DocumentCode
2226632
Title
A Novel Shape Descriptor: Gaussian Curvature Moment Invariants
Author
Guo Kehua ; Li Min
Author_Institution
Sch. of Inf. Sci. & Technol., Central South Univ., Changsha, China
fYear
2009
fDate
26-28 Dec. 2009
Firstpage
1087
Lastpage
1090
Abstract
The moment descriptor is combined with Gaussian curvature for three-dimensional shape representation and a novel three-dimensional shape descriptor combined local with global representations is proposed in this paper. Normalization process to the new moment invariants is presented and their independence to the translation, rotation and scaling transforms is proved. Experiments indicate a better classification result to objects with slight different shape characteristic compared with some traditional approaches without increasing the running complexity.
Keywords
Gaussian processes; computational geometry; image classification; object recognition; shape recognition; Gaussian curvature moment invariants; classification result; global representations; moment descriptor; moment invariants; normalization process; rotation; running complexity; scaling transforms; shape descriptor; three-dimensional shape representation; translation transforms; Character recognition; Image segmentation; Information science; Kernel; MPEG 7 Standard; Moment methods; Noise shaping; Pattern recognition; Robustness; Shape measurement;
fLanguage
English
Publisher
ieee
Conference_Titel
Information Science and Engineering (ICISE), 2009 1st International Conference on
Conference_Location
Nanjing
Print_ISBN
978-1-4244-4909-5
Type
conf
DOI
10.1109/ICISE.2009.125
Filename
5455286
Link To Document