Dramatic advances in computer vision and machine learning have all but solved the problem of object detection and recognition using conventional visible band color imagery. However, this is conditioned on the availability of plenty of training data and high-quality images with well resolved objects. This is often not the case for infra-red object detection and recognition. Not only is there a dearth of training data for infra-red (IR) application, but the deployment scenarios often limit the quality of the sensed images. In think talk, we will briefly review the current state of the art in computer vision, and then focus on the problem of IR object detection and recognition with relatively few pixels. We will also discuss recent techniques for training algorithms with comparatively few IR training images, and for optimizing object detection performance in the presence of terrain clutter.
Abhijit Mahalanobis is an associate professor in the Department of Electrical and Computer Engineering at the University of Arizona. His primary research areas are in machine vision and computational imaging. He has over 180 journal and conference publications in these areas. He holds six patents, has 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 M.S. and Ph.D. degrees in 1985 and 1987, respectively. Prior to joining UCF, Abhijit was a faculty member at the University of Central Florida in the Center for Research in Computer Vision (CRCV). He was also a former Senior Fellow at Lockheed Martin in Orlando and worked at Raytheon in Tucson prior to that. 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 was elected Fellow of IEEE in 2015 for his work on the theory of correlation filters.