Details:
Title  Adaptive and optimal difference operators in image processing  Author(s)  Kristof Teelen, Peter Veelaert  Type  Article in Journal  Abstract  Differential operators are essential in many image processing applications. Previous work has shown how to compute derivatives more accurately by examining the image locally, and by applying a difference operator which is optimal for each pixel neighborhood. The proposed technique avoids the explicit computation of fitting functions, and replaces the function fitting process by a function classification process using a filter bank of feature detection templates. Both the feature detectors and the optimal difference operators have a specific shape and an associated cost, defined by a rigid mathematical structure, which can be described by Gröbner bases. This paper introduces a cost criterion to select the operator of the best approximating function class and the most appropriate template size so that the difference operator can be locally adapted to the digitized function. We describe how to obtain discrete approximates for commonly used differential operators, and illustrate how image processing applications can benefit from the adaptive selection procedure for the operators by means of two example applications: tangent computation for digitized object boundaries and the Laplacian of Gaussian edge detector.  Keywords  Difference operator, Gröbner basis, Local feature detector, Tangent, Laplacian  ISSN  00313203 
URL 
http://www.sciencedirect.com/science/article/pii/S0031320308004810 
Language  English  Journal  Pattern Recognition  Volume  42  Number  10  Pages  2317  2326  Year  2009  Note  Selected papers from the 14th IAPR International Conference on Discrete Geometry for Computer Imagery 2008  Edition  0  Translation 
No  Refereed 
No 
