Stochastic Model-Based Image Segmentation Using Functional Approximation
Andr
KAUP, Til AACH
Summary :
An unsupervised segmentation technique is presented that is based on a layered statistical model for both region shapes and the region internal texture signals. While the image partition is modelled as a sample of a Gibbs/Markov random field, the texture inside each image segment is described using functional approximation. The segmentation and the unknown parameters are estimated through iterative optimization of an MAP objective function. The obtained tesults are subjectively agreeable and well suited for the requirements of region-oriented transform image coding.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E77-A No.9 pp.1451-1456
- Publication Date
- 1994/09/25
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- Special Section PAPER (Special Section of Papers Selected from the 8th Digital Signal Processing Symposium)
- Category
- Image Processing
Authors
KAUPKeyword
Latest Issue
Copyrights notice of machine-translated contents
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Cite this
Copy
Andr KAUP, Til AACH, "Stochastic Model-Based Image Segmentation Using Functional Approximation" in IEICE TRANSACTIONS on Fundamentals,
vol. E77-A, no. 9, pp. 1451-1456, September 1994, doi: .
Abstract: An unsupervised segmentation technique is presented that is based on a layered statistical model for both region shapes and the region internal texture signals. While the image partition is modelled as a sample of a Gibbs/Markov random field, the texture inside each image segment is described using functional approximation. The segmentation and the unknown parameters are estimated through iterative optimization of an MAP objective function. The obtained tesults are subjectively agreeable and well suited for the requirements of region-oriented transform image coding.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e77-a_9_1451/_p
Copy
@ARTICLE{e77-a_9_1451,
author={Andr KAUP, Til AACH, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Stochastic Model-Based Image Segmentation Using Functional Approximation},
year={1994},
volume={E77-A},
number={9},
pages={1451-1456},
abstract={An unsupervised segmentation technique is presented that is based on a layered statistical model for both region shapes and the region internal texture signals. While the image partition is modelled as a sample of a Gibbs/Markov random field, the texture inside each image segment is described using functional approximation. The segmentation and the unknown parameters are estimated through iterative optimization of an MAP objective function. The obtained tesults are subjectively agreeable and well suited for the requirements of region-oriented transform image coding.},
keywords={},
doi={},
ISSN={},
month={September},}
Copy
TY - JOUR
TI - Stochastic Model-Based Image Segmentation Using Functional Approximation
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1451
EP - 1456
AU - Andr KAUP
AU - Til AACH
PY - 1994
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E77-A
IS - 9
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - September 1994
AB - An unsupervised segmentation technique is presented that is based on a layered statistical model for both region shapes and the region internal texture signals. While the image partition is modelled as a sample of a Gibbs/Markov random field, the texture inside each image segment is described using functional approximation. The segmentation and the unknown parameters are estimated through iterative optimization of an MAP objective function. The obtained tesults are subjectively agreeable and well suited for the requirements of region-oriented transform image coding.
ER -
English
