Quality metric for automated image registration performance prediction

Kathy Minear; Jay K. Hackett

doi:10.1117/12.394107

27 July 2000 Quality metric for automated image registration performance prediction

Kathy Minear, Jay K. Hackett

Proceedings Volume 4054, Automated Geo-Spatial Image and Data Exploitation; (2000) https://doi.org/10.1117/12.394107
Event: AeroSense 2000, 2000, Orlando, FL, United States

Abstract

Automated image geo-registration of military and defense related imagery can sometimes produce an unsuccessful result due to poor image quality, cloud cover, supporting data errors, and sensor phenomenology. In addition, there are many possible image processing algorithms that further compound the problem of prediction. An accurate mathematical model that is able to incorporate all these parameters and can predict the outcome of a registration event is not feasible. What is proposed here is a probabilistic approach to the problem. A robust quality metric that is able to determine the success of an autonomous registration will be discussed.

Citation Download Citation

Kathy Minear and Jay K. Hackett "Quality metric for automated image registration performance prediction", Proc. SPIE 4054, Automated Geo-Spatial Image and Data Exploitation, (27 July 2000); https://doi.org/10.1117/12.394107

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