Ms. Daria B. Petukhova Profile

Daria B. Petukhova

Student at National Research Univ of ITMO

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Publications (3)

Proceedings Article | 13 March 2015 Paper

Research of principles for estimating the freshness of meat products by color analysis method

Elena Gorbunova, Aleksandr Chertov, Daria Petukhova, Artem Alekhin, Valery Korotaev

Proceedings Volume 9369, 93690Z (2015) https://doi.org/10.1117/12.2081664

KEYWORDS: Computer simulations, Analytical research, Light sources, Machine vision, Oxygen, Natural surfaces, Light, Algorithm development, 3D image processing, Nitrogen

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Proceedings Article | 23 September 2014 Paper

Principles of image processing in machine vision systems for the color analysis of minerals

Daria Petukhova, Elena Gorbunova, Aleksandr Chertov, Valery Korotaev

Proceedings Volume 9217, 921720 (2014) https://doi.org/10.1117/12.2061602

KEYWORDS: Minerals, Image processing, Diagnostics, Image analysis, Algorithm development, Machine vision, RGB color model, Software development, Analytical research, Raw materials

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At the moment color sorting method is one of promising methods of mineral raw materials enrichment. This method is based on registration of color differences between images of analyzed objects. As is generally known the problem with delimitation of close color tints when sorting low-contrast minerals is one of the main disadvantages of color sorting method. It is can be related with wrong choice of a color model and incomplete image processing in machine vision system for realizing color sorting algorithm. Another problem is a necessity of image processing features reconfiguration when changing the type of analyzed minerals. This is due to the fact that optical properties of mineral samples vary from one mineral deposit to another. Therefore searching for values of image processing features is non-trivial task. And this task doesn't always have an acceptable solution. In addition there are no uniform guidelines for determining criteria of mineral samples separation. It is assumed that the process of image processing features reconfiguration had to be made by machine learning. But in practice it's carried out by adjusting the operating parameters which are satisfactory for one specific enrichment task. This approach usually leads to the fact that machine vision system unable to estimate rapidly the concentration rate of analyzed mineral ore by using color sorting method. This paper presents the results of research aimed at addressing mentioned shortcomings in image processing organization for machine vision systems which are used to color sorting of mineral samples. The principles of color analysis for low-contrast minerals by using machine vision systems are also studied. In addition, a special processing algorithm for color images of mineral samples is developed. Mentioned algorithm allows you to determine automatically the criteria of mineral samples separation based on an analysis of representative mineral samples. Experimental studies of the proposed algorithm were performed using samples of gold and copper-nickel ores. And obtained results confirmed its efficiency with respect to mineral objects. The research results will allow: expanding the use of the color sorting method in the field of mineral raw materials enrichment; facilitating the search for values of image processing features for machine vision systems which are used to the color analysis of minerals; reducing the time required for reconfiguration of image processing features when changing the type of analyzed minerals; realizing the process of rapid estimating the concentration rate of analyzed mineral ore by using color sorting method.

Proceedings Article | 24 May 2013 Paper

Optical-electronic system for express analysis of mineral raw materials dressability by color sorting method

Artem Alekhin, Elena Gorbunova, Aleksandr Chertov, Darya Petuhova

Proceedings Volume 8791, 87911N (2013) https://doi.org/10.1117/12.2020460

KEYWORDS: Minerals, Raw materials, RGB color model, Statistical analysis, Image processing, Statistical modeling, Optics manufacturing, Light sources, Systems modeling, Image segmentation

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