中文摘要：

提出了多种图像特征相结合的建筑物和物体识别方法．使用尺度不变特征描述器描述的Harris-Laplace兴趣点以及边缘颜色直方图描述的边缘特征表示图像．边缘和兴趣点包含图像的重要信息．对2种特征的抽取同时进行：基于Harris检测器可以直接得到边缘特征；在多个尺度下进行Harris兴趣点检测，利用Laplace公式得到Harris-Laplace兴趣点．进行物体识别时，根据兴趣点的数目自适应地改变兴趣点和边缘特征的相似性权重．与同类方法相比较表明，该方法具有更高的识别正确率，在视点变化、光照条件变化等情况下具有较好的性能．

英文摘要：

We present a novel approach using combined features to recognize images containing specific objects and buildings. The content of an image is characterized by two kinds of features： Harris-Laplace interest points described by the scale invariant feature transform （SIFT） descriptor and edges described by the edge color histogram. Edges and corners contain the maximal amount of information necessary for object recognition. The feature detection in this work is an integrated process： edges are detected directly based on the Harris function; Harris interest points are detected at several scales and Harris-Laplace interest points are found using the Laplace function. The combination of edges and interest points brings efficient feature detection and high recognition ratio to the object recognition system. Experimental results show that this system has good performance.