Method for carrying out infrared image segmentation by virtue of active outline

Abstract

The invention discloses a method for carrying out infrared image segmentation by virtue of an active outline. According to the method disclosed by the invention, the grey level distribution information of an image is adequately utilized by constructing an active outline model based on the combined drive of a local entropy and a local standard deviation, meanwhile, the infrared image is segmented by introducing a Gaussian kernel function to much accurately driving outline evolution in a local area. The method disclosed by the invention is capable of effectively realizing segmentation for the infrared image, so as to obtain a complete and correct target outline.

Description

Technical field [0001] The invention belongs to the technical field of image processing, and specifically relates to a method for segmenting infrared images by using active contours. Background technique [0002] Infrared image segmentation plays an important role in the recognition and tracking of infrared targets. Because infrared images usually have the characteristics of blurred edges and uneven gray levels, the commonly used image segmentation methods are difficult to achieve ideal results for infrared image segmentation. In recent years, Active Contour Model (ACM) has been widely used in image segmentation, and it has achieved good results in the segmentation of medical images. However, further research is needed in the field of infrared image segmentation. [0003] Active contour models can be divided into two types: boundary-based models and region-based models. The active contour models based on boundary information use the gradient of the image to define the boundaries of...

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Problems solved by technology

[0004] Literature 1 (Zhang K, Zhang L, Song H, et al. Active contours with selective local or global segmentation: a new formulation and level set method [J]. Image and Vision Computing, 2010, 28(4): 668-676 .) The SLGS model is proposed, which organically combines the GAC and CV models. It uses the gray information defined in the CV model to construct a sign control force function (SPF), which replaces the edge stop function in GAC, so that it can better Control the direction of curve evolution, but it is based on the assumption that the gray level of the area to be segmented is uniform, so the model is not ideal for the segmentation of infrared images with uneven gray levels

[0005] Document 2 (Li C, Kao C Y, Gore J C, et al. Minimization of region-scalable fitting energy for image segmentation [J]. Image Processing, IEEE Transactions on, 2008, 17(10): 1940-1949.) proposed The LBF model mainly uses the gray level information of the local area of ​​the image to drive the curve evolution, and can segment images with uneven gray levels. However, because the model uses less image information, it is also difficult to directly use it for infrared image segmentation. get accurate results

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