A Well-Ordered Feature Space Mapping For Sensor Fusion

Gerald M. Flachs; Cynthia L. Beer; David R. Scott

doi:10.1117/12.960490

14 September 1989 A Well-Ordered Feature Space Mapping For Sensor Fusion

Gerald M. Flachs, Cynthia L. Beer, David R. Scott

Proceedings Volume 1100, Sensor Fusion II; (1989) https://doi.org/10.1117/12.960490
Event: SPIE 1989 Technical Symposium on Aerospace Sensing, 1989, Orlando, FL, United States

Abstract

An approach is presented for mapping a multisensor feature space into a space that is well-ordered for vision tasks. A new statistic, the tie statistic (TS), is introduced for measuring the difference between two probability density functions (pdfs). The TS is related to the Kolmogorov-Smirnov statistic (KS) to demonstrate its ability to decide whether or not a sample came from a known pdf. The TS is used to map the measured feature space into a simplified decision space. In the mapping process, the tie statistic is itself a random variable that has a distribution that can be parametrically approximated by the Beta distribution. The tie mapping process is presented and applied to solve two important vision problems.

Citation Download Citation

Gerald M. Flachs, Cynthia L. Beer, and David R. Scott "A Well-Ordered Feature Space Mapping For Sensor Fusion", Proc. SPIE 1100, Sensor Fusion II, (14 September 1989); https://doi.org/10.1117/12.960490

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