@article{brailean1995recursive,
abstract = {In this paper, a recursive model-based algorithm for obtaining the maximum a posteriori (MAP) estimate of the displacement vector field (DVF) from successive image frames of an image sequence is presented. To model the DVF, we develop a nonstationary vector field model called the vector coupled Gauss-Markov (VCGM) model. The VCGM model consists of two levels: an upper level, which is made up of several submodels with various characteristics, and a lower level or line process, which governs the transitions between the submodels. A detailed line process is proposed. The VCGM model is well suited for estimating the DVF since the resulting estimates preserve the boundaries between the differently moving areas in an image sequence. A Kalman type estimator results, followed by a decision criterion for choosing the appropriate line process. Several experiments demonstrate the superior performance of the proposed algorithm with respect to prediction error, interpolation error, and robustness to noise. {\textcopyright} 1995 IEEE},
author = {Brailean, J.C. and Katsaggelos, A.K.},
doi = {10.1109/83.370672},
issn = {10577149},
journal = {IEEE Transactions on Image Processing},
month = {apr},
number = {4},
pages = {416--429},
publisher = {IEEE},
title = {{A recursive nonstationary MAP displacement vector field estimation algorithm}},
url = {http://ieeexplore.ieee.org/document/370672/},
volume = {4},
year = {1995}
}

{"_id":"ywxxz7CHj39bXtNth","bibbaseid":"brailean-katsaggelos-arecursivenonstationarymapdisplacementvectorfieldestimationalgorithm-1995","author_short":["Brailean, J.","Katsaggelos, A."],"bibdata":{"bibtype":"article","type":"article","abstract":"In this paper, a recursive model-based algorithm for obtaining the maximum a posteriori (MAP) estimate of the displacement vector field (DVF) from successive image frames of an image sequence is presented. To model the DVF, we develop a nonstationary vector field model called the vector coupled Gauss-Markov (VCGM) model. The VCGM model consists of two levels: an upper level, which is made up of several submodels with various characteristics, and a lower level or line process, which governs the transitions between the submodels. A detailed line process is proposed. The VCGM model is well suited for estimating the DVF since the resulting estimates preserve the boundaries between the differently moving areas in an image sequence. A Kalman type estimator results, followed by a decision criterion for choosing the appropriate line process. Several experiments demonstrate the superior performance of the proposed algorithm with respect to prediction error, interpolation error, and robustness to noise. © 1995 IEEE","author":[{"propositions":[],"lastnames":["Brailean"],"firstnames":["J.C."],"suffixes":[]},{"propositions":[],"lastnames":["Katsaggelos"],"firstnames":["A.K."],"suffixes":[]}],"doi":"10.1109/83.370672","issn":"10577149","journal":"IEEE Transactions on Image Processing","month":"apr","number":"4","pages":"416–429","publisher":"IEEE","title":"A recursive nonstationary MAP displacement vector field estimation algorithm","url":"http://ieeexplore.ieee.org/document/370672/","volume":"4","year":"1995","bibtex":"@article{brailean1995recursive,\nabstract = {In this paper, a recursive model-based algorithm for obtaining the maximum a posteriori (MAP) estimate of the displacement vector field (DVF) from successive image frames of an image sequence is presented. To model the DVF, we develop a nonstationary vector field model called the vector coupled Gauss-Markov (VCGM) model. The VCGM model consists of two levels: an upper level, which is made up of several submodels with various characteristics, and a lower level or line process, which governs the transitions between the submodels. A detailed line process is proposed. The VCGM model is well suited for estimating the DVF since the resulting estimates preserve the boundaries between the differently moving areas in an image sequence. A Kalman type estimator results, followed by a decision criterion for choosing the appropriate line process. Several experiments demonstrate the superior performance of the proposed algorithm with respect to prediction error, interpolation error, and robustness to noise. {\\textcopyright} 1995 IEEE},\nauthor = {Brailean, J.C. and Katsaggelos, A.K.},\ndoi = {10.1109/83.370672},\nissn = {10577149},\njournal = {IEEE Transactions on Image Processing},\nmonth = {apr},\nnumber = {4},\npages = {416--429},\npublisher = {IEEE},\ntitle = {{A recursive nonstationary MAP displacement vector field estimation algorithm}},\nurl = {http://ieeexplore.ieee.org/document/370672/},\nvolume = {4},\nyear = {1995}\n}\n","author_short":["Brailean, J.","Katsaggelos, A."],"key":"brailean1995recursive","id":"brailean1995recursive","bibbaseid":"brailean-katsaggelos-arecursivenonstationarymapdisplacementvectorfieldestimationalgorithm-1995","role":"author","urls":{"Paper":"http://ieeexplore.ieee.org/document/370672/"},"metadata":{"authorlinks":{}}},"bibtype":"article","biburl":"https://sites.northwestern.edu/ivpl/files/2023/06/IVPL_Updated_publications-1.bib","dataSources":["KTWAakbPXLGfYseXn","ePKPjG8C6yvpk4mEK","37Qfzv6wRptYkoSCL","zFPgsTDAW8aDnb5iN","E6Bth2QB5BYjBMZE7","nbnEjsN7MJhurAK9x","PNQZj6FjzoxxJk4Yi","7FpDWDGJ4KgpDiGfB","bod9ms4MQJHuJgPpp","QR9t5P2cLdJuzhfzK","D8k2SxfC5dKNRFgro","7Dwzbxq93HWrJEhT6","qhF8zxmGcJfvtdeAg","fvDEHD49E2ZRwE3fb","H7crv8NWhZup4d4by","DHqokWsryttGh7pJE","vRJd4wNg9HpoZSMHD","sYxQ6pxFgA59JRhxi","w2WahSbYrbcCKBDsC","XasdXLL99y5rygCmq","3gkSihZQRfAD2KBo3","t5XMbyZbtPBo4wBGS","bEpHM2CtrwW2qE8FP","teJzFLHexaz5AQW5z"],"keywords":[],"search_terms":["recursive","nonstationary","map","displacement","vector","field","estimation","algorithm","brailean","katsaggelos"],"title":"A recursive nonstationary MAP displacement vector field estimation algorithm","year":1995}