loading
Papers

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Markus Schoeler ; Simon Christoph Stein ; Jeremie Papon ; Alexey Abramov and Florentin Woergoetter

Affiliation: Georg-August University of Göttingen and III, Germany

ISBN: 978-989-758-004-8

Keyword(s): Object Recognition, On-line Training, Local Feature Orientation, Invariant Features, Vision Pipeline.

Related Ontology Subjects/Areas/Topics: Applications ; Pattern Recognition ; Robotics ; Software Engineering

Abstract: Today most recognition pipelines are trained at an off-line stage, providing systems with pre-segmented images and predefined objects, or at an on-line stage, which requires a human supervisor to tediously control the learning. Self-Supervised on-line training of recognition pipelines without human intervention is a highly desirable goal, as it allows systems to learn unknown, environment specific objects on-the-fly. We propose a fast and automatic system, which can extract and learn unknown objects with minimal human intervention by employing a two-level pipeline combining the advantages of RGB-D sensors for object extraction and high-resolution cameras for object recognition. Furthermore, we significantly improve recognition results with local features by implementing a novel keypoint orientation scheme, which leads to highly invariant but discriminative object signatures. Using only one image per object for training, our system is able to achieve a recognition rate of 79% for 18 ob jects, benchmarked on 42 scenes with random poses, scales and occlusion, while only taking 7 seconds for the training. Additionally, we evaluate our orientation scheme on the state-of-the-art 56-object SDU-dataset boosting accuracy for one training view per object by +37% to 78% and peaking at a performance of 98% for 11 training views. (More)

PDF ImageFull Text

Download
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 34.225.194.144

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:
Schoeler, M.; Stein, S.; Papon, J.; Abramov, A. and Woergoetter, F. (2014). Fast Self-supervised On-line Training for Object Recognition Specifically for Robotic Applications.In Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2014) ISBN 978-989-758-004-8, pages 94-103. DOI: 10.5220/0004688000940103

@conference{visapp14,
author={Markus Schoeler. and Simon Christoph Stein. and Jeremie Papon. and Alexey Abramov. and Florentin Woergoetter.},
title={Fast Self-supervised On-line Training for Object Recognition Specifically for Robotic Applications},
booktitle={Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2014)},
year={2014},
pages={94-103},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004688000940103},
isbn={978-989-758-004-8},
}

TY - CONF

JO - Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2014)
TI - Fast Self-supervised On-line Training for Object Recognition Specifically for Robotic Applications
SN - 978-989-758-004-8
AU - Schoeler, M.
AU - Stein, S.
AU - Papon, J.
AU - Abramov, A.
AU - Woergoetter, F.
PY - 2014
SP - 94
EP - 103
DO - 10.5220/0004688000940103

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.