A generalized Gaussian image model for edge-preserving MAP estimation

C. Bouman; K. Sauer

doi:10.1109/83.236536

A generalized Gaussian image model for edge-preserving MAP estimation

IEEE Transactions on Image Processing ◽

10.1109/83.236536 ◽

1993 ◽

Vol 2 (3) ◽

pp. 296-310 ◽

Cited By ~ 574

Author(s):

C. Bouman ◽

K. Sauer

Keyword(s):

Map Estimation ◽

Image Model ◽

Edge Preserving ◽

Gaussian Image

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A filtering approach to edge preserving map estimation of images

2004 International Conference on Image Processing, 2004. ICIP '04. ◽

10.1109/icip.2004.1418751 ◽

2005 ◽

Cited By ~ 2

Author(s):

D. Humphrey ◽

D. Taubman

Keyword(s):

Map Estimation ◽

Edge Preserving ◽

Filtering Approach

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Accurate Disparity Map Estimation Based on Edge-preserving Filter

2018 International Conference on Smart Computing and Electronic Enterprise (ICSCEE) ◽

10.1109/icscee.2018.8538360 ◽

2018 ◽

Author(s):

Rostam Affendi Hamzah ◽

M. Saad Hamid ◽

A. F. Kadmin ◽

S. Fakhar Abd Gani ◽

S. Salam ◽

...

Keyword(s):

Map Estimation ◽

Disparity Map ◽

Edge Preserving

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Edge-preserving MAP estimation of motion vector fields in noisy low-dose x-ray image sequences

10.1117/12.323215 ◽

1998 ◽

Author(s):

Til Aach

Keyword(s):

Vector Fields ◽

Low Dose ◽

Motion Vector ◽

Image Sequences ◽

Map Estimation ◽

Edge Preserving ◽

X Ray

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A Filtering Approach to Edge Preserving MAP Estimation of Images

IEEE Transactions on Image Processing ◽

10.1109/tip.2010.2092432 ◽

2011 ◽

Vol 20 (5) ◽

pp. 1234-1248 ◽

Cited By ~ 6

Author(s):

David Humphrey ◽

David Taubman

Keyword(s):

Map Estimation ◽

Edge Preserving ◽

Filtering Approach

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An Iterative Method for Edge-Preserving MAP Estimation When Data-Noise Is Poisson

SIAM Journal on Scientific Computing ◽

10.1137/080726884 ◽

2010 ◽

Vol 32 (1) ◽

pp. 171-185 ◽

Cited By ~ 6

Author(s):

Johnathan M. Bardsley ◽

John Goldes

Keyword(s):

Iterative Method ◽

Map Estimation ◽

Edge Preserving ◽

Data Noise

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Edge-preserving disparity map estimation from stereo videos for bokeh synthesis

2017 IEEE International Conference on Multimedia and Expo (ICME) ◽

10.1109/icme.2017.8019534 ◽

2017 ◽

Author(s):

Wei-Lun Lan ◽

Shih-Hsuan Yao ◽

Shang-Hong Lai

Keyword(s):

Map Estimation ◽

Disparity Map ◽

Edge Preserving

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Computational Micrograph Registration with Sieve Processes

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100164660 ◽

1996 ◽

Vol 54 ◽

pp. 440-441

Author(s):

P.J. Phillips ◽

J. Huang ◽

S. M. Dunn

Keyword(s):

Planar Graph ◽

Efficient Algorithm ◽

Spatial Organization ◽

Spatial Arrangement ◽

Stereo Image ◽

Image Model ◽

Geometric Invariants ◽

Point Set

In this paper we present an efficient algorithm for automatically finding the correspondence between pairs of stereo micrographs, the key step in forming a stereo image. The computation burden in this problem is solving for the optimal mapping and transformation between the two micrographs. In this paper, we present a sieve algorithm for efficiently estimating the transformation and correspondence.In a sieve algorithm, a sequence of stages gradually reduce the number of transformations and correspondences that need to be examined, i.e., the analogy of sieving through the set of mappings with gradually finer meshes until the answer is found. The set of sieves is derived from an image model, here a planar graph that encodes the spatial organization of the features. In the sieve algorithm, the graph represents the spatial arrangement of objects in the image. The algorithm for finding the correspondence restricts its attention to the graph, with the correspondence being found by a combination of graph matchings, point set matching and geometric invariants.

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