基于图像边缘与玻璃属性约束的窗户检测

窗户检测是建筑物图像理解的重要内容之一。为此提出了一种基于图像边缘与玻璃 属性约束的窗户检测方法。首先，将图像边缘作为窗户的初始定位，然后分别在边缘两侧提取局 部颜色和纹理特征，并将二者的差异性作为识别窗户的特征，最后，先采用Mahalanobis 距离分 类器进行局部含窗区域判决，再利用图像分割算法提取窗户的整体区域。实验表明，提出的算法 能在窗户形状结构未知的情况下，实现对建筑物表面的窗户有效检测。

Window Detection Based on Constraints of Image Edges and Glass Attributes

Window detection is one of the important researches on building image understanding. We proposed an approach of window detection under edges and glass attribute constraints. Firstly, windows are basically located with edges in image. Next, color and texture features on the edge neighbor are extracted, and the dissimilarities between the two features are applied for windows recognition. Finally, the Mahalanobis distance classifier is first adopted to improve the local recognition containing window area, then the image segmentation algorithm is used to extract the whole area of the window. Experiments show that our proposed approach can efficiently detect windows in the case of unknowing the shape and structure of windows.