Saccadic eyes are important human behaviors and have important applications in commercial and security fields. In this
paper, we focus on saccadic eyes recognition from 3-D shape data acquired from a 3-D near infrared sensor. Two salient
features, normal vectors of meshes and curvatures of surfaces, are extracted. The distributions of normal vectors and
curvatures are computed to represent eye states. The support vector machine (SVM) is applied to classify eyes states into
saccadic and non-saccadic eyes states. To verify the proposed method, we performed three groups of experiments using
different strategies for samples selected from 300 3-D data, and present experimental results that demonstrate the
effectiveness and robustness of the proposed algorithm.
A new speckle reduction method for ultrasonic images is presented. The proposed approach exploits the knowledge of
multiplicative speckle model and a regularization scheme is applied to diffusion processing. The nonlinear diffusion is
integrated with dyadic wavelet transform. Experimental results show the new algorithm can not only reduce speckle
effectively, but also preserve and even enhance edge and details.
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