Outdoor World Modeling by Intelligent Integration of Multi-Visual Information

在本课题中，我们提出了一种场景解译方法，该方法根据感官数据处理的中间结果，根据场景中物体的知识和个体感官数据的特性，动态地将强度和距离图像等多视觉感官数据集成到场景描述中。在第一年，我们发现，利用多视觉信息对一个对象类进行几何建模，对于提取该类对象和推理场景中对象之间的空间关系是有用的。今年，我们对我们的方法进行了扩展，使其可以适用于各种对象类。也就是说，我们开发了一个模型驱动的空间推理系统，它从背景中提取各种对象，并确定这些对象和未探索区域的几何结构。研究结果如下：(1)在立体彩色图像的情况下，基于特征的立体匹配方法得到的距离数据稀疏，不足以进行场景分割。然后，我们利用区域分割的结果，利用在视差空间中提取的平面块的颜色信息来恢复场景结构。结果表明，场景结构基本恢复，但需要更精确的距离信息来确定场景结构的细节。(2)在一对密集的距离数据和强度图像的情况下，根据距离数据估计的平面片的不确定性确定参数模型的参数。结果表明，不确定度取决于两种误差源：概率性误差和系统性误差。我们揭示了它们之间的关系，并将其应用于目标识别问题。

In this project, we proposed a method of scene interpretation which dynamically integrates multi-visual sensory data such as intensity and range images into the scene descriptions according to the intermediate results of the sensory data processings that are obtained by using the knowledge of objects in the scene and the properties of the individual sensory data. In the first year, we found that geometric modeling of one object class by using multi-visual information was useful for extraction of the objects in this class and for reasoning of spatial relationships between objects in the scene. This year, we extended our method so that it could be applicable to various kindsof object classes. That is, we developed a model-driven spatial reasoning system which extracted various kinds of objects from the background and determined the geometric structures of these objects and unexplored regions as well. Research results are as follows:(1) In the case of stereo-color images, the range data obtained from a feature based stereo matching method are sparse, therefore they are not sufficient for segmentation of the scene. Then, we utilized the result of region segmentation by using the color information which was useful to recover the scene structure in terms of planar patches extracted in the disparity space. As a result, the scene structure was roughly recovered, but more accurate range information is needed to determine the details of the scene structure.(2) In the case of a pair of dense range data and an intensity image, we determined the parameters of the parametric model from the uncertainties of planar patches estimated from the range data. It has been shown that the uncertainty depends on two kinds of error sources: probable and systematic errors. We revealed their relationships and applied it to the problem of object recognition.