應用向量次序統計與模糊梯度於彩色影像之自動邊緣偵測

Abstract

本論文，提出改進基於向量次序統計之彩色邊緣偵測技術的方法，我們的邊緣偵測方法包含兩個部份，首先，第一部份利用模糊梯度的概念來估測每個處理像素的梯度方向，並且根據此方向來調整相對應的視窗方位；第二部部分依向量次序統計計算向量平均距離(VMD)，如此一來，整合了向量次序統計與模糊梯度的偵測方法能夠產生更為穩健的邊緣偵測響應。更進一步，我們將此技術整合到我們所提出的門檻偵測方法，此方法依據影像內容自動作最佳化調整門檻，而不需要手動選取。由測試彩色合成影像與實際影像的數據顯示，我們的自動彩色邊緣偵測是非常方便與可靠的。

In this thesis, we have proposed an improvement of color edge detector based on vector order statistics. The proposed detector consists of two stages. In the first stage, we use the concept of fuzzy gradient to estimate the direction of the gradient for every processing pixel in the image and adjust the corresponding processing window according to this detected direction for reliable edge detection setup. The second stage computes the vector mean distance (VMD) based on vector order statistics. Hence, the proposed detector, which integrates vector order statistics and fuzzy gradient, can provide more robust response for edge detection. Furthermore, we also combine the edge detector to our proposed thresholding method, which can automatically determine an optimal threshold and be adaptive to different image contents without manual intervention. Thus, the excellent results by our proposed edge detection scheme demonstrate that it is very user friendly and confident.