The existing binary descriptors,generated from random or uniform point pairs sampling,suffer from low robustness and high computation. A novel sampling method, named RBS (retina-imitation based sampling), was proposed, which combines different densities sampling, multi-scale smoothing and reception field overlapping to imitate the converting from light signal to vision of ganglion cells of human retina cells, and further selects most discriminative comparison pairs based on learning on training data. Finally,compact binary descriptor was generated based on comparisons between the neighbor mean instead of singe sampled point. The experimental results show the RBS-128 with 128 bit outperforms FREAK and BRSIK with 512 bit about 16.4％ and 5.3％ in precision on the dataset provided by Mikolajczyk.%现有二进制描述子生成采用随机点对或均匀采样方式,顽健性弱、计算复杂.针对这一问题,提出了一种模仿人眼视网膜特性的采样模式(RBS),首先通过设计采样密度、多尺度光滑、视野重叠等采样方法来模仿视网膜神经节细胞层(ganglion cell layer),也称为视神经层,将光信号转换为视信息的方式,再通过对典型数据学习来选择特征点对,最后使用区块均值代替单像素点计算点对比较值,生成顽健的紧致二进制描述子.在 Mikolajczyk 提出的数据集上进行了实验,实验结果表明,128 bit 的RBS-128 相对于512 bit 的FREAK 和BRISK正确率分别提升16.4％和5.3％.
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