Image analysis based on Radon-type integral transforms over conic sections

Dhekra El Hamdi , Mai K. Nguyen (1) , Hedi Tabia (1) , Atef Hamouda (2)

1 ETIS - UMR 8051 - Equipes Traitement de l'Information et Systèmes
2 UTM - Université de Tunis El Manar

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) shapes of images under study.

Mots clés: Radon transform; conic sections; image analysis; feature extraction.; conic sections; image analysis; feature extraction

Domaines: Traitement du signal et de l'image [eess.SP]; Vision par ordinateur et reconnaissance de formes [cs.CV]

Dhekra El Hamdi, Mai K. Nguyen, Hedi Tabia, Atef Hamouda. Image analysis based on Radon-type integral transforms over conic sections. 13th International Conference on Computer Vision Theory and Applications (VISAPP), Jan 2018, Funchal, Madeira, Portugal. ⟨hal-01759952⟩