MSC: Sequential Classification and Detection via Markov Models in Point Clouds of Urban Scenes

MSC：通过城市场景点云中的马尔可夫模型进行顺序分类和检测

• 批准号: 0916452
• 负责人: Ioannis Stamos
• 金额: $ 38万
• 依托单位: CUNY Hunter College
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0916452&HistoricalAwards=false
• 关键词: MSC; Sequential; Classification; Detection; via

One of the most important problems in 3D computer vision and graphics is the automatic scene reconstruction from 2D and 3D images. Recently, the reconstruction of complex urban scenes has attracted significant interest. This is because accurate 3D city models are paramount in the further development of a variety of fields such as urban planning, architecture, and archeology. They are also very important for applications commonly used in everyday life such as street map visualization and navigation, as well as in the film and construction industries. Automatic 3D image reconstruction and classification of urban scenes, though, is a problem whose complexity still challenges today?s research community. 3D reconstruction of city models is achieved through data acquisition using a variety of devices such as laser scanners and regular cameras. While laser scanners provide dense, detailed and accurate 3D points, they suffer from slow speed which dramatically increases the cost of acquisition.This project, which is led by a multi-disciplinary team, combines expertise from computer vision, mathematical modeling and statistics to address this limitation. The goal of this work is to develop and implement real-time detection and classification techniques applied to streams of 3D point-cloud data. This allows focused acquisition of objects of interest (e.g. facades, etc.) and thus increases speed and reduces power consumption. It also aids high-level recognition processes in detecting and classifying objects in urban scenes. Reduction of the high-dimensional nature of the data is achieved by the clever innovative selection of a measurement model. A new formulation using hidden Markov models is used to capture the complexity of urban scenes. The real-time algorithms are tested on point-cloud data acquired in a real urban setting by the latest generation of laser range scanning technology.On the education front, this project provides a stimulating research environment for both undergraduate and graduate students on the interdisciplinary frontier of mathematics and computer science. It also provides a framework for innovating the curriculum at both Brooklyn, a minority-serving institution, and Hunter Colleges through the development of interdisciplinary courses.

从二维和三维图像中自动重建场景是三维计算机视觉和图形学中的一个重要问题。近年来，复杂城市场景的重建引起了人们极大的兴趣。这是因为精确的3D城市模型在城市规划、建筑和考古等各个领域的进一步发展中至关重要。它们对于日常生活中常用的应用也非常重要，例如街道地图可视化和导航，以及电影和建筑行业。然而，城市场景的自动3D图像重建和分类仍然是一个复杂的问题。的研究社区。城市模型的3D重建是通过使用各种设备（如激光扫描仪和常规相机）进行数据采集来实现的。虽然激光扫描仪可以提供密集、详细和准确的3D点，但它们的速度很慢，这大大增加了采集成本。该项目由多学科团队领导，结合了计算机视觉、数学建模和统计学的专业知识，以解决这一限制。 这项工作的目标是开发和实现应用于3D点云数据流的实时检测和分类技术。这允许对感兴趣的对象（例如，立面等）的集中采集。从而提高速度并降低功耗。 它还有助于在城市场景中检测和分类对象的高级识别过程。数据的高维性质的减少是通过测量模型的巧妙创新选择来实现的。 一个新的配方使用隐马尔可夫模型是用来捕捉城市场景的复杂性。在最新一代的激光范围扫描技术在真实的城市环境中获得的点云数据上测试了实时算法。在教育方面，该项目为数学和计算机科学跨学科前沿的本科生和研究生提供了一个激励性的研究环境。它还提供了一个框架，通过跨学科课程的发展，在布鲁克林，一个少数民族服务机构，和亨特学院创新的课程。