Authors:
Maxim Mikhnevich
and
Denis Laurendeau
Affiliation:
Laval University, Canada
Keyword(s):
Shape From Silhouette, SFS, Multi-view Image Segmentation, Multi-lighting, Visual Hull, VH, Graph Cuts.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Computer Vision, Visualization and Computer Graphics
;
Geometry and Modeling
;
Image and Video Analysis
;
Image-Based Modeling
;
Pattern Recognition
;
Segmentation and Grouping
;
Software Engineering
Abstract:
This paper presents an image segmentation approach for obtaining a set of silhouettes along with the Visual Hull of an object observed from multiple viewpoints. The proposed approach can deal with mostly any type of appearance characteristics such as textured or textureless, shiny or lambertian surface reflectance, opaque or transparent objects. Compared to more classical methods for silhouette extraction from multiple views, for which certain assumptions are made on the object or scene, neither the background nor the object’s appearance properties are modeled. The only assumption is the constancy of the unknown background at a given camera viewpoint while the object is under motion. The principal idea of the method is the estimation of the temporal evolution of each pixel over time which leads to the ability to estimate the background likelihood. Furthermore, the object is captured under different lighting conditions in order to cope with shadows. All the information from the space,
time and lighting domains is merged based on a MRF framework and the constructed energy function is minimized via graph cuts.
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