Detection of Occlusion Edges from the Derivatives of Weather Degraded Images

The University of Victoria, Victoria, British Columbia, Canada May 09-May 11

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http://doi.ieeecomputersociety.org/10.1109/CRV.2005.35

The 2nd Canadian Conference on Comput ...

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	Daniel L?vesque, Fran?ois Desch?nes, "Detection of Occlusion Edges from the Derivatives of Weather Degraded Images," Computer and Robot Vision, Canadian Conference, pp. 114-120, The 2nd Canadian Conference on Computer and Robot Vision (CRV'05), 2005.

	BibTex	x
	@article{ 10.1109/CRV.2005.35, author = {Daniel L?vesque and Fran?ois Desch?nes}, title = {Detection of Occlusion Edges from the Derivatives of Weather Degraded Images}, journal ={Computer and Robot Vision, Canadian Conference}, volume = {0}, year = {2005}, isbn = {0-7695-2319-6}, pages = {114-120}, doi = {http://doi.ieeecomputersociety.org/10.1109/CRV.2005.35}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Computer and Robot Vision, Canadian Conference TI - Detection of Occlusion Edges from the Derivatives of Weather Degraded Images SN - 0-7695-2319-6 SP114 EP120 A1 - Daniel L?vesque, A1 - Fran?ois Desch?nes, PY - 2005 KW - 3D structure KW - occlusion KW - light scattering KW - weather degraded image VL - 0 JA - Computer and Robot Vision, Canadian Conference ER -

Daniel L?vesque, Universit? de Sherbrooke, Canada

Fran?ois Desch?nes, Universit? de Sherbrooke, Canada

Degradation of images of outdoor scenes caused by varying conditions of visibility can be exploited in order to get information on the scene. We propose two new methods for detecting occlusion edges between textured areas of a scene. These methods are based on the use of partial derivatives of two images acquired under different conditions of visibility. They were validated on images of both synthetic and real scenes.

Index Terms:

3D structure, occlusion, light scattering, weather degraded image

Citation:

Daniel L?vesque, Fran?ois Desch?nes, "Detection of Occlusion Edges from the Derivatives of Weather Degraded Images," crv, pp.114-120, The 2nd Canadian Conference on Computer and Robot Vision (CRV'05), 2005

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