本文提出了一種新的基于同質性和時間自適應自組織神經網絡（TASOM）的邊緣檢測算法。算法首先利用同質性檢測到圖像的候選邊緣點，并根據(jù)這些點構造TASOM網絡結構；其次，利用Canny算子檢測到的邊緣點作為網絡訓練的特征點；最后，通過比較控制點和特征點調整TASOM網絡的連接權，實現(xiàn)對網絡神經元的自動添加和刪減，達到保持邊緣的連續(xù)性和刪除冗余邊緣點的目的。試驗結果表明所提算法優(yōu)于傳統(tǒng)的邊緣檢測方法。
關鍵詞同質性；Canny算子；TASOM；邊緣檢測；特征點
Abstract This paper presents a new algorithm for edge detection based on homogeneity and time adaptive self-organizing neural networks (TASOM). Firstly, the method uses homogeneity to extract the candidate edge points, and constructs the TASOM network according to these points; and then, make the edge points detected by Canny operator as
feature points of the TASOM network; finally, the weights of TASOM are updated to achieve the adding and deletion of neurons automatically by comparing the difference between control points and feature points; consequently, the aims that remaining the continuity of the edges and deleting the redundant points are achieved. The experimental results show that the
proposed method is superior to other algorithms.
Keywords homogeneous；canny operator；TASOM；edge detection；feature points