特徵描述子為電腦視覺中相當重要的一部分，本論文基於知名的特徵描述子：區域二元化圖型的架構上，提出了新的特徵描述子，並將其命名為延展式區域三元化圖型。我們所提出的特徵描述子與區域二元化圖型相比，有著較強的抗噪能力而且保留了區域二元化圖型簡單的計算複雜度。本論文也探討了區域三元化圖型中是否存在著uniform pattern，基於區域二元化圖型中uniform pattern的定義，我們提出了屬於區域三元化圖型的uniform pattern，並在圖像實驗中驗證了其大量存在性。我們將區域三元化圖型應用於材質分析與人臉辨識中，實驗結果驗證了本論文所提出的特徵描述法在這些應用的優越性。 / Robust feature descriptor is essential in developing effective computer vision applications. In this thesis, we present an extension to the well-known local binary pattern (LBP) feature descriptor. The newly defined descriptor known as extended local ternary pattern (ELTP) exhibits better noise resistivity than the original LBP, while maintaining computational simplicity. We further investigate the presence of uniform patterns in ELTP. With a slight modification of the definition of uniformity, it is found experimentally that uniform ELTPs account for 80% of all patterns in texture images. The proposed ELTP and uniform ELTP are applied to object classification tasks, including texture analysis and face recognition. Experimental results validate the superiority of ELTP over conventional LBP approaches.