Shadow and Specular Removal by Photometric Linearization based on PCA with Outlier Exclusion

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.