Modeling and Recognition of Arbitrary Curved Objects from Image Contours

今年是连续三年获奖的第一年。 的 研究致力于下一代基于模型的视觉 系统，这应该证明在单目识别 从大型数据库中选择复杂曲面对象的图像数据 的模型。 这将需要在形状方面取得重大进展 表示、建模和匹配。这项研究的重点是一个 一种新的形状表示方法及其在自动建模中的应用 从距离和视频图像集合到对象的构造 识别，包括方面图的建设，姿态计算， 并编入模型数据库。 在本研究中， 任意平滑的3D形状由一组 特征表面曲线具有的特性是， 确定图像轮廓的结构及其灾难性 变化，并且有一些自然的对应关系， 这些曲线和单眼轮廓特征，例如拐点和 双切线

This is the first year of a three-year continuing award. The research addresses the next generation of model-based vision systems, which should demonstrate the recognition in monocular image data of complex curved objects selected from a large database of models. This will require major progress in the areas of shape representation, modeling, and matching. This research focuses on a novel shape representation and its application to automatic model construction from sets of range and video images and to object recognition, including aspect graph construction, pose computation, and indexing into a database of models. In this research, arbitrary smooth 3D shapes are represented by a set of characteristic surface curves having the properties that they determine the structure of the image contours and its catastrophic changes, and there is a natural correspondence between some of these curves and monocular contour features such as inflections and bitangents.