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Editor-in-Chief
Nikiforov
Vladimir O.
D.Sc., Prof.
Partners
doi: 10.17586/2226-1494-2015-15-5-796-802
OUT-OF-FOCUS REGION SEGMENTATION OF 2D SURFACE IMAGES WITH THE USE OF TEXTURE FEATURES
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Article in Russian
For citation: Trambitskiy K.V., Anding K., Polte G.A., Garten D., Musalimov V.M. Out-of-focus region segmentation of 2D surface images with the use of texture features. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2015, vol. 15, no. 5, pp. 796–802.
Abstract
For citation: Trambitskiy K.V., Anding K., Polte G.A., Garten D., Musalimov V.M. Out-of-focus region segmentation of 2D surface images with the use of texture features. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2015, vol. 15, no. 5, pp. 796–802.
Abstract
A segmentation method of out-of-focus image regions for processed metal surfaces, based on focus textural features is proposed. Such regions contain small amount of useful information. The object of study is a metal surface, which has a cone shape. Some regions of images are blurred because the depth of field of industrial cameras is limited. Automatic removal of out-of-focus regions in such images is one of the possible solutions to this problem. Focus texture features were used to calculate characteristics that describe the sharpness of particular image area. Such features are used in autofocus systems of microscopes and cameras, and their application for segmentation of out-of-focus regions of images is unusual. Thirty-four textural features were tested on a set of metal surface images with out-of-focus regions. The most useful features, usable for segmentation of an image more accurately, are an average grey level and spatial frequency. Proposed segmentation method of out-of-focus image regions for metal surfaces can be successfully applied for evaluation of processing quality of materials with the use of industrial cameras. The method has simple implementation and high calculating speed.
Keywords: image processing, segmentation, image sharpness, texture features, focus features, surface, industrial camera.
Acknowledgements. The research project, which forms the basis of this paper, is funded by the Thuringian Ministry of Economics, Technology and Work, the European Social Fund (ESF) and German Academic Exchange Service (DAAD).
References
Acknowledgements. The research project, which forms the basis of this paper, is funded by the Thuringian Ministry of Economics, Technology and Work, the European Social Fund (ESF) and German Academic Exchange Service (DAAD).
References
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