sample images by SIFT (scale invariant feature transform). SOM (Self Organizing Map) is introduced to find appropriate labels of image features throughout images with different configurations of robot. The vector of visual feature points mapped to low dimensional space express relation between the robot and its environment. The proposed method was evaluated by experiment with a humanoid robot collision classification.

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 52.14.221.113

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Kobayashi, Y.; Matsui, R. and Kaneko, T. (2013). Manifold Learning Approach toward Image Feature-based State Space Construction. In Proceedings of the 5th International Joint Conference on Computational Intelligence (IJCCI 2013) - NCTA; ISBN 978-989-8565-77-8; ISSN 2184-3236, SciTePress, pages 529-534. DOI: 10.5220/0004630305290534

@conference{ncta13,
author={Yuichi Kobayashi. and Ryosuke Matsui. and Toru Kaneko.},
title={Manifold Learning Approach toward Image Feature-based State Space Construction},
booktitle={Proceedings of the 5th International Joint Conference on Computational Intelligence (IJCCI 2013) - NCTA},
year={2013},
pages={529-534},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004630305290534},
isbn={978-989-8565-77-8},
issn={2184-3236},
}

TY - CONF

JO - Proceedings of the 5th International Joint Conference on Computational Intelligence (IJCCI 2013) - NCTA
TI - Manifold Learning Approach toward Image Feature-based State Space Construction
SN - 978-989-8565-77-8
IS - 2184-3236
AU - Kobayashi, Y.
AU - Matsui, R.
AU - Kaneko, T.
PY - 2013
SP - 529
EP - 534
DO - 10.5220/0004630305290534
PB - SciTePress

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic