Apple Image Fusion Based on Scale-invariant Feature Transform

doi:10.3969/j.issn.1006-2475.2017.10.002

Abstract

Abstract: 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.

Key words: apple quality inspection, image fusion, SIFT

LUO Xiao-qing, WANG Peng-fei. Apple Image Fusion Based on Scale-invariant Feature Transform[J]. Computer and Modernization, 2017, 0(10): 5-9.

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