Sequential Detection and Classification in 3D Computer Vision

序贯观测统计行为的最快检测和分类问题是一个经典问题，在工程学、经济学和流行病学中有着广泛的应用。在当今快速发展的技术中，新的应用领域不断涌现。特别是，城市场景的自动三维图像重建和分类是一个复杂的问题，它仍然挑战着计算机科学家。传统上，它是通过激光扫描仪获取数据来治疗的，激光扫描仪可以产生高分辨率的图像，但速度可能非常慢。因此，必须只将激光扫描集中到感兴趣的区域，这将导致对感兴趣的区域做出快速决策。这可以节省大量的时间和成本，同时仍能生成高分辨率的3D图像。该项目的目标是开发和实现处理和分析3D激光距离数据的实时算法。通过巧妙、创新地选择测量模型，降低了数据的高维性质。然后，通过在线参数和非参数分类和检测技术来处理相互依赖的观察流。最后，使用新的统计模型来捕捉城市场景中的障碍物。这为使用高分辨率激光数据的快速和有效的3D图像分类问题提供了系统的处理。目前的提案预计将为传统的最快检测和分类技术开发和建立一系列新的可能性。该项目承诺将经典序贯统计学的应用扩展到3D计算机视觉领域，从而为创建由计算机科学家和统计学家组成的具有明确共同目标的新的协同跨学科研究和教育团队铺平道路。将这两个社区的专业知识结合起来，预计将带来更复杂的技术和软件，用于获取和处理来自激光扫描仪的3D数据。该项目为本科生创造了一个鼓舞人心的研究环境，激励他们在数学和计算机科学的跨学科前沿寻求深造。它还提供了一个框架，通过开发跨学科课程来创新布鲁克林学院的课程，布鲁克林学院是一所为少数族裔服务的机构。

The problem of quickest detection and classification in the statistical behavior of sequential observations is a classical one, with numerous applications in engineering, economics and epidemiology. In today's fast-growing technologies new areas of applications constantly emerge. In particular, the automatic 3D image reconstruction and classification of urban scenes is a problem whose complexity still challenges computer scientists. It has traditionally been treated through the acquisition of data using laser-scanners, which produce high-resolution images, but can be very slow. It is thus essential to concentrate laser scanning only to the areas of interest, which leads to fast decision-making about areas of interest. This can save significant time and cost, while still producing high-resolution 3D images. The goal of this project is to develop and implement real-time algorithms for processing and analyzing 3D laser range data. The high-dimensional nature of the data is reduced by a clever innovative selection of a measurement model. Interdependent streams of observations are then processed by on-line parametric and non-parametric classification and detection techniques. And finally, new statistical models are used to capture obstacles in urban scenes. This provides a systematic treatment of the problems of fast and efficient 3D image classification using high-resolution laser data. The current proposal is expected to develop and establish a new line of possibilities for the traditional quickest detection and classification techniques. This project promises to expand the applications of classical sequential statistics to the area of 3D Computer vision, thus carving the road for the creation of new synergistic interdisciplinary research and education teams of computer scientists and statisticians with well-defined common goals. Combining the expertise of these two communities is expected to lead to more sophisticated technology and software for the acquisition and processing of 3D data that comes from laser scanners. This project creates a stimulating research environment for undergraduate students, motivating them to seek advanced studies on the interdisciplinary frontier of mathematics and computer science. It also provides a framework for innovating the curriculum at Brooklyn College, a minority-serving institution, through the development of interdisciplinary courses.