LEARNING OBJECT DETECTION USING MULTIPLE NEURAL NETWORKS

Ignazio Gallo, Angelo Nodari

Abstract

Multiple neural network systems have become popular techniques for tackling complex tasks, often giving improved performance compared to a single network. In this study we propose an innovative detection algorithm in image analysis using a multiple neural network approach where many neural networks are jointly used to solve the object detection problem. We use a group of networks configured with different parameters and features, then combines them in order to obtain new networks. The topology of the set of neural networks is statically configured as a tree where the root node produces in output the detection map. This work represents a preliminary study through which we want to move from detection to segmentation and recognition of objects of interest. We have compared our model with other detection algorithms using a standard dataset and the results are encouraging. The results highlight the advantages and problems that will guide the evolution of the proposed model.

References

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Paper Citation


in Harvard Style

Gallo I. and Nodari A. (2011). LEARNING OBJECT DETECTION USING MULTIPLE NEURAL NETWORKS . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2011) ISBN 978-989-8425-47-8, pages 131-136. DOI: 10.5220/0003328301310136


in Bibtex Style

@conference{visapp11,
author={Ignazio Gallo and Angelo Nodari},
title={LEARNING OBJECT DETECTION USING MULTIPLE NEURAL NETWORKS},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2011)},
year={2011},
pages={131-136},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003328301310136},
isbn={978-989-8425-47-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2011)
TI - LEARNING OBJECT DETECTION USING MULTIPLE NEURAL NETWORKS
SN - 978-989-8425-47-8
AU - Gallo I.
AU - Nodari A.
PY - 2011
SP - 131
EP - 136
DO - 10.5220/0003328301310136