An image segmentation method for caged laying hens based on improved active contour model

Abstract

The invention discloses an image segmentation method of a caged laying hen based on an improved active contour model. The S-component images of caged layer images were extracted and analyzed by usingk-Means clustering method to divide the pixels of S-component image into two classes, An S component image is traverse, two class cent functions are obtained, an energy function is set that containsthese two class center functions, the average kernel function and the level set function are introduced into the energy function to form the total energy functional, The standard gradient descent method is used to minimize the total energy functional to obtain the boundary evolution equation. The morphological open operation and Gaussian filtering operation are added to the boundary evolution equation. Finally, the finite difference method is used to iterate the boundary evolution equation until it converges, and the final evolution boundary is the segmentation result of the caged layer image.The invention can quickly and accurately segment the image of the caged laying hen and eliminate the influence of the cage shielding.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to an image segmentation method for caged laying hens based on an improved active contour model. Background technique [0002] Chicken behavior is an important basis to reflect its health, and it is also one of the evaluation indicators to measure breeding welfare (Lao Fengdan et al., 2017). The rapid and accurate segmentation of chicken images is a key step in the application of machine vision systems to quickly identify sick and dead chickens in the actual breeding environment (Bimina et al., 2016). At present, the breeding mode of laying hens in my country is mainly based on the cage mode. Compared with other breeding modes, the laying hens in the cage mode suffer from greater physiological and psychological pressure, and their welfare and health status are more serious. However, due to the occlusion of the cage and the large change in the body shape of the ...

AI Technical Summary

Problems solved by technology

In 2001, Chan and Vese proposed the famous piecewise constant active contour model—CV model, but it is difficult to deal with images with uneven gray levels

In 2008, Li et al. proposed a region shrinkable fitting active contour model—the RSF model, which can effectively segment images with uneven gray levels, but it is sensitive to the setting of the initial contour and is time-consuming.

In terms of agricultural engineering applications, Ma Li et al. (2015) proposed a sow infrared image segmentation method combining CV model and RSF model, but its segmentation efficiency is low, and the anti-occlusion ability and robustness of motion tracking are poor

Xiao Linfang et al. (2018) used the improved C-V model based on morphology to segment the image of caged laying hens, but it needs to use rough segmentation as the initial contour, and the result of rough segmentation will affect the accuracy and efficiency of image segmentation

In summary, the existing segmentation methods based on the active contour model have problems such as sensitivity to initial contour settings, poor motion tracking ability, weak anti-occlusion ability, and time-consuming segmentation.

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