基于蚁群优化算法的图像边缘检测

图像边缘携带了图像的大部分主要信息。通过对图像进行边缘检测不仅能有效地提取图像信息降低计算的复杂度而且是图像测量、图像分割、图像压缩、模式识别等图像处理的基础。本文尝试将蚁群优化算法（Ant Colony Optimization, ACO）用于图像边缘检测，通过选取经典house图像和SAR机场图像设置阈值进行自适应边缘提取，实现了边缘的精确检测。实验结果显示，该算法能够有效地提取图像目标的轮廓信息，很好保持图像纹理，具有理想的抗干扰性能，保证了检测结果的准确性。

Image Edge Detection Based on Ant Colony Optimization Algorithm

Image edge carries most of the major information of the image. And image edge detection can effectively reduce the computation complexity and is also the basis of image processing such as image measurement, image segmentation, image compression, pattern recognition and so on. In this paper Ant Colony Optimization (ACO) was used in image edge detection. The house image and SAR airport image were adaptively extracted by setting threshold, and accurate edge detection can be realized. Experimental results indicate that this algorithm can effectively extract the image object contour information, keep images texture, show ideal anti-jamming competence, and guarantee the detection accuracy.