Entire Reflective Object Surface Structure Understanding

Qinglin Lu, Olivier Laligant, Eric Fauvet and Anastasia Zakharova

Abstract

Reflection from reflective surface has been a long-standing problem for object recognition, it brings negative effects on object’s color, texture and structural information. Because of that, it is not a trivial task to recognize the surface structure affected by the reflection, especially when the object is entirely reflective. Most of the time, reflection is considered as noise. In this paper, we propose a novel method for entire reflective object sub-segmentation by transforming the reflection motion into object surface label. Instead of considering the reflection as noise, our approach takes reflection as an advantage for understanding the surface structure of the entire reflective objects. The experimental results on specular and transparent objects show that the surface structures of the reflective objects can be revealed and the segmentation based on the surface structure outperforms the approaches in literature.

Session

Poster 2

Files

Extended Abstract (PDF, 4M)
Paper (PDF, 6M)

DOI

10.5244/C.29.116
https://dx.doi.org/10.5244/C.29.116

Citation

Bibtex

@inproceedings{BMVC2015_116,
	title={Entire Reflective Object Surface Structure Understanding},
	author={Qinglin Lu and Olivier Laligant and Eric Fauvet and Anastasia Zakharova},
	year={2015},
	month={September},
	pages={116.1-116.11},
	articleno={116},
	numpages={11},
	booktitle={Proceedings of the British Machine Vision Conference (BMVC)},
	publisher={BMVA Press},
	editor={Xianghua Xie, Mark W. Jones, and Gary K. L. Tam},
	doi={10.5244/C.29.116},
	isbn={1-901725-53-7},
	url={https://dx.doi.org/10.5244/C.29.116}
}