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

Cuny Hunter College, New York NY

Investigators

Abstract

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.