We propose a fixed-lag finite-impulse-response (FIR) smoother for a discrete-time state space model. The proposed FIR smoother estimates the state at the fixed-lag time using measured output samples on the recent finite time horizon so that the variance of the estimation error is minimized. The minimum variance FIR (MVFIR) smoother is unbiased and independent of any a priori information of the state on the horizon. A numerical example shows that the proposed MVFIR smoother has better performance than the fixed-lag Kalman smoother based on the infinite impulse response structure when transitory modeling uncertainties exist.

This paper proposes two Hausdorff distance (HD) matching algorithms, in which robust HD measures are implemented in pyramidal structures. By computer simulations, the matching performance of the conventional HD measures and the proposed robust HD matching algorithms using pyramidal structures is compared, with real images which are degraded by noise and occlusions.

A Hausdorff distance (HD) is one of commonly used measures for object matching. This work analyzes the conventional HD measures and proposes two robust HD measures based on m-estimation and least trimmed square (LTS) which are more efficient than the conventional HD measures. By computer simulation, the matching performance of the conventional and proposed HD measures is compared with synthetic and real images.

A Hausdorff distance (HD) is one of commonly used measures for object matching. It calculates the distance between two point sets of edges in two-dimensional binary images without establishing correspondences. This paper analyzes the conventional HD measures based on robust statistics, and proposes robust HD measures based on M-estimation, least trimmed square (LTS), and /spl alpha/-trimmed mean methods. The matching performance by the conventional and proposed HD measures is compared with synthetic and real images.