Motion estimation on polarimetric IR data sequences

摘要

This paper presents an algorithm to estimate motion vectors from Polarimetric IR data sequences. In the proposed algorith, based on the I, P, and PSI frames of a PIR sequence, motion estimation o is formulated as a problem of obtaining the Maximum A Posteriori in the Markov Random Field (MAP-MRF). An optimization method based on the Mean Field Theory (MFT) is chosen to carry out the MAP search. The estimation of motion vectors is modeled by two MRF's, namely, motion vector field and unpredictable field. A truncation function is employed to bahdle the discontinity between motion vectors on neighboring sites. In this algorithm, a "double thresehold" step is first applied to partition the sites into three regions, whereby the ensuing MFT-based step for each MRF is performed on one or two of the three regions. With this algorithm, no significant difference exists between the block-based and pixel-based MAP searches any more. Consequently, a good compromise between precision and efficiency can be obtained with ease.