提出融合梯度幅值(GM)和梯度发生率(GO)的新方法,构建了两种具有高分辨能力的新描述符。抑制归一化描述符通过对GM进行分段归一化,抑制较小的GM以构建新描述符;GM-GO融合描述符是通过抑制部分GO(其对应的GM较小)、再将GM和GO进行融合的新描述符,可提高描述符的可分辨性。实验结果表明,在噪声和光照等环境的影响下,提出的两种方法都具有更高的匹配精度。

In this paper, a new method based on fusing gradient magnitude (GM) and gradient occurrence (GO) is proposed to construct two new descriptors with high resolution. The suppression normalization descriptor constructs a new descriptor by segmenting the GM and inhibiting the smaller GM. GM-GO fusion descriptor is a new descriptor that fuses GM and GO by suppressing partial GO values (which correspond to a smaller GM) to improve the discriminability of the descriptor. Experimental results show that the two proposed methods have higher matching accuracy under the influences of noise and illumination.