透過圖像分割使用多個方法對人臉進行邊緣檢測

Abstract

一張圖像對人們來講，往往只對圖像中某些部分感興趣，這些部分稱為目標或是前景，而其他部分稱為背景。將圖像切割成目標和背景是圖像處理的基本工作，這個工作的主要目的是找出目標的邊緣和線條，對人類視覺系統而言，可以很容易的辨識出目標的邊緣並分割出目標和背景，這是一件相當輕而易舉的事，但是在計算機視覺系統中，如何模仿人類視覺能力的行為是值得研究的主題。 故本文將透過Robert、Prewitt、Sobel和Laplacian等邊緣檢測法來提取人臉邊緣，並且結合了平均值法、P分位數法、雙峰平均值法、迭代最佳閾值法等圖像分割閾值法，來提升檢測邊緣的結果，讓效果更好，並藉此比較不同方法的優缺點。

An image for people, usually they only interest some parts of it, these parts called "target" or "front scene", and the other parts called "background". Dividing an image into targets and back scenes is the basic work in image processing, this work has primarily goal which is to recognize the edges and boundaries of a target. For human's visual system, that can easily identify a target whose edges and separate target from background, which is an easy task to accomplish. However, in computer’s visual system, how to imitate human's visual ability, is a study-worth topic. In this paper, we’ll using Robert, Prewitt, Sobel and Laplacian, four methods of edge detection to extract edge from human face, and combine Mean, P-Tile, Two-Peaks and Optimal Threshold Iterated, four image segmentation threshold algorithm to increase the results of edge detection, make the effort better. In furthermore, to compare with these different methods’ pros and cons.