Dr. Mustapha M. Sadki Profile

Dr. Mustapha M. Sadki

Research Scientist at Datumatic Ltd

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Publications (7)

Proceedings Article | 11 March 2005 Paper

Inter-update Metz filtering as regularization for variable block-ART in PET reconstruction

Mustapha Sadki, Maite San-Martin

Proceedings Volume 5674, (2005) https://doi.org/10.1117/12.587631

KEYWORDS: Positron emission tomography, Reconstruction algorithms, Image filtering, Scanners, Chromium, Image processing, Data acquisition, Data modeling, Tumors, Visualization

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Proceedings Article | 2 November 2004 Paper

Generalized and optimized classification framework for textural imagery

Maite Trujillo San-Martin, Mustapha Sadki

Proceedings Volume 5558, (2004) https://doi.org/10.1117/12.561069

KEYWORDS: Image classification, Synthetic aperture radar, Feature extraction, Image enhancement, Image analysis, Image processing, Classification systems, Data analysis, Remote sensing, Feature selection

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Proceedings Article | 2 November 2004 Paper

Multilayer cellular neural network and fuzzy C-mean classifiers: comparison and performance analysis

Maite Trujillo San-Martin, Vejen Hlebarov, Mustapha Sadki

Proceedings Volume 5558, (2004) https://doi.org/10.1117/12.561077

KEYWORDS: Neural networks, Classification systems, Fuzzy logic, Corrosion, Image classification, Feature extraction, Image processing, Evolutionary algorithms, Neurons, Databases

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Proceedings Article | 28 May 2004 Paper

Fuzzy C-means classification for corrosion evolution of steel images

Maite Trujillo, Mustapha Sadki

Proceedings Volume 5298, (2004) https://doi.org/10.1117/12.527116

KEYWORDS: Image classification, Fuzzy logic, Corrosion, Mathematical modeling, Data modeling, Databases, RGB color model, Image processing, Classification systems, Tolerancing

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Proceedings Article | 3 May 2004 Paper

Sensitivity analysis for texture models applied to rust steel classification

Maite Trujillo, Mustapha Sadki

Proceedings Volume 5303, (2004) https://doi.org/10.1117/12.526838

KEYWORDS: Statistical analysis, Image classification, Corrosion, Data modeling, RGB color model, Coating, Mathematical modeling, Principal component analysis, Image processing, Data analysis

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Proceedings Article | 8 March 2002 Paper

Performance-scalable volumetric data classification for online industrial inspection

Aby Abraham, Mustapha Sadki, R. Lea

Proceedings Volume 4664, (2002) https://doi.org/10.1117/12.460203

KEYWORDS: Inspection, Visualization, Visual analytics, Computed tomography, Manufacturing, Detection and tracking algorithms, Hough transforms, Databases, Commercial off the shelf technology, Volume visualization

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Proceedings Article | 21 August 1996 Paper

Fast algorithms for automatic moire fringe analysis: application to noncontact measurements for quality control of industrial components

Michel Bruynooghe, Mustapha Sadki, Jacques Harthong, Axel Becker

Proceedings Volume 2786, (1996) https://doi.org/10.1117/12.248581

KEYWORDS: Moire patterns, Image segmentation, Fringe analysis, Image enhancement, Image processing, Control systems, Image filtering, Quality measurement, Binary data, Image quality

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Moire methods are optical methods that are based on the effect of superposition of grating lines and have been widely used in the context of industrial applications for shape analysis, for non-contact measurements, and for quality control of industrial components. In applications the following computations: image filtering, fringe skeletonizing and fringe numbering have to be performed for each test object, before comparison between the numerically reconstructed test object shape and its CAD model. In order to reduce the computing time required by the preceding computations, the inverse moire technique has been introduced by Harthong. Instead of using a grating made of parallel straight lines, the inverse moire technique uses a pre-computed specific gratin, that is formed of curved lines such that the moire pattern is composed of parallel straight fringes if the test object shape is conformed to its CAD model. Defects are then characterized by a deformation and a curvature of these parallel fringes. In this paper, we present examples showing that standard fringe extraction by automatic thresholding is not that easy. To overcome this difficulty, we propose a four stage process algorithmical approach that allows fringe detection in inverse moire images with high sensitivity and specificity. First we used the well-known image processing technique called unsharp masking, to enhance moire image and to emphasize low contrasted fringes. The second step is to extract bright fringes by image segmentation and constrained contour modeling. After detection of these bright fringes inside the zone of interest of the moire image, we get the thick skeleton of adjacent background and of dark fringes. The third step is to skeletonize this thick skeleton of adjacent background and of dark fringes, using morphological thinning of well-composed sets, that assures that each fringe skeleton will be one pixel thick, at the difference of standard thinning techniques. The fourth step is to apply a graph technique to isolate the individual dark fringes. When all these four steps have been followed, one is left with a binary image showing the dark fringe pattern skeleton. The experimental results that have been obtained have shown the robustness of this algorithmical approach, for the analysis of noisy inverse moire images.

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