Proceedings PaperPixel-Level Sensor Fusion For Improved Object Recognition
|Format||Member Price||Non-Member Price|
A method is proposed to exploit simultaneous, co-registered FLIR and TV images of isolated objects against relatively bland backgrounds to improve recognition of those objects. The method uses edges extracted from the TV imagery to segment objects in the FLIR imagery. A binary tree classifier is shown to perform significantly better with objects defined in this manner than with objects extracted separately from the FLIR or TV images, or with a feature level fusion scheme which combines features of separately extracted objects. The structure of the tree indicates that the cross-segmented objects are simply ordered in feature space. An argument is presented that this sensor fusion scheme is natural in terms of the known or-ganization of neural vision systems. Generalizations to other sensor types and fusion schemes should be considered, since it has been shown that co-registered imagery can be exploited to improve recognition at no additional computational cost.