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Locally linear embedding (LLE) is a prevalent manifold learning method in pattern recognition and machine learning. It preserves the intrinsic structure information of data set and has been widely applied to feature extraction and dimensionality reduction. This paper introduces LLE to aircraft pose recognition. The representative motion poses of an aircraft in the air are analyzed. Unfolding results...
With increasing popularity of using motion capture hardware for motion synthesis, studies that exploit large motion databases for faster indexing and retrieval become more important. Here, an automated procedure to analyze motion databases and cluster poses according to various feature vectors is presented. We propose the use of limb centroids as an alternative feature vector. Limb centroids have...
Kernel principal component analysis (KPCA) has gained much attention for capturing nonlinear image features which is particularly important for clustering high-dimensional multi-class features. We introduce KPCA in this paper for detecting and classifying moving vehicles from its viewpoint images. The KPCA extracts non-linear features of multi-class moving vehicles by mapping input space to a higher...
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