Title :
Statistical Analysis of the Global Geodesic Function for 3D Object Classification
Author :
Aouada, Djamila ; Shuo Feng ; Krim, H.
Author_Institution :
Dept. of Electr. & Comput. Eng., North Carolina State Univ., Raleigh, NC, USA
Abstract :
This paper presents a novel classification strategy for 3D objects. Our technique is based on using a global geodesic function to intrinsically describe the surface of an object. The choice of the global geodesic function ensures the invariance of the classification procedure to scaling and all isometric transformations. Using the Jensen-Shannon divergence, feature parameters are extracted from the probability distribution functions of the global geodesic function for each one of the classes. These parameters are used in the decision of a class membership of an object. This approach demonstrates low computational cost, efficiency, and robustness to resolution over many different data sets.
Keywords :
differential geometry; feature extraction; image classification; statistical analysis; 3D object classification; Jensen-Shannon divergence; feature parameter extraction; global geodesic function; isometric transformations; statistical analysis; Application software; Computational efficiency; Data mining; Feature extraction; Geophysics computing; Probability distribution; Robustness; Shape measurement; Statistical analysis; Testing; Feature extraction; Geodesic; Jensen-Shannon Divergence; Object classification;
Conference_Titel :
Acoustics, Speech and Signal Processing, 2007. ICASSP 2007. IEEE International Conference on
Conference_Location :
Honolulu, HI
Print_ISBN :
1-4244-0727-3
DOI :
10.1109/ICASSP.2007.365990