The Use of the Combination of Texture, Color and Intensity Transformation Features for Segmentation in the Outdoors with Emphasis on Video Processing

Sajad Sabzi, Yousef Abbaspour-Gilandeh, Jose Luis Hernandez-Hernandez, Farzad Azadshahraki and Rouhollah Karimzadeh
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Sajad Sabzi: Department of Biosystems Engineering, College of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil 56199-11367, Iran
Yousef Abbaspour-Gilandeh: Department of Biosystems Engineering, College of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil 56199-11367, Iran
Jose Luis Hernandez-Hernandez: Division of Research and Graduate Studies, Technological Institute of Chilpancingo, TecNM, Chilpancingo Guerrero 39070, Mexico
Farzad Azadshahraki: Agricultural Engineering Research Institute, Agricultural Research, Education and Extension Organization (AREEO), Karaj 31585-845, Iran
Rouhollah Karimzadeh: Department of physics, Shahid Beheshti University, G.C., Tehran 19839, Iran

Agriculture, 2019, vol. 9, issue 5, 1-14

Abstract: Segmentation is the first and most important part in the development of any machine vision system with specific goals. Segmentation is especially important when the machine vision system works under environmental conditions, which means under natural light with natural backgrounds. In this case, segmentation will face many challenges, including the presence of various natural and artificial objects in the background and the lack of uniformity of light intensity in different parts of the camera's field of view. However, today, we must use different machine vision systems for outdoor use. For this reason, in this study, a segmentation algorithm was proposed for use in environmental conditions without the need for light control and the creation of artificial background using video processing with emphasizing the recognition of apple fruits on trees. Therefore, a video with more than 12 minutes duration containing more than 22,000 frames was studied under natural light and background conditions. Generally, in the proposed segmentation algorithm, five segmentation steps were used. These steps include: 1. Using a suitable color model; 2. Using the appropriate texture feature; 3. Using the intensity transformation method; 4. Using morphological operators; and 5. Using different color thresholds. The results showed that the segmentation algorithm had the total correct detection percentage of 99.013%. The highest sensitivity and specificity of segmentation algorithm were 99.224 and 99.458%, respectively. Finally, the results showed that the processor speed was about 0.825 seconds for segmentation of a frame.

Keywords: texture features; color features; precision farming; machine vision; segmentation; video processing (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2019
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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