[The study of SVM-based recognition of particles in urine sediment].

This article used support vector machine (SVM) algorithm to recognize the particles in urine sediment in this paper. After feature extraction, cross-validation method and the contour chart of the accuracy were implemented to select the kernel function and the parameters of SVM, and according to the characteristics of SVM classifier and sample data, Multi-SVMs with two-level-classifier was successfully designed and A classification matrix was eventually obtained. The evaluation by using clinical data and comparative results with the artificial neural network have demonstrated that the proposed algorithm gets better results.

[An autofocus algorithm based on principal component analysis].

After extracting definition measurements by different focus functions from the images, a novel autofocus algorithm which combines them with principal component analysis (PCA) and considers the first principal component as the final measurement, is presented in this paper. The experiment results with 70 groups of images show that this algorithm increases the difference between the definition measurements of the images and provides higher focusing accuracy in comparison with other single ones.