建筑物类型的确定可为3维重建、图像检索等提供重要依据，而实现其自动判断往往是困难的。通过分析城市建筑物中两大基本类型建筑物的角点特点，利用大尺度形态筛获得初步轮廓区域；通过改进Hough变换和提出的线段、角点优化算法，对各类型角点进行分类统计，实现数码相机拍摄的平顶和非平顶建筑物的自动分类。通过对几十幅采集的建筑物图像进行实验证明，提出的线段优化算法可以有效消除错误轮廓线的影响，进而实现建筑物类型判断，准确率可以达到80%。

Obtaining the type of a building automatically is significant for 3D modeling and image retrieval, but it is very difficult. In this paper, a new automatic urban building classification algorithm is proposed. This work analyzes the corner characteristic of common city buildings, applies morphological sieves of large scale to obtain rough contours. Then it uses a new segment and corner optimization process to count all kinds of corners, and realizes the automatic distinguish of flat roof buildings and non-flat buildings. The tests of dozens of images demonstrate the validity and effectiveness of the proposed segments optimization approach. The results indicate that it can achieve over 80% accuracy on type judgment with the presented method.