REU: Implicit Polynomial Technology for Computer Vision and Image/Video Databases

本研究的目标是将新的隐式多项式二维曲线和三维表面物体识别概念引入计算机视觉和图像处理研究人员的通用状态，并开发计算快速的基于形状的二维图像和三维图像数据库索引。所采用的方法包括新的拟合概念和算法、新的不变量类型、隐式多项式二维曲线和三维曲面单计算位姿估计的新概念和算法，以及通过将丰富的二维曲线集或丰富的三维表面斑块集与自动提取并存储在图像数据库中的曲面集进行匹配来进行索引。这项工作将评估隐式多项式方法在2D和3D数据中建模和推断物体形状的价值。这项研究还将为研究界的应用提供有用的工具，并将提供对这项技术的优点和缺点的理解。在科学和商业领域，搜索和索引图像数据库是一项迅速发展的活动。这个项目将提供一个几何形状为基础的索引精度和速度的理解和自动化的数据库准备可能与新的隐式多项式方法。

The goals of this research are to bring new implicit polynomial 2D curve and 3D surface object recognition concepts to a state of general usefulness to researchers in computer vision and image processing, and to develop computationally-fast shape-based indexing into 2D image and 3D image databases. The approaches to be used include new fitting concepts and algorithms, new types of invariants, new concepts and algorithms for single-computation pose estimation for implicit polynomial 2D curves and 3D surfaces, and indexing by matching rich sets of 2D curvelets or rich sets of 3D surface patchlets with those that have been autonomously extracted and stored in the image databases. This work will assess the value of implicit polynomial methods for modeling and making inferences about object shape in 2D and 3D data. The research will also produce useful tools for the research community for applications, and will provide an understanding of the strengths and weaknesses of this technology. Searching and indexing into image databases is a rapidly growing activity in science and commerce. This project will provide an understanding of the geometric shape-based indexing accuracy and speed and the automation of database preparation possible with the new implicit polynomial methodology.