Regularization looks for an interpolating function which is close to the data and also "smooth". This function is obtained by minimizing an error functional which is the weighted sum of a "fidelity term"and a "smoothness term". However, using only one set of weights does not guarantee that this function will be the MAP estimate. One has to consider all possible weights in order to find the MAP function. Also, using only one combination of weights makes the algorithm very sensitive to the data. The solution suggested here is through the Bayesian approach: A probability distribution over all weights is constructed and all weights are considered when reconstructing the function or computing the expectation of a linear functional on the function space.
|Title of host publication||Proceedings - International Conference on Pattern Recognition|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||5|
|State||Published - 1994|
|Event||12th IAPR International Conference on Pattern Recognition - Conference C: Signal Processing - Conference D: Parallel Computing, ICPR 1994 - Jerusalem, Israel|
Duration: 9 Oct 1994 → 13 Oct 1994
|Name||Proceedings - International Conference on Pattern Recognition|
|Conference||12th IAPR International Conference on Pattern Recognition - Conference C: Signal Processing - Conference D: Parallel Computing, ICPR 1994|
|Period||9/10/94 → 13/10/94|
Bibliographical noteFunding Information:
This research was sponsored by ARPA through the U.S. Office of Naval Research under grant N00014-93-1-1202, R&T Project Code 4424341-01.
© 1994 IEEE.
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition