Dr. Sophie Kohler Profile

Dr. Sophie Kohler

at Univ de Haute Alsace

SPIE Involvement:

Author

Publications (17)

Proceedings Article | 28 July 2023 Paper

f-AnoGAN for non-destructive testing in industrial anomaly detection

Oumaima Sliti, Maxime Devanne, Sophie Kohler, Naim Samet, Jonathan Weber, Christophe Cudel

Proceedings Volume 12749, 1274915 (2023) https://doi.org/10.1117/12.3000063

KEYWORDS: Education and training, Gallium nitride, Data modeling, Image restoration, Manufacturing, RGB color model, Statistical analysis

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Proceedings Article | 16 July 2021 Paper

Deflectometry based on light-field imaging

A. Meguenani, K. Tout, S. Kohler, S. Bazeille, J.-P. Chambard, C. Cudel

Proceedings Volume 11794, 1179408 (2021) https://doi.org/10.1117/12.2588988

KEYWORDS: Deflectometry, Cameras, Defect detection, System integration, Reflectivity, Imaging systems

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Proceedings Article | 30 April 2015 Paper

A comparative study of multi-sensor data fusion methods for highly accurate assessment of manufactured parts

Ammar Hannachi, Sophie Kohler, Alex Lallement, Ernest Hirsch

Proceedings Volume 9534, 95340X (2015) https://doi.org/10.1117/12.2182832

KEYWORDS: Data fusion, 3D modeling, Sensors, 3D image processing, Machine vision, 3D vision, Image fusion, Head, Manufacturing, Structured light

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Proceedings Article | 30 April 2015 Paper

A four-lens based plenoptic camera for depth measurements

Cécile Riou, Zhiyuan Deng, Bruno Colicchio, Jean-Philippe Lauffenburger, Sophie Kohler, Olivier Haeberlé, Christophe Cudel

Proceedings Volume 9534, 95340V (2015) https://doi.org/10.1117/12.2182815

KEYWORDS: Cameras, Calibration, Lenses, Distortion, Image sensors, 3D metrology, Copper, Prototyping, Stereoscopic cameras, Corner detection

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Proceedings Article | 11 May 2012 Paper

Line matching for automatic change detection algorithm

Jérôme Dhollande, David Monnin, Laetitia Gond, Christophe Cudel, Sophie Kohler, Alain Dieterlen

Proceedings Volume 8357, 83571L (2012) https://doi.org/10.1117/12.919856

KEYWORDS: Signal detection, Cameras, Image registration, Improvised explosive devices, Roads, Video, Detection and tracking algorithms, Image processing, Global Positioning System, Signal processing

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SPIE Journal Paper | 7 May 2012

Original method to compute epipoles using variable homography: application to measure emergent fibers on textile fabrics

Jun Xu, Christophe Cudel, Sophie Kohler, Olivier Haeberlé, Stephane Fontaine, Marie-Louise Klotz

JEI, Vol. 21, Issue 2, 021103, (May 2012) https://doi.org/10.1117/12.10.1117/1.JEI.21.2.021103

KEYWORDS: Cameras, Optical fibers, 3D metrology, Calibration, Device simulation, Glasses, Computer simulations, Defect detection, Microscopes, Imaging systems

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Proceedings Article | 12 July 2011 Paper

Illumination control in view of dynamic (re)planning of 3D reconstruction tasks

Abdelkrim Belhaoua, Sophie Kohler, Ernest Hirsch

Proceedings Volume 8000, 800002 (2011) https://doi.org/10.1117/12.889322

KEYWORDS: Light sources, 3D modeling, Image processing, 3D image processing, 3D image reconstruction, Image quality, Sensors, Control systems, 3D acquisition, Cameras

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Proceedings Article | 12 July 2011 Paper

Using variable homography to measure emergent fibers on textile fabrics

Jun Xu, Christophe Cudel, Sophie Kohler, Stéphane Fontaine, Olivier Haeberlé, Marie-Louise Klotz

Proceedings Volume 8000, 800003 (2011) https://doi.org/10.1117/12.890897

KEYWORDS: Cameras, Optical fibers, Calibration, Prototyping, Glasses, Defect detection, 3D metrology, Applied sciences, Microscopes, Computer simulations

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Proceedings Article | 17 June 2009 Paper

Estimation of 3D reconstruction errors in a stereo-vision system

A. Belhaoua, S. Kohler, E. Hirsch

Proceedings Volume 7390, 73900X (2009) https://doi.org/10.1117/12.827346

KEYWORDS: 3D modeling, Error analysis, 3D image processing, Image processing, Cameras, Calibration, Image segmentation, Image quality, Control systems, Edge detection

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Proceedings Article | 29 May 2007 Paper

Dynamic replanning of 3D automated reconstruction using situation graph trees and illumination adjustment

Sophie Kohler, Aïcha Beya Far, Ernest Hirsch

Proceedings Volume 6356, 635602 (2007) https://doi.org/10.1117/12.736684

KEYWORDS: 3D modeling, Light sources and illumination, Image segmentation, 3D image reconstruction, 3D image processing, Computer aided design, 3D acquisition, Solid modeling, Cognitive modeling, Visual process modeling

