Back to Search
Start Over
Grouping/degrouping point process, a point process driven by geometrical and topological properties of a partition in regions
- Source :
- Computer Vision and Image Understanding, Computer Vision and Image Understanding, Elsevier, 2011, 115, pp.1324-1339. ⟨10.1016/j.cviu.2011.05.003⟩
- Publication Year :
- 2011
- Publisher :
- Elsevier BV, 2011.
-
Abstract
- International audience; We present a new type of point process, called Grouping/Degrouping Point Process (GDPP), which aim is to select a set of regions of a volume associated to an object or a Region of Interest (ROI). These regions can be obtained from a first low-level region-based segmentation for example. Geometrical and topological information of regions as localisation, adjacency, number of holes ..., are introduced in potentials which computation is done from a population of points which fall in these regions. Thus, a population of points can iteratively converge using Simulated Annealing and therefore select an optimal set of regions. In the paper, we provide the definition of region based potentials and birth and death moves used in a Reversible Jump Monte Carlo Markov Chain method. We also propose special birth and death moves using adjacency of regions. Simulations are done on Positron Emission Tomography. They show the possibility to estimate coherent sets of regions using GDPP as these sets make sense with ROIs defined by a clinician. Implementation of the process implies manipulation of 3D regions. Topological maps have been used as they permit an efficient computation of geometrical and topological properties of 3D regions and they provide a basis that allows further developments.
- Subjects :
- Positron emission tomography
Population
Markov models
Point processes
Topological maps
02 engineering and technology
Topology
01 natural sciences
Simulated annealing
Point process
Reversible Jump MCMC
010104 statistics & probability
[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing
Topological properties
0202 electrical engineering, electronic engineering, information engineering
Segmentation
Point (geometry)
Topological map
0101 mathematics
education
Markov Chain Monte Carlo (MCMC) Methods
Mathematics
education.field_of_study
Geometrical properties
Image segmentation
Reversible-jump Markov chain Monte Carlo
Partition (database)
Exponential family models
Signal Processing
020201 artificial intelligence & image processing
Computer Vision and Pattern Recognition
Segmentation 3D
[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Software
Subjects
Details
- ISSN :
- 10773142 and 1090235X
- Volume :
- 115
- Database :
- OpenAIRE
- Journal :
- Computer Vision and Image Understanding
- Accession number :
- edsair.doi.dedup.....7e1ba2061977923bb81c66beb2b5fc87