Abstract: Aiming at the weakness of the computational complexity and huge calculation of the three-dimensional Otsu, a fast three-dimensional Otsu image segmentation algorithm based on decomposition was presented in this paper. Firstly, the original three-dimensional Otsu algorithm was decomposed into three one-dimensional Otsu algorithms. Then, based on the one-dimensional Otsu algorithm, a novel algorithm with a new threshold recognition function was proposed, which combines between-class distance with within-class distance, and the fast realization method was also presented. The experimental results show that the proposed algorithm does not only get satisfactory segmentation result, but also improves the calculation speed, which spends 1400 times less than the recursive algorithm for the three-dimensional Otsu method.

Locality Preserving Projection (LPP) is a manifold learning method, while the face recognition application of LPP is known to suffer from singular value problem, so a solution scheme using Singular Value Decomposition (SVD) was proposed for recognition application. In this model, the sample data were projected on a non-singular orthogonal matrix to solve the problem of singular value. Then the data of the low dimensional sample space projection subspace were obtained according to the LPP method. The training samples and testing samples were projected onto low-dimensional subspace respectively. Finally the nearest neighbor classifier was used for classification. A series of experiments to compare the proposed algorithm with the traditional local projection algorithm and Principal Component Analysis (PCA) were given on ORL face database. The experimental results demonstrate the efficacy of the improved LPP approach for face recognition.