Two-Stage Image Segmentation by Adaptive Thresholding and Gradient Watershed

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The 2nd Canadian Conference on Computer and Robot Vision (CRV'05)

The University of Victoria, Victoria, British Columbia, Canada

May 09-May 11

ISBN: 0-7695-2319-6

	ASCII Text	x
	Ming Hong Pi, Hong Zhang, "Two-Stage Image Segmentation by Adaptive Thresholding and Gradient Watershed," Computer and Robot Vision, Canadian Conference, pp. 57-64, The 2nd Canadian Conference on Computer and Robot Vision (CRV'05), 2005.

	BibTex	x
	@article{ 10.1109/CRV.2005.82, author = {Ming Hong Pi and Hong Zhang}, title = {Two-Stage Image Segmentation by Adaptive Thresholding and Gradient Watershed}, journal ={Computer and Robot Vision, Canadian Conference}, volume = {0}, year = {2005}, isbn = {0-7695-2319-6}, pages = {57-64}, doi = {http://doi.ieeecomputersociety.org/10.1109/CRV.2005.82}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Computer and Robot Vision, Canadian Conference TI - Two-Stage Image Segmentation by Adaptive Thresholding and Gradient Watershed SN - 0-7695-2319-6 SP57 EP64 A1 - Ming Hong Pi, A1 - Hong Zhang, PY - 2005 KW - null VL - 0 JA - Computer and Robot Vision, Canadian Conference ER -

Ming Hong Pi, University of Alberta, Edmonton, AB, Canada

Hong Zhang, University of Alberta, Edmonton, AB, Canada

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CRV.2005.82

Many images contain the coarse pieces and fine particles. Hence it is often important to segment the coarse pieces and fine particles using different segmentation techniques. In this paper, we combine adaptive thresholding and watershed transform in a two-stage image segmentation method, which has been used for oil-sand size analysis. We also present an image-retrieval-like approach to speed up size estimation of the fine particles.

Citation:

Ming Hong Pi, Hong Zhang, "Two-Stage Image Segmentation by Adaptive Thresholding and Gradient Watershed," crv, pp.57-64, The 2nd Canadian Conference on Computer and Robot Vision (CRV'05), 2005

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