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Abstract
In this paper, a new method coined dynamic two-directional two-dimensional principal component analysis (D(2D)2PCA) was established, which has further developed the feature expression of the video image data collected by the optical vision system of unmanned underwater vehicle (UUV). The contributions are as follows: (1) D(2D)2PCA is also based on the dynamic characteristics of image data in addition to being relied on the static relationship among data; (2) An image covariance matrix that directly uses the sample image data in the dynamic time series augmented matrix is constructed, and the projection vector for image feature extraction is derived; (3) To test and performance estimation of D(2D)2PCA, a series of experiments were performed on the video image data collected by underwater optical vision system. Experimental results show that, compared with the traditional underwater target recognition method, D(2D)2PCA has more effective feature extraction ability and higher target recognition accuracy, which improves the autonomous underwater operation ability of UUV.
Published Version
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