Invariance-Based Target Recognition Using Independent Component Analysis
Ting Rui · Mini-micro Systems · 2005
In target recognition, translation-, scaling-and rotation-invariance is a very important factor. While there exist object moment invariance, the high-order ones are sensitive to noise, resulting in instability in target recognition. To overcome these difficulties, a novel approach is proposed in this paper. First, principal component analysis (PCA) is used to determine a target's major axis whose direction has the maximal variance. Combined with stable low-order moments, the translation, scaling and rotation invariance of a target is achieved. Independent component analysis (ICA) is used to extract target features for different classes of targets. Next, target models are reconstructed in their own feature spaces. Finally, target recognition is conducted based on reconstructed models' error analysis. The proposed algorithm is tested with two experiments. The experimental results demonstrate the robustness and accuracy of the proposed algorithm.