Authors:
Takahiro Mori
1
;
Shinsaku Hiura
2
and
Kosuke Sato
1
Affiliations:
1
Osaka University, Japan
;
2
Hiroshima City University, Japan
Keyword(s):
Photometric Linearization, Reflection Components, Shadow Removal.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Image and Video Analysis
;
Image Enhancement and Restoration
;
Image Formation and Preprocessing
;
Segmentation and Grouping
Abstract:
The photometric linearization method converts real images, including various photometric components such as diffuse reflection, specular reflection, attached and cast shadow, into images with diffuse reflection components only, which satisfies the Lambertian law. The conventional method(Mukaigawa et al., 2007) based on a random sampling framework successfully achieves the task; however, it contains two problems. The first is that the three basis images selected from the input images by the user seriously affect the linearization result quality. The other is that it takes a long time to process the enormous number of random samples needed to find the correct answer probabilistically. We therefore propose a novel algorithm using the PCA (principal component analysis) method with outlier exclusion. We used knowledge of photometric phenomena for the outlier detection and the experiments show that the method provides fast and precise linearization results.