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。开发的方法可以有许多应用，例如创建全景图的图像拼接，从照片集合中重建3D模型，自动驾驶汽车和机器人的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项基础研究工作，将研究与教育结合起来。本研究利用极值统计理论为分类器开发了理论基础的置信度度量。特别是，该项目研究了最近邻分类器的置信度度量，这是一种在许多计算机视觉和其他应用中广泛使用的分类器。该项目研究了两个计算机视觉任务：鲁棒估计和场景压缩的置信度度量。研究重点是使用开发的置信度度量来自适应地使用非最小样本进行假设生成和基于ransac的估计器的快速收敛。此外，该项目开发了一种基于凸优化方法的场景压缩算法，该算法的目标函数考虑了空间覆盖率和视觉独特性。该项目研究了使用从这项工作中得到的置信度度量来增强视觉独特性的方法，并将它们应用于不同的应用程序，以证明估计图像特征对应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.