Technical gazette, Vol. 25 No. 6, 2018.
Original scientific paper
https://doi.org/10.17559/TV-20171129091703
Application of Statistical Indicators for Digital Image Analysis and Segmentation in Sorting of Agriculture Products
Ivana Markovic
orcid.org/0000-0001-7200-763X
; Faculty of Mechanical Engineering, Kraljcie Marije 16, 11000 Belgrade, Serbia
Dragan Markovic
; Faculty of Mechanical Engineering, Kraljcie Marije 16, 11000 Belgrade, Serbia
Jelena Ilic
; Faculty of Mechanical Engineering, Kraljcie Marije 16, 11000 Belgrade, Serbia
Vojislav Simonovic
orcid.org/0000-0001-7698-5414
; Faculty of Mechanical Engineering, Kraljcie Marije 16, 11000 Belgrade, Serbia
Emil Veg
orcid.org/0000-0002-6702-6251
; Faculty of Mechanical Engineering, Kraljcie Marije 16, 11000 Belgrade, Serbia
Goran Šiniković
; Faculty of Mechanical Engineering, Kraljcie Marije 16, 11000 Belgrade, Serbia
Nenad Gubeljak
orcid.org/0000-0002-3276-8431
; University of Maribor, Facutly of Mechanical Engineering, Smetanova 17, 2000 Maribor, Slovenia
Abstract
Food processing industry is moving forward to a full automation of all processes, especially in technological line segments which represent critical control points of food safety. One of these points is color sorting by using machine vision, where inappropriate products are removed. Most important product appearance attributes are color and texture. During food processing, the product is captured by optical devices, mostly color cameras and lasers. The aim of this paper is to investigate new eligibility criteria for digital image segmentation by using only image from the camera. The goal is to describe the texture of the product, based on chosen mathematical measures, and to allow for recognition and then classification according to the predefined range of values in an appropriate class. Images of frozen raspberry were used. Image analysis of color parameters in RGB color space and statistical tests to examine normality of data were carried out. Thereafter, one-way Anova and correlation analysis was performed. Statistically significant difference was found for the values of two indicators: entropy and new criteria were derived from standard deviation, as well as mean values of pixels for every channel, and marked as L. After determining the range of these criteria, a new algorithm was developed for image segmentation written in Matlab. One of the results of applying this algorithm is that more than 80% of good products were recognized.
Keywords
color; fruits; segmentation; sorting
Hrčak ID:
212829
URI
Publication date:
16.12.2018.
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