Robust noise hybrid active contour model for infrared image segmentation using orientation column filters

Infrared (IR) image segmentation plays an important role in many applications of night vision, including pedestrian detection, security monitoring, etc. However, the precision is constrained by edge blur and noise interference from the original infrared imaging. In order to achieve robust segmentation results under noise interference, a hybrid active contour model for the segmentation of targets in images using local feature information and global information is proposed. Based on the concept of orientation columns in the primary visual cortex, orientation column filters are defined, which can effectively extract local feature information with noise robustness. Then a global term with noise robustness is defined, and the adaptive weight matrix is adopted to combine the two to construct a complete signed pressure force (SPF) function. Several experiments demonstrate that the proposed algorithm performs more accurately and robustly on noisy infrared images segmentation compared to typical algorithms.

Automated summary

This paper proposes a hybrid active contour model for infrared image segmentation to address the issue of segmentation under noise interference. By extracting local feature information and global information, more accurate and robust segmentation results are achieved.