彩色影像切割是機器視覺系統最重要的前處理工作，本論文提出一種非監督式的色彩閥值分割演算法有效的去解決這個議題。此演算法包含學習程序以及多閥值搜尋程序。前者目的在於學習輸入影像在HSV色彩空間中的顏色分佈模型，後者則基於新的變異數標準(class-variance criterion)自動決定最佳閥值去切割影像中的感興趣色彩。在學習程序中採用了彩色像素提取演算法與色彩分佈學習演算法來學習視訊影像的色彩分佈模型，而在多閥值搜尋程序中則提出一非參數化多閥值搜尋演算法與延伸的群內變異數(within-class variance)評估準則自動尋找各色彩通道的最佳上、下限閥值。在完成物件切割後，本論文亦提出一以影像為基礎之物體角度估測演算法，利用幾何物體在不同角度之投影量的改變為概念設計出一準確且有效率之演算法。電腦模擬與實驗結果呈現出本論文提出的兩種演算法皆能達到不錯的效果，亦能搭配機械手臂完成感興趣物體切割以及幾何物體的抓取等任務。 Color image segmentation is one of the most important preliminary tasks in robotic vision systems. This thesis presents a novel unsupervised multilevel color thresholding algorithm to address this issue efficiently. The proposed algorithm consists of a learning process and a multi-threshold searching process. The former aims to learn the color distribution of an input video sequence in HSV color space, and the latter automatically determines the optimal multiple thresholds to segment all colors-of-interest in the video based on a new class-variance criterion. In the learn process, a novel color-distribution learning algorithm cooperating with a color-pixel extraction method is proposed to learn a color distribution model of all colors-of-interest in the video images. In the multi-threshold searching process, a nonparametric multilevel color thresholding algorithm with an extended within-class variance criterion is proposed to automatically find the optimal upper-bound and lower-bound threshold values of each color channel. After segmenting objects-of-interest, this thesis also proposes an image-based object orientation estimation algorithm, which is developed based on the projection of a geometric object with different angles to accurately and efficiently estimate its orientation from a single view image. Simulation and experimental results show that both proposed algorithms not only provide satisfactory results, but also are suitable to combine with a robot manipulator system for achieving object-segmentation and pick-and-place tasks.