CS-FREAK: An Improved Binary Descriptor
A large number of vision applications rely on matching key points across images, its main problem is to find a fast and robust key point descriptor and a matching strategy. This paper presents a two-step matching strategy based on voting and an improved binary descriptor CS-FREAK by adding the neighborhood intensity information of the sampling points to the FREAK descriptor. This method divides the matching task in to two steps, firstly simplify the FREAK1 8-layer retina model to a 5-layer one and construct a binary descriptor, secondly encode the neighborhood intensity information of the center symmetry sampling points, and then create a 16-dimentional histogram according to a pre-constructed index table, which is the basis for voting strategy. This two-step matching strategy can improve learning efficiency meanwhile enhance the descriptor identification ability, and improve the matching accuracy. Experimental results show that the accuracy of the matching method is superior to SIFT and FREAK.
point matching binary descriptor two-step matching strategy FREAK
Jianyong Wang Xuemei Wang Xiaogang Yang Aigang Zhao
Department of Automation, Xian Institution of High-tech Xian, China
国际会议
9th Conference on Image and Graphics Technologies and Applications(IGTA2014)(第九届图像图形技术与应用学术会议)
北京
英文
134-141
2014-06-01（万方平台首次上网日期，不代表论文的发表时间）