Active Contour Segmentation Method of Infrared Ship Image Based on Local Entropy Convex Optimization

Abstract

The invention provides an infrared ship image movable contour segmentation model capable of improving segmentation precision and speed. The method defines the convex optimization energy functional of local entropy, can calculate the local entropy value according to the characteristics of an infrared image itself so as to drive curve evolution and ensure accurate computation and smooth evolution of the model. In the energy functional, a convex optimization analysis process is introduced so as to avoid the local minimum of the model and increase the precision of the segmentation model.

Description

technical field [0001] The invention belongs to the technical field of infrared imaging, in particular to an infrared ship image active contour segmentation method based on local entropy convex optimization that can improve segmentation accuracy and speed. Background technique [0002] With the development of modern technical equipment such as computer processing and network communication, the management of ships on the sea surface is becoming increasingly intelligent and automated. In ship navigation and port ship monitoring, the key technology of infrared imaging system is target segmentation. Segmentation accuracy is the premise of collision risk assessment, multi-objective decision-making, and optimal range for collision avoidance. How to improve it is a key problem that needs to be solved urgently. Among them, the active contour model uses the concept of dynamics to segment images, which has become the first in this field. Major innovation. The basic idea of ​​image s...

AI Technical Summary

Problems solved by technology

[0003] At present, a large number of active contour models are mainly a non-convex problem, and there are local minimum solutions, so that the segmentation results are highly dependent on the initial contour line

In addition, because the curve evolution process depends too much on image features to control, but the discrete gradient in the actual image is bounded, or the edge position near the target object cannot be idealized, these will cause the evolved curve to cross the target actual location of

Especially for images with strong noise, the active contour model is easy to fall into local optimum, resulting in failure of image segmentation

Therefore, the existing non-convex active contour model has a narrow application range, and is only suitable for images with less noise, complete target contours, and obvious contrast with the background

[0004] However, infrared ship images have complex sea environment such as sea clutter, background instability and other factors, the sea background is composed of real scene images and imaging interference

In addition, the imaging process of infrared images reflects the difference in thermal radiation, which is very sensitive to temperature, coupled with the scattering and absorption of thermal radiation by the surrounding environment, blurred edges and almost no texture details in infrared images

Therefore, the existing active contour segmentation methods are not suitable for infrared ship images, and the segmentation accuracy and speed are low

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