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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI)
Ghizlane Khaissidi
Sidi Mohamed Ben Abdellah University
Computer Science & IT Electrical & Electronics Engineering Engineering

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The impact of the image processing in the indexation system Youssef Elfakir; Ghizlane Khaissidi; Mostafa Mrabti; Driss Chenouni
International Journal of Electrical and Computer Engineering (IJECE) Vol 9, No 5: October 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1079.499 KB) | DOI: 10.11591/ijece.v9i5.pp4311-4320

Abstract

This paper presents an efficient word spotting system applied to handwritten Arabic documents, where images are represented with bag-of-visual-SIFT descriptors and a sliding window approach is used to locate the regions that are most similar to the query by following the query-by-example paragon. First, a pre-processing step is used to produce a better representation of the most informative features. Secondly, a region-based framework is deployed to represent each local region by a bag-of-visual-SIFT descriptors. Afterward, some experiments are in order to demonstrate the codebook size influence on the efficiency of the system, by analyzing the curse of dimensionality curve. In the end, to measure the similarity score, a floating distance based on the descriptor’s number for each query is adopted. The experimental results prove the efficiency of the proposed processing steps in the word spotting system.
Attention gated encoder-decoder for ultrasonic signal denoising Nabil Jai Mansouri; Ghizlane Khaissidi; Gilles Despaux; Mostafa Mrabti; Emmanuel Le Clézio
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 4: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i4.pp1695-1703

Abstract

Ultrasound imaging is one of the most widely used non-destructive testingmethods. The transducer emits pulses that travel through the imaged samplesand are reflected by echo-forming impedance. The resulting ultrasonic signalsusually contain noise. Most of the traditional noise reduction algorithmsrequire high skills and prior knowledge of noise distribution, which has acrucial impact on their performances. As a result, these methods generallyyield a loss of information, significantly influencing the final data and deeplylimiting both sensitivity and resolution of imaging devices in medical andindustrial applications. In the present study, a denoising method based on anattention-gated convolutional autoencoder is proposed to fill this gap. Toevaluate its performance, the suggested protocol is compared to widely usedmethods such as butterworth filtering (BF), discrete wavelet transforms(DWT), principal component analysis (PCA), and convolutional autoencoder(CAE) methods. Results proved that better denoising can be achievedespecially when the original signal-to-noise ratio (SNR) is very low and thesound waves’ traces are distorted by noise. Moreover, the initial SNR wasimproved by up to 30 dB and the resulting Pearson correlation coefficient wasmaintained over 99% even for ultrasonic signals with poor initial SNR.
Co-Authors Driss Chenouni Emmanuel Le Clézio Gilles Despaux Mostafa Mrabti Nabil Jai Mansouri Youssef Elfakir