COMPUTER VISION
Funded Projects

The Research Foundation of CUNY highlighted our NSF-sponsored project at its 2010 annual report.



NSF CCF MSC (2009-2013)

                                                                                                          
($380,000)

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


 Description

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. For more information please go to link.

People

Co-PI

Ioannis Stamos



Graduate Students


Hongzhong Zhang, Thomas Flynn



Undergraduate Students


Mansen Lin
Anh Dinh



Selected Relevant Publications

For a list of all publications click here

Related Internal Grants


NSF DMS IGMS (2009-2011)

                                                                                                             
($100,000)

Sequential Detection and Classification in 3D Computer Vision

 Description

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. For more information please go to link.

People

Undergraduate Students
 
Artur Sahakyan
(Brooklyn College alumnus, currently employed at IBM's dispatching division).




Selected Relevant Publications

For a list of all publications click here