CRII: RI: Matching Image Features with Correctness Predictions

该项目开发了测量图像特征对应正确性的方法，这有助于系统识别好的和坏的对应。该项目研究了鲁棒的估计器和3D点云压缩算法，这些算法利用图像特征对应的正确性度量来提高其有效性。所开发的方法可以有许多应用，例如用于创建照片的图像拼接，从照片集重建3D模型，自动驾驶汽车和机器人中基于视觉的导航，以及增强现实。该项目通过开发3D计算机视觉课程并让本科生和研究生参与这一基础研究工作，将研究与教育相结合。本研究发展了基于理论的置信度的分类器使用统计极值理论。特别是，该项目研究了最近邻分类器的置信度，这是一种广泛用于许多计算机视觉和其他应用的分类器。本计画研究两个电脑视觉任务中的信心测度：强健估计与景像压缩。研究的重点是使用发达国家的信心措施，使自适应使用非最小样本的假设生成和快速收敛的RANSAC为基础的估计。在此基础上，提出了一种基于凸优化方法的场景压缩算法，其目标函数考虑了空间覆盖率和视觉显著性。该项目研究了如何使用来自这项工作的置信度来增强视觉独特性，并将其应用于不同的应用程序，以证明估计图像特征对应关系正确性的鲁棒性和效率。

The project develops methods for measuring the correctness of image-feature correspondences, which helps systems to identify good and bad correspondences. The project investigates robust estimators and 3D-point cloud compression algorithms that leverage the correctness measures of image-feature correspondences to increase their effectiveness. The developed methods can have many applications, such as image stitching for the creation of panoramas, 3D model reconstruction from photo collections, vision-based navigation in self-driving cars and robots, and augmented reality. The project integrates research with education by developing 3D computer vision courses and involving undergraduate and graduate students in this fundamental research effort. This research develops theoretically grounded confidence measures for classifiers using the statistical theory of extreme values. In particular, the project investigates confidence measures for the nearest neighbor classifier, a widely used classifier in many computer vision and other applications. The project investigates the confidence measures in two computer vison tasks: robust estimation and scene compression. The research focuses on using the developed confidence measures to enable the adaptive use of non-minimal samples for hypotheses generation and fast convergence in RANSAC-based estimators. Furthermore, the project develops a scene-compression algorithm based on a convex optimization method, of which objective function considers spatial coverage and visual distinctiveness. The project studies ways to enforce visual distinctiveness using confidence measures derived from this work and applies them to different applications to demonstrate the robustness and efficiency of estimating the correctness of image-feature correspondences.