结合样本自动选择与规则性约束的窗户提取方法

A Method for Window Extraction with Automatic Sample Selection and Regularity Constraint

针对窗户内部结构性与分布规则性等特点,提出了一种结合样本自动选择和分布规则性约束的窗户提取方法。首先,利用模板匹配对选取的单个窗户样本进行拓展,自动选择一定数量的正负样本;其次,利用自动选择的样本对JointBoost分类器进行训练,并对建筑物立面影像进行窗户提取;最后,建立包含窗户走向线、倾向线、兴趣点和相似度4个要素的窗户分布规则性模型,并利用规则性模型约束对提取结果进行优化,得到最终窗户提取结果。在复杂背景、复杂窗户结构及存在透视变形的建筑物影像窗户提取实验中,该方法均有较好的检测率与正确率。

Windows are important elements of building facade.Therefore,window extraction is of significant value to building structural analysis and facade reconstruction.With respect to the inner structural feature and the distribution regularity among windows,this paper proposed a window extract method based on automatic sample selection and distribution regularity constraint.Firstly,sample selection was performed by a template matching method to select a number of window samples,both the positive and the negative,from one selected window sample.Secondly,JointBoost classifier,trained by the window samples,was employed to achieve preliminary window extraction.Then,windows distribution regularity model,which includes horizontal direction,vertical direction,point of interest and similarity,was defined and reconstructed using the preliminary window extraction.Finally,the final window extraction result was achieved by optimizing preliminary window extraction result on the constraint of distribution regularity model.The experiments proved that the proposed method has high extraction ratio and accurate ratio on images with complicated background,complex window structure and perspective distortion.