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511 Object Detection Using SURF and Superpixels* (2013)
BibTeX
@MISC{Lopez-de-la-calleja13511object,
author = {Miriam Lopez-de-la-calleja and Takayuki Nagai and Muhammad Attamimi and Mariko Nakano-miyatake and Hector Perez-meana},
title = {511 Object Detection Using SURF and Superpixels*},
year = {2013}
}
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Abstract
Copyright © 2013 Miriam Lopez-de-la-Calleja et al. This is an open access article distributed under the Creative Commons Attribu-tion License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. This paper proposes a novel object detection method in which a set of local features inside the superpixels are extracted from the image under analysis acquired by a 3D visual sensor. To increase the segmentation accuracy, the proposed method firstly performs the segmentation of the image, under analysis, using the Simple Linear Iterative Clustering (SLIC) superpixels method. Next the key points inside each superpixel are estimated using the Speed-Up Robust Fea-ture (SURF). These key points are then used to carry out the matching task for every detected keypoints of a scene in-side the estimated superpixels. In addition, a probability map is introduced to describe the accuracy of the object detec-tion results. Experimental results show that the proposed approach provides fairly good object detection and confirms the superior performance of proposed scene compared with other recently proposed methods such as the scheme pro-posed by Mae et al.
Keyphrases
object detection using surf key point novel object detection method visual sensor probability map original work superior performance simple linear iterative clustering detected keypoints unrestricted use speed-up robust fea-ture good object detection miriam lopez-de-la-calleja et al creative common attribu-tion license segmentation accuracy experimental result open access article object detec-tion result estimated superpixels local feature
