期刊名称：Journal of Software Engineering and Applications
印刷版ISSN：1945-3116
电子版ISSN：1945-3124
出版年度：2013
卷号：6
期号：9
页码：511-518
DOI：10.4236/jsea.2013.69061
出版社：Scientific Research Publishing
摘要：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 Feature (SURF). These key points are then used to
carry out the matching task for every detected keypoints of a scene inside the estimated
superpixels. In addition, a probability map is introduced to describe the
accuracy of the object detection 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 proposed by Mae et
al.
关键词：Object Detection; SURF; SLIC Superpixels; Keypoints Detection; Local Features; Voting
