Object recognition using radon transform-based RST parameter estimation

Authors:
Nafaa Nacereddine;Salvatore Tabbone;Djemel Ziou
Affiliations:
DTSI, Centre de Recherche en Soudage et Controle, Algiers, Algeria;Université Nancy 2-Loria, Vandoeuvre-lès-Nancy, France;Dépt. d'informatique, Univ. de Sherbrooke, Qc., Canada
Venue:
ACIVS'12 Proceedings of the 14th international conference on Advanced Concepts for Intelligent Vision Systems
Year:
2012

Citing 9
Cited 0

On Image Analysis by the Methods of Moments

IEEE Transactions on Pattern Analysis and Machine Intelligence
Invariant Image Recognition by Zernike Moments

IEEE Transactions on Pattern Analysis and Machine Intelligence
Applying algebraic and differential invariants for logo recognition

Machine Vision and Applications
Scaling and rotation invariant analysis approach to object recognition based on Radon and Fourier-Mellin transforms

Pattern Recognition
A new shape descriptor defined on the Radon transform

Computer Vision and Image Understanding
Shape-Based Image Retrieval Using a New Descriptor Based on the Radon and Wavelet Transforms

ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Invariant Description and Retrieval of Planar Shapes Using Radon Composite Features

IEEE Transactions on Signal Processing - Part I
Rotation-invariant multiresolution texture analysis using Radon and wavelet transforms

IEEE Transactions on Image Processing
Identification of Reflected, Scaled, Translated, and Rotated Objects From Their Radon Projections

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we propose a practical parameter recovering approach, for similarity geometric transformations using only the Radon transform and its extended version on [0,2π]. The derived objective function is exploited as a similarity measure to perform an object recognition system. Comparison results with common and powerful shape descriptors testify the effectiveness of the proposed method in recognizing binary images, RST transformed, distorted, occluded or noised.