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Authors: Julia Moehrmann and Gunther Heidemann

Affiliation: University of Osnabrück, Germany

ISBN: 978-989-758-077-2

Keyword(s): Image Recognition System, Development, Image Recognition, Image Annotation, Ground Truth Annotation.

Related Ontology Subjects/Areas/Topics: Applications ; Computer Vision, Visualization and Computer Graphics ; Image Understanding ; Object Recognition ; Pattern Recognition ; Software Engineering ; Web Applications

Abstract: Despite the growing importance of image data, image recognition has succeeded in taking a permanent role in everyday life in specific areas only. The reason is the complexity of currently available software and the difficulty in developing image recognition systems. Currently available software frameworks expect users to have a comparatively high level of programming and computer vision skills. FOREST – a flexible object recognition framework – strives to overcome this drawback. It was developed for non-expert users with little-to-no knowledge in computer vision and programming. While other image recognition systems focus solely on the recognition functionality, FOREST covers all steps of the development process, including selection of training data, ground truth annotation, investigation of classification results and of possible skews in the training data. The software is highly flexible and performs the computer vision functionality autonomously by applying several feature detection and extraction operators in order to capture important image properties. Despite the use of weakly supervised learning, applications developed with FOREST achieve recognition rates between 86 and 99% and are comparable to state-of-the-art recognition systems. (More)

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Paper citation in several formats:
Moehrmann J. and Heidemann G. (2015). FOREST - A Flexible Object Recognition System.In Proceedings of the International Conference on Pattern Recognition Applications and Methods - Volume 2: ICPRAM, ISBN 978-989-758-077-2, pages 119-127. DOI: 10.5220/0005175901190127

@conference{icpram15,
author={Julia Moehrmann and Gunther Heidemann},
title={FOREST - A Flexible Object Recognition System},
booktitle={Proceedings of the International Conference on Pattern Recognition Applications and Methods - Volume 2: ICPRAM,},
year={2015},
pages={119-127},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005175901190127},
isbn={978-989-758-077-2},
}

TY - CONF

JO - Proceedings of the International Conference on Pattern Recognition Applications and Methods - Volume 2: ICPRAM,
TI - FOREST - A Flexible Object Recognition System
SN - 978-989-758-077-2
AU - Moehrmann J.
AU - Heidemann G.
PY - 2015
SP - 119
EP - 127
DO - 10.5220/0005175901190127

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