Based on Improved Level Set Algorithm to Enhanced Fuzzy C-Means Clustering for Controlled Knife Image Segmentation

Abstract

With the deepening of China's urbanization construction, public safety is also facing great challenges. Therefore, China has strengthened security measures for important public places, and established monitoring network and security inspection system. However, the current machine security inspection algorithm is not accurate enough for the identification of sensitive objects such as controlled knives, and it also depends on manual detection and confirmation, which leads to low work efficiency and often leads to low efficiency It is also not conducive to the prevention and control of the epidemic. This paper proposes an image segmentation method based on Improved level set to enhance fuzzy clustering. In this paper, the fuzzy clustering method and the improvement of the level set method, first of all, using morphological method image preprocessing algorithms, so as to realize the foreground and background of the area range, is advantageous for the next level set segmentation of the target outline, so as to determine the fuzzy clustering of heart, makes strengthening fuzzy clustering on its own the advantage of rapid more accurate. Through experiments, the effectiveness of the algorithm is verified, and the segmentation accuracy is higher than the traditional segmentation algorithm.

Improvement of Point Spread Function Estimation Method for Motion Blurred Image and Image Restoration

“…Traditional PSF estimation methods are affected by the bright cross in the center of the spectrum, which reduces the of blur angle estimation and thus affects the effectiveness of image restoration. Therefore, this article proposes a new motion blur parameter estimation scheme and conducts blur angle accuracy measurement experiments using optical images as an example [19]. The experimental results show that the PSF estimation algorithm in this paper improves the estimation accuracy of motion blur angles, thereby achieving the quality of image deconvolution restoration, which meets the practical engineering requirements of image restoration problems.…”