A goal-driven unsupervised image segmentation method combining graph-based processing and Markov random fields.
Marco TrombiniDavid SolarnaGabriele MoserSilvana G. DellepianePublished in: Pattern Recognit. (2023)
Keyphrases
- segmentation method
- markov random field
- energy function
- image segmentation
- goal driven
- graph cuts
- energy minimization
- segmentation algorithm
- conditional random fields
- mrf model
- region growing
- random fields
- segmented images
- object segmentation
- active contours
- maximum a posteriori
- active contour model
- higher order
- segmentation scheme
- image restoration
- parameter estimation
- image segmentation methods
- shape prior
- superpixels
- watershed segmentation
- shape model
- belief propagation
- image analysis
- pairwise
- multiscale
- low level vision
- segmentation result
- input image
- image intensity
- intensity information
- map estimation
- piecewise constant functions
- computer vision
- software engineering
- image features
- image processing