A note on the gradient of a multi-image
Computer Vision, Graphics, and Image Processing - Lectures notes in computer science, Vol. 201 (G. Goos and J. Hartmanis, Eds.)
Edge detection in multispectral images
CVGIP: Graphical Models and Image Processing
Bayesian inference for multiband image segmentation via model-based cluster trees
Image and Vision Computing
Multiband segmentation based on a hierarchical Markov model
Pattern Recognition
Anisotropic diffusion of multivalued images with applications to color filtering
IEEE Transactions on Image Processing
Hybrid image segmentation using watersheds and fast region merging
IEEE Transactions on Image Processing
Image segmentation and analysis via multiscale gradient watershed hierarchies
IEEE Transactions on Image Processing
Multiscale gradient watersheds of color images
IEEE Transactions on Image Processing
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In this paper, we propose a framework for the segmentation of multicomponent images. The specific framework we aim at contains different steps in which all components of the multicomponent image are processed simultaneously, accounting for the correlation between the image components. The framework contains the following steps: a) to initiate, a pixel-based, spectral clustering procedure is applied. b) to include spatial information, a model-based region-merging technique is used, applying a multinormal model for the coefficient regions, and estimating the model parameters using Maximum Likelihood principles; c)the model allows to treat noise that might be present efficiently; d) a multiscale version of the framework is established by repeating the same procedure at different resolution levels of the original image. e) Then, a link between the different levels is established by constructing a hierarchy between the regions at different levels. In this work, we will demonstrate the performance of the framework for segmentation purposes. The procedure is performed on color images and multispectral remote sensing images.