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中文摘要：
提出一种通过分析理解单幅街景图像内的景物构图关系实现图像深度估计的方法.该方法首先对单幅街景图像分块并提取图像块自身的特征以及邻域联合特征,通过机器学习的方法根据图像块的特征识别图像中的各类景物,分析理解街景图像中景物的组成结构;然后,依据小孔成像模型推导出景物的图像坐标和真实深度之间的关系,从而计算出图像内地面区域的深度信息;并根据景物与地面之间的相对位置关系和景物自身的特征变化估计图像内其它景物的深度信息,最终得到整幅街景图像的深度估计结果.实验表明,该方法得到的街景图像深度估计结果能准确反映图像内各个景物在真实世界中的深度分布,在效果上要优于其它的方法.
英文摘要：
A method for depth estimation by understanding how the objects compose the whole scene in a single image of street scene is presented.Firstly,a single image of street scene is segmented into regions.The features of each region and the associated features of its neighbor area are extracted.And the regions are classified as types of object with features of each region by machine learning method,which shows how the image is made up of every object.Then,the depth of ground is estimated by the relationship between coordinate in image and depth in the real world of the same object which is deduced from pin-hole imaging model.And the depth of others in image is estimated by not only the relative position between the objects and ground but also the change of some features in objects.The depth map of image is produced at last. The experiment shows that our algorithm performs better than others and the result of depth estimation reflects the location of each object in the real world exactly.
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