Modified von Neumann neighborhood and taxicab geometry-based edge detection technique for infrared images

Abstract

Infrared images have several applications such as security, health, passenger monitoring, and so on. The quality of infrared image gets affected by noise, blurring effect, and low illumination environment. Due to the low contrast, blurring, and hazy effects in infrared images, state-of-the-art techniques are frequently unable to achieve appropriate edge details. Thus, an edge detection algorithm is proposed using a modified Von Neumann neighborhood kernel and taxicab geometry-based shortest path method. It has been found to perform in a better manner compared to earlier studies in a similar field. The objective of the proposed method is to produce sharp, less noisy and robust edge lines. First, pre-processing of the image is done for edge-preserving smoothing of an infrared image using a smoothing parameter. Second, image segmentation is done based on a two-level threshold value computed by a modified Von Neumann-based kernel. Then, Fourier transform of the segmented image is done to remove spike noise followed by the inverse Fourier transform to produce the final edge lines. The simulation experiment results show that the proposed method is found to yield robust and sharp edge lines compared to other state-of-the-art methods both numerically and visually. Moreover, the whole process takes less computation time.