Because a large number of useless features exist in the image of person re-identification due to occlusion and background interference, a person re-identification method based on low-pass filtering model was proposed. First, the person images were divided into blocks. Then the similar number of small blocks in each image were calculated. Among them, the blocks with higher similarity number were marked as high-frequency noise features and the blocks with smaller similarity number were the beneficial features. Finally, different from the low-pass filter which filtered the mutation features and maintained the smooth features in the common image processing, the low-pass filter in the communication system was used to achieve the goal of suppressing high-frequency noise features and gain beneficial features in the proposed method. Experimental results show that the identification rate of the proposed method on ETHZ dataset is nearly 20% higher than that of the classic Symmetry-Driven Accumulation of Local Features (SDALF) method, and at the same time, this method achieves similar results on VIPeR (Viewpoint Invariant Pedestrian Recognition) and I-LIDS (Imagery Library for Intelligent Detection Systems) datasets.

In oral surgery, segmentation of teeth has important application value. However, due to the ambiguity of tooth boundary, the adhesion of adjacent teeth, and the flexible change of topological structure in dental Computer Tomography (CT) images, it is very difficult to achieve the accurate segmentation. To provide a useful reference for researches, this paper explored the search progress of dental CT image segmentation base on level set methods, summarized the traditional methods of dental CT images segmentation, introduced the level set theory briefly, introduced the details of level set methods for teeth segmentation in recent years, studied the energy terms in level set function, and implemented some contrast experiments. In the dental CT images segmentation based on level set method, the energy terms mainly included competitive energy, edge energy, shape prior energy, global intensity prior energy and local intensity energy. The experimental results show that the performance of hybrid model of the level set method is the best. The segmentation accuracies of incisor and molar teeth were 88.92% and 92.34% respectively. Compared to the method of adaptive threshold and level set without re-initialization, the accuracy of hybrid model improved more than 10% overall. With the utilization of image information and prior knowledge, it is expected to improve the accuracy of segmentation by optimizing and innovating the energy term in the level set function.