title: Separating reflection components based on chromaticity and noise analysis.
abstract: Many algorithms in computer vision assume diffuse only reflections and deem specular reflections to be outliers. However, in the real world, the presence of specular reflections is inevitable since there are many dielectric inhomogeneous objects which have both diffuse and specular reflections. To resolve this problem, we present a method to separate the two reflection components. The method is principally based on the distribution of specular and diffuse points in a two-dimensional maximum chromaticity-intensity space. We found that, by utilizing the space and known illumination color, the problem of reflection component separation can be simplified into the problem of identifying diffuse maximum chromaticity. To be able to identify the diffuse maximum chromaticity correctly, an analysis of the noise is required since most real images suffer from it. Unlike existing methods, the proposed method can separate the reflection components robustly for any kind of surface roughness and light direction.
authors.affiliation: Department of Computer Science, The University of Tokyo, 3rd Dept Ikeuchi Laboratory, 4-6-1 Komba, Meguro-ku, Tokyo 153-8505, Japan. robby@cvl.iis.u-tokyo.ac.jp
authors.forename: Robby T
authors.initials: RT
authors.lastname: Tan
authors.forename: Ko
authors.initials: K
authors.lastname: Nishino
authors.forename: Katsushi
authors.initials: K
authors.lastname: Ikeuchi
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003116:Color; D003124:Colorimetry / Q000379:methods*; D003196:Computer Graphics; D003198:Computer Simulation; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods; D016247:Information Storage and Retrieval / Q000379:methods; D008027:Light; D008029:Lighting; D008954:Models, Biological; D015233:Models, Statistical; D009716:Numerical Analysis, Computer-Assisted; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D013269:Stochastic Processes; D013382:Subtraction Technique