基于光照模糊相似融合不变矩的航天器目标识别

doi:10.7527/S1000-6893.2013.0403

Abstract

Abstract:

In order to solve the non-robustness for affine moment invariants with changing illumination conditions and unknown blurring, an illumination and blur invariant is proposed which is based on the gray light model, image blurring theory and basic moment invariants theory. Combining the geometric affine moment invariants with the proposed illumination and blur invariants, the combined illumination, blur and affine (CIBA) moment invariants are built and their invariant features of illumination, blur and similarity transform (i.e. rotation, scaling and translation) are theoretically proved. Taking multiple images of three spacecraft models as examples, the average recognition accuracy by using CIBA moment invariants in a linear minor distance classifier can reach 86.37%, which is a 94.00% improvement compared with the affine moment invariants, and a 24.54% improvement as compared with the illumination moment invariants. CIBA moment invariants can effectively solve the problem in the identification of spacecraft under different conditions of illumination, unknown blurring and pose. They enhance the target recognition robustness which is based on moment invariants.

Key words: illumination invariant, blur invariant, affine transforms, moment invariant, object recognition, image recognition

CLC Number:

V19
TP391.4

XU Guili, XU Jing, WANG Biao, TIAN Yupeng, GUO Ruipeng, LV Wen. CIBA Moment Invariants and Their Use in Spacecraft Recognition Algorithm[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2014, 35(3): 857-867.

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CIBA Moment Invariants and Their Use in Spacecraft Recognition Algorithm

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