A Well-Ordered Feature Space Mapping For Sensor Fusion

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.