Invariance-based Target Recognition Using Linear Discriminant Analysis
Ting Rui, Pla Uni · Jisuanji gongcheng · 2005
A novel approach is proposed in this paper. First, principal component analysis (PCA) is used to determine a target’s major axis whosedirection has the maximal variance. Combined with stable low-order moments, the translation, scaling and rotation invariance of a target is achieved.Because of independent component analysis (ICA)’s superior feature extraction capabilities, it is used to extract target features of targets. Finally,target recognition is conducted based on Fisher’s. The experimental results demonstrate the robustness and accuracy of the proposed algorithm.