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Image Edge Tracking via Ant Colony Optimization

机译:通过蚁群优化进行图像边缘跟踪

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A good edge plot should use continuous thin lines to describe the complete contour of the captured object. However, the detection of weak edges is a challenging task because of the associated low pixel intensities. Ant Colony Optimization (ACO) has been employed by many researchers to address this problem. The algorithm is a meta-heuristic method developed by mimicking the natural behaviour of ants. It uses iterative searches to find the optimal solution that cannot be found via traditional optimization approaches. In this work, ACO is employed to track and repair broken edges obtained via conventional Sobel edge detector to produced a result with more connected edges.
机译:好的边缘图应使用连续的细线来描述捕获对象的完整轮廓。然而,由于相关联的低像素强度,弱边缘的检测是一项具有挑战性的任务。蚁群优化(ACO)已被许多研究人员用来解决此问题。该算法是通过模仿蚂蚁的自然行为而开发的一种元启发式方法。它使用迭代搜索来找到传统解决方案无法找到的最佳解决方案。在这项工作中,ACO被用来跟踪和修复通过常规Sobel边缘检测器获得的折断边缘,以产生具有更多连接边缘的结果。

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