Abstract
The edge-based active contour model has been one of the most influential models in image segmentation, in which the level set method is usually used to minimize the active contour energy function and then find the desired contour. However, for infrared thermal pedestrian images, the traditional level set-based method that utilizes the gradient information as edge indicator function fails to provide the satisfactory boundary of the target. That is due to the poorly defined boundaries and the intensity inhomogeneity. Therefore, we propose a novel level set-based thermal infrared image segmentation method that is able to deal with the above problems. Specifically, we firstly explore the one-bit transform convolution kernel and define a soft mark, from which the target boundary is enhanced. Then we propose a weight function to adaptively adjust the intensity of the infrared image so as to reduce the intensity inhomogeneity. In the level set formulation, those processes can adaptively adjust the edge indicator function, from which the evolving curve will stop at the target boundary. We conduct the experiments on benchmark infrared pedestrian images and compare our introduced method with the state-of-the-art approaches to demonstrate the excellent performance of the proposed method.
Highlights
Infrared imaging has been applied in many application fields, such as industrial inspection, defense and security
In order to deal with the boundary leakage problem and the intensity inhomogeneity, we propose a robust infrared image segmentation method, named intensity adjustment level set evolution (IALSE), which is based on using the level set evolution to extract infrared target contours
Proved that a particular of the classical energy of the snakes model is equivalent to finding a geodesic curve in a Riemannian space with a metric derived from the image content, and introduced the geometric active contours, which is based on the idea that the contours are represented as the zero level set of an implicit function, called level set function
Summary
Infrared imaging has been applied in many application fields, such as industrial inspection, defense and security. In the computer vision and image processing fields, various methods have been proposed to solve the image segmentation problems [1,2,3]. The level set method (LSM) for capturing moving fronts was proposed by Osher and Sethian [4]. In computer vision and image processing, the level set method was introduced independently by Caselles et al [5,6,7] and Malladi et al [8] in the context of the active contour (or snake) models [9,10] for image segmentation.
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