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Proceedings Article | 1 May 2003 Paper

Cognitive intelligent sensory system for vision-based quality control

Sophie Kohler, Ernest Hirsch

Proceedings Volume 5132, (2003) https://doi.org/10.1117/12.515118

KEYWORDS: Sensors, 3D modeling, Light sources, Visual process modeling, 3D image processing, Solid modeling, Reflection, Image analysis, Light sources and illumination, Computer aided design

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Proceedings Article | 4 April 2001 Paper

New filter for detecting defects on industrial parts by artificial vision

Sophie Kohler, Pierre Geveaux, Johel Miteran

Proceedings Volume 4301, (2001) https://doi.org/10.1117/12.420899

KEYWORDS: Electronic filtering, Defect detection, Signal to noise ratio, Image segmentation, Signal detection, Light sources and illumination, Machine vision, Gaussian filters, Digital filtering, Bandpass filters

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SPIE Journal Paper | 1 December 2000

Analysis of compatibility between lighting devices and descriptive features using Parzen's Kernel: application to flaw inspection by artifical vision

Pierre Geveaux, Sophie Kohler, Johel Miteran, Frederic Truchetet

OE, Vol. 39, Issue 12, (December 2000) https://doi.org/10.1117/12.10.1117/1.1323676

KEYWORDS: Light sources and illumination, Chromium, Inspection, Machine vision, Feature selection, Error analysis, Optical engineering, Statistical analysis, Principal component analysis, Image processing

Proceedings Article | 21 March 2000 Paper

Comparison between two classification methods: application to defects detection by artificial vision in industrial field

Pierre Geveaux, Sophie Kohler, Johel Miteran, Frederic Truchetet, Fabrice Meriaudeau

Proceedings Volume 3966, (2000) https://doi.org/10.1117/12.380069

KEYWORDS: Machine vision, Image segmentation, Defect detection, Image classification, Light sources and illumination, Statistical analysis, Electroluminescence, Pattern recognition, Image quality, Classification systems

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Quality control by artificial vision is getting more and more widespread within the industry. Indeed, in many cases, industrial applications require a control with high stability performance, satisfying high production rate. For texture control, some major problems may occur: uneasiness to show different textures, segmentation features as well as classification and decision phases requiring still to much computation time. This article presents a comparison between two non-parametric classification methods used for real time control of textured objects moving at a cadence of 10 pieces per second. Four types of flaws have to be indifferently detected: smooth surfaces, bumps, hollow knocked surfaces and lacks of material. These defects generate texture variations which have to be detected and sorted out by our system, each flaw apparition being registered to carry out a survey over the production cycle. We previously presented a search for an optimal lighting system, in this case the acquired images were tremendously improved. On these optimal images, we described a method for selecting the best segmentation features. The third step, which is presented here, is a comparison between two multi-classes classification algorithms: the Parzen's estimator and the so-called 'stressed polytopes' method. These two algorithms which require a learning phase are both based on a non-parametric discrimination method of the flaw classes. In one hand, they are both relatively inexpensive in time calculation but on the other hand they present different assets relative to the easiness of the learning phase or the number of useable segmentation features. They also have a different behavior towards the cut out of the features space, especially on the 'cross-classes' border. Their comparison is made through the aforementioned quoted points which are relevant for the evaluation of the discrimination efficiency. Finally, through an industrial example we present the results of such a comparison. The control, a PC based machine, includes the calculation five classification features (calculations were carried out on the local neighborhood of each pixel), five distinct classes for the classification phase and the decision phase. This led to a 3,63% classification error ratio for the best compromise.

Proceedings Article | 19 May 1999 Paper

Human cell texture analysis with quincunx spline wavelet transform

Frederic Nicolier, Olivier Laligant, Frederic Truchetet, Anne-Claire Legrand, Sophie Kohler

Proceedings Volume 3644, (1999) https://doi.org/10.1117/12.348481

KEYWORDS: Digital filtering, Wavelets, Wavelet transforms, Image processing, Image analysis, Ions, Image classification, Statistical analysis, Image filtering, Distance measurement

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Proceedings Article | 8 March 1999 Paper

Method of searching segmentation parameters applied to quality control of industrial pieces

Pierre Geveaux, Sophie Kohler, Johel Miteran, Frederic Truchetet

Proceedings Volume 3652, (1999) https://doi.org/10.1117/12.341127

KEYWORDS: Light sources and illumination, Image segmentation, Machine vision, Defect detection, Image quality, Feature extraction, Feature selection, Principal component analysis, Inspection, Nanoimprint lithography

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Proceedings Article | 6 October 1998 Paper

Searching method for an optimal lighting application for defect detection by artificial vision in industrial field

Pierre Geveaux, Sophie Kohler, Johel Miteran, Frederic Truchetet

Proceedings Volume 3522, (1998) https://doi.org/10.1117/12.325755

KEYWORDS: Light sources and illumination, Machine vision, Defect detection, Image quality, Light emitting diodes, Image segmentation, Robots, Computer vision technology, Robot vision, Active vision

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Showing 5 of 17 publications

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