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.
