Comparative Study of Exact Continuous Orthogonal Moments Applications : Local Feature Extraction and Data Compression

Authors

  • Zaineb Bahaoui Laboratoire Systèmes Intelligents & Applications (LSIA), Faculty of Science Technique Fes, Université Sidi Mohamed Ben Abdellah. Fes Morocco.
  • Rachid Benouini Laboratoire Systèmes Intelligents & Applications (LSIA), Faculty of Science Technique Fes, Université Sidi Mohamed Ben Abdellah. Fes Morocco.
  • Hakim EL Fadili Laboratoire LIPI (Informatique et Physique Interdisciplinaire),ENSA School, Université Sidi Mohamed Ben Abdellah. Fes Morocco.
  • Khalid Zenkouar Laboratoire Systèmes Intelligents & Applications (LSIA), Faculty of Science Technique Fes, Université Sidi Mohamed Ben Abdellah. Fes Morocco
  • Arsalane Zarghili Laboratoire Systèmes Intelligents & Applications (LSIA), Faculty of Science Technique Fes, Université Sidi Mohamed Ben Abdellah. Fes Morocco.

DOI:

https://doi.org/10.14738/tmlai.54.3226

Keywords:

Exact continuous moments, Legendre moments, Zernike moments, Pseudo-Zernike moments, Gegenbauer moments, Overlapping block, Feature extraction, Compression.

Abstract

This paper present an improved reconstruction algorithm of the multi-gray level images based on overlapping block method using exact continuous moments computation: Legendre , Zernike, Pseudo-Zernike and Gegenbauer moments .  We solve the artifact issue caused by unitary block reconstruction which affects the visual image quality. This method aim to ensure high accuracy and low computation time, using only small finite number of moments. Our approaches aims to introduce these moments in the field of data compression and local feature extraction for pattern recognition. Experimental results show the superiority of our proposed approaches over the existing methods.

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Published

2017-09-01

How to Cite

Bahaoui, Z., Benouini, R., EL Fadili, H., Zenkouar, K., & Zarghili, A. (2017). Comparative Study of Exact Continuous Orthogonal Moments Applications : Local Feature Extraction and Data Compression. Transactions on Machine Learning and Artificial Intelligence, 5(4). https://doi.org/10.14738/tmlai.54.3226

Issue

Section

Special Issue : 1st International Conference on Affective computing, Machine Learning and Intelligent Systems