Paper Info
Title
Classifying Body and Surface Reflections Using Expectation-Maximization
Abstract
This paper presents a new method for the classification of dielectrical object's RGB values into their body and sur- face reflections. Instead of segmenting into the two reflec- tion components a weight is estimated that a given pixel belongs to one of them. A weighting value may be useful for classification of body and surface reflections in com- bination with other methods. The method operates glob- ally on the pixel points using expectation maximization for fitting the body and surface vectors in the case of one highlight reflection. In the case of multiple highlights it is shown that it is possible to relax the method by fitting one surface vector to multiple highlights. The method was empirically validated on real image data captured using a high dynamic imaging sensor (120dB). Promising results show that the method is capable of classifying the two re- flection components.
Year
Venue
Keywords
2003
PICS
data capture,image sensor,expectation maximization
Field
DocType
Citations 
Pattern recognition,Expectation–maximization algorithm,Computer science,Pixel,RGB color model,Artificial intelligence,Dynamic imaging,Real image,A-weighting
Conference
0
PageRank 
References 
Authors
0.34
2
2
Name
Order
Citations
PageRank
Hans Jørgen Andersen116719.41
Moritz Störring219514.36