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Edge Detection based on Ant Colony Optimization Using Adaptive Thresholding Technique

机译:基于蚁群算法的自适应阈值边缘检测

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Image edge detection is a process where true edges of an image are identified. In past, gradient based methods in which first or second order pixel difference is used to find discontinuities and if magnitude value of gradient is higher than certain threshold then that pixel under observation is identified as edge pixel. These methods are full of error, because in addition to true edges they also find false edges and infect false edges are more in comparison to true edges. To solve such problem, swarm intelligence based ant colony optimization based edge detection method is detailed where numbers of falsely detected edges are very small. The performance of the ant colony optimization (ACO) is done in terms of Peak Signal to Noise Ratio, Performance Ratio and Efficiency.
机译:图像边缘检测是识别图像的真实边缘的过程。过去,使用基于梯度的方法,其中使用一阶或二阶像素差来查找不连续性,并且如果梯度的大小值高于某个阈值,则将观察中的像素标识为边缘像素。这些方法充满错误,因为与真实边缘相比,除了真实边缘之外,它们还会发现虚假边缘,并且感染虚假边缘的可能性更大。为了解决该问题,详细描述了基于群体智能的基于蚁群优化的边缘检测方法,其中错误检测的边缘的数量非常少。蚁群优化(ACO)的性能是根据峰值信噪比,性能比和效率来完成的。

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