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Multi-Dimensional Algorithms for Target Detection in LADAR Imagery

Dr. Abhijit Mahalanobis
Wednesday, March 28, 2007
2:00PM ~ 3:30PM Harris Engineering Center 125

Abstract


LADAR is a unique sensor that measures data which can be manipulated in 3D coordinates. There are numerous advantages of using 3D data, such as the ability to measure range to every pixel so that objects are represented at their true size. Traditional methods for processing LADAR imagery have relied on edge-based techniques and model-matching. Correlation based techniques offer an unique methodology for recognizing objects in LADAR imagery that takes advantage of full volumetric information without requiring the objects to be segmented. The ability to build robust distortion tolerant filters also helps to reduce the computational complexity of the matching algorithms. We present two different types of correlation filters that are specialized for “detection” and “classification” of objects in LADAR data. The first methodology results in a quadratic filter that is optimized to detect targets in LADAR range imagery. We then discuss the concept of a dynamic matching process using Volume Correlation Filters (VCFs) to recognize the detected targets by 3D filtering. Performance results on both synthetic and real LADAR data are presented.

Short Bio


Dr. Abhijit Mahalanobis is a Fellow of the Lockheed Martin Corporation, and is currently the head of the Signal and Image Processing group in the Research and Technology organization in Orlando. His main interests are in multi-sensor automatic target recognition, pattern recognition, and image processing. He has over 120 journal and conference publications in this area. He also holds three patents, co-authored a book on pattern recognition, contributed several book chapters, and edited special issues of several journals. Abhijit completed his B.S. degree with Honors at the University of California, Santa Barbara in 1984. He then joined the Carnegie Mellon University and received the MS. and Ph.D. degrees in 1985 and 1987, respectively. Prior to joining Lockheed Martin, Abhijit worked at Raytheon in Tucson, and was a faculty at the University of Arizona and the University of Maryland.

Abhijit was elected a Fellow of SPIE in 1997, and a Fellow of OSA 2004 for his work on optical pattern recognition and automatic target recognition. He is currently an associate editor for Applied Optics. He was as an associate editor for the journal of the Pattern Recognition Society from 1994-2003. He served on OSA’s Science and Engineering council in the capacity of Pattern Recognition Chair from 2001-2004. He also serves on the organizing committees for the SPIE conferences, and OSA’s annual and topical meetings. Abhijit received the Hughes Business unit Patent Award in 1998. He was recognized as the Innovator of the Year by the State of Arizona in 1999, and was elected to the Raytheon Honors program for distinguished technical contribution and leadership. At Lockheed Martin, he was elected to the rank of Distinguished Member of Technical Staff in 2000, and twice received the Lockheed Martin Technical Excellence award, the Author of the Year award in 2001, and the Inventor of the Year in 2005 for designing novel target recognition systems. In October 2005, he received the prestigious Lockheed Martin NOVA award, the Corporation’s highest honor, for putting together a National Team and a winning strategy in the FCS competition. Most recently, Abhijit was recognized as the 2006 Scientist of the Year by Science Spectrum Magazine, a publication of the Career Communication Group, Inc.

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