Quadtree-based eigendecomposition for pose estimation in the presence of occlusion and background clutter

Eigendecomposition-based techniques are popular for a number of computer vision problems, e.g., object and pose estimation, because they are purely appearance based and they require few on-line computations. Unfortunately, they also typically require an unobstructed view of the object whose pose is being detected. The presence of occlusion and background clutter precludes the use of the normalizations that are typically applied and significantly alters the appearance of the object under detection. This work presents an algorithm that is based on applying eigendecomposition to a quadtree representation of the image dataset used to describe the appearance of an object. This allows decisions concerning the pose of an object to be based on only those portions of the image in which the algorithm has determined that the object is not occluded. The accuracy and computational efficiency of the proposed approach is evaluated on 16 different objects with up to 50% of the object being occluded and on images of ships in a dockyard.

Anthony A. MaciejewskiEmail:

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Chu-Yin Chang   received the B.S. degree in mechanical engineering from National Central University, Chung-Li, Taiwan, ROC, in 1988, the M.S. degree in electrical engineering from the University of California, Davis, in 1993, and the Ph.D. degree in electrical and computer engineering from Purdue University, West Lafayette, in 1999. From 1999--2002, he was a Machine Vision Systems Engineer with Semiconductor Technologies and Instruments, Inc., Plano, TX. He is currently the Vice President of Energid Technologies, Cambridge, MA, USA. His research interests include computer vision, computer graphics, and robotics. Anthony A. Maciejewski   received the BSEE, M.S., and Ph.D. degrees from Ohio State University in 1982, 1984, and 1987. From 1988 to 2001, he was a professor of Electrical and Computer Engineering at Purdue University, West Lafayette. He is currently the Department Head of Electrical and Computer Engineering at Colorado State University. He is a Fellow of the IEEE. A complete vita is available at: Venkataramanan Balakrishnan   is Professor and Associate Head of Electrical and Computer Engineering at Purdue University, West Lafayette, Indiana. He received the B.Tech degree in electronics and communication and the President of India Gold Medal from the Indian Institute of Technology, Madras, in 1985. He then attended Stanford University, where he received the M.S. degree in statistics and the Ph.D. degree in electrical engineering in 1992. He joined Purdue University in 1994 after post-doctoral research at Stanford, CalTech and the University of Maryland. His primary research interests are in convex optimization and large-scale numerical algebra, applied to engineering problems. Rodney G. Roberts   received B.S. degrees in Electrical Engineering and Mathematics from Rose-Hulman Institute of Technology in 1987 and an MSEE and Ph.D. in Electrical Engineering from Purdue University in 1988 and 1992, respectively. From 1992 until 1994, he was a National Research Council Fellow at Wright Patterson Air Force Base in Dayton, Ohio. Since 1994 he has been at the Florida A&M University---Florida State University College of Engineering where he is currently a Professor of Electrical and Computer Engineering. His research interests are in the areas of robotics and image processing. Kishor Saitwal   received the Bachelor of Engineering (B.E.) degree in Instrumentation and Controls from Vishwakarma Institute of Technology, Pune, India, in 1998. He was ranked Third in the Pune University and was recipient of National Talent Search scholarship. He received the M.S. and Ph.D. degrees from the Electrical and Computer Engineering department, Colorado State University, Fort Collins, in 2001 and 2006, respectively. He is currently with Behavioral Recognition Systems, Inc. performing research in computer aided video surveillance systems. His research interests include image/video processing, computer vision, and robotics.