基于改进活动轮廓模型的图像分割

Image Segment Based on Improved Active Contour Model

图像分割是图像研究领域的一个热点问题,当前图像分割方法难以准确、快速实现分割,经常出现“过分割”或者“欠分割”的现象,错误概率高。为此提出了基于改进活动轮廓模型的图像分割方法。首先对当前图像分割的研究进展进行分析,找到引起图像分割不足的原因,然后引入活动轮廓模型对图像进行分割,并针对传统活动轮廓模型存在的局限性,对其进行相应的改进,以获得更优的图像分割效果,最后与其它图像分割方法在相同环境进行仿真对比实验,以验证改进活动轮廓模型的优越性。结果表明,改进活动轮廓模型可以对各种图像进行高精度的分割,分割区域十分完整,而且对噪声的鲁棒性要明显优于当前其它图像分割方法,是一种精度高、速度快的图像分割方法,为后续图像处理打了良好的基础。

Image segmentation is a hot issue in the field of image research.It is difficult for current image segmentation methods to segment images accurately and quickly.There are often"over-segmentation"or"under-segmentation"phenomena.The error probability of image segmentation is quite high.Therefore,an image segmentation method based on improved active contour model is proposed.Firstly,the current research progress of image segmentation is analyzed to find out the reasons for the inadequacy of image segmentation.Then the active contour model is introduced to segment the image.Aiming at the limitations of the standard active contour model,the active contour model is improved accordingly to obtain better image segmentation effect.Finally,it is simulated with other image segmentation methods in the same environment.True contrast experiments are conducted to verify the superiority of the improved active contour model.The results show that the improved active contour model can segment all kinds of images with high accuracy,the segmentation area is very complete,and the robustness to noise is obviously better than other image segmentation methods.It is a high precision and fast image segmentation method,and lays a good foundation for subsequent image processing.