基于Tabu搜索算法的似然性估计在边界检测中的运用

边界检测一直是图像分析处理的重点和难点，现在的边界检测算法（如K－L变换，Sobel算子的梯度检测，模板检测等）在不同程度上存在着抗噪声能力不强，算法计算量过 大的缺点，不适于实时应用，新出现的一批智能化自适应算法（如Tabu搜索算法，遗传算法等）在此方面有很大改进，本文主要介绍Tabu算法在边界检测中的运用。

Application of the Tabu-Algorithm-Based Possibility Predication in Edge Finding

Edge finding has been a difficult and important task in image processing. The current edge-finding algorithms (e.g. K-L transformation, Sobel gratitude edge finding, model matching) are vulnerable to noise or need too much computation, which are not suitable for real-time applications. Recently-emerged intelligent adaptive algorithms , such as the Tabu algorithm and GAs, have improved a lot. This paper will mainly introduce the application of the Tabu algorithm in edge finding.