Object Detection Using SURF and Superpixels

作     者：Miriam Lopez-de-la-Calleja Takayuki Nagai Muhammad Attamimi Mariko Nakano-Miyatake Hector Perez-Meana 

作者机构：ESIME Culhuacan Instituto Politecnico Nacional Mexico City Mexico University of Electro-Communications Tokyo Japan 

出 版 物：《Journal of Software Engineering and Applications》 (软件工程与应用（英文）)

年 卷 期：2013年第6卷第9期

页      面：511-518页

学科分类：081203[工学-计算机应用技术] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Object Detection SURF SLIC Superpixels Keypoints Detection Local Features Voting 

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.