Authors:
Dhekra El Hamdi
1
;
Mai K. Nguyen
2
;
Hedi Tabia
3
and
Atef Hamouda
2
Affiliations:
1
Université de Cergy-Pontoise, Faculté des Sciences de Tunis and Université de Tunis EL Manar, France
;
2
Université de Cergy-Pontoise, France
;
3
Faculté des Sciences de Tunis and Université de Tunis EL Manar, Tunisia
Keyword(s):
Radon Transform, Conic Sections, Image Analysis, Feature Extraction.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Features Extraction
;
Image and Video Analysis
Abstract:
This paper presents a generalized Radon transform defined on conic sections called Conic Radon Transform (CRT) for image analysis. The proposed CRT extends the classical Radon transform (RT) which integrates a image function f(x,y) over straight lines. As the CRT is capable of detecting conic sections with any position
and orientation in original images it makes possible to build a new descriptor based on integrating an image over conic sections. In order to test and verify the utility and performance of this new approach we have developed, in this work, the Radon transforms defined on circles and on parabolas, then built a descriptor
combining the features extracted by the circular RT, parabolic RT and linear RT. This descriptor is applied to object classification. A number of experiments on both synthetic and real datasets illustrates the efficiency and the advantages of this new approach taking into account the global features of different (circular, parabolic
and linear) shape
s of images under study.
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