基于尺度不变特征变换的苹果图像融合  

Apple Image Fusion Based on Scale-invariant Feature Transform

作　　者：罗晓清[1] 王鹏飞[1] 

机构地区：[1]江南大学物联网工程学院,江苏无锡214122

出　　处：《计算机与现代化》2017年第10期5-9,共5页Computer and Modernization

基　　金：江苏省自然科学基金资助项目(BK20151358;BK20151202)

摘　　要：提出一种基于尺度不变特征变换的图像融合方法,用于辅助苹果质量检测。首先利用非下采样轮廓波变换将待融合图像分解为低频子带和高频子带;然后对低频子带利用尺度不变特征变换寻找特征描述子,并记录下每个特征描述子在低频子带中的位置,利用特征描述子构造一种内容匹配度指标,提出一种基于内容匹配度的混合融合策略用来融合低频子带;对高频子带利用绝对值取大的融合策略实现高频子带系数的融合;最后利用非下采样轮廓波逆变换生成融合图像。实验结果表明,本文提出的方法在苹果质量检测中是可行的、有效的。A scale-invariant feature transform based image fusion method is proposed to detect the quality of apple in this paper.First,source images are decomposed into low frequency subbands and high frequency suabbands by nonsubsampled contourlet transform（ NSCT）. Second,a scale-invariant feature transform（ SIFT） method is employed to find descriptors of low frequency subbands,which are used to construct a content matching metric. Next,this metric is introduced into the fusion of low frequency subbands. Then,a choose-max fusion rule is adopted to fuse the high frequency subbands. At last,the composite subbands are converted into a fused image by the inverse NSCT. Experimental results show the proposed method is sufficient and efficient in the application of apple quality inspection.

关 键 词：苹果质量检测 图像融合 尺度不变特征变换 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]