Neural Networks and Rules-based Systems used to Find Rational and Scientific Correlations between being Here and Now with Afterlife Conditions
February 17, 2026
Neural Networks and Rules-based Systems used to Find Rational and
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Extraction of object regions from complex background is a hard task and it is an essential part of image segmentation and recognition. Image segmentation denotes a process of dividing an image into different regions. Several segmentation approaches for images have been developed. Image segmentation plays a vital role in image analysis. According to several authors, segmentation terminates when the observer’s goal is satisfied. The very first problem of segmentation is that a unique general method still does not exist: depending on the application, algorithm performances vary. This paper studies the insect segmentation in complex background. The segmentation methodology on insect images consists of five steps. Firstly, the original image of RGB space is converted into Lab color space. In the second step ‘a’ component of Lab color space is extracted. Then segmentation by two-dimension OTSU of automatic threshold in ‘a-channel’ is performed. Based on the color segmentation result, and the texture differences between the background image and the required object, the object is extracted by the gray level co-occurrence matrix for texture segmentation. The algorithm was tested on dreamstime image database and the results prove to be satisfactory.
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M.Joseph Prakash. 2013. \u201cA New Texture Based Segmentation Method to Extract Object from Background\u201d. Global Journal of Computer Science and Technology - F: Graphics & Vision GJCST-F Volume 12 (GJCST Volume 12 Issue F15): .
Crossref Journal DOI 10.17406/gjcst
Print ISSN 0975-4350
e-ISSN 0975-4172
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Extraction of object regions from complex background is a hard task and it is an essential part of image segmentation and recognition. Image segmentation denotes a process of dividing an image into different regions. Several segmentation approaches for images have been developed. Image segmentation plays a vital role in image analysis. According to several authors, segmentation terminates when the observer’s goal is satisfied. The very first problem of segmentation is that a unique general method still does not exist: depending on the application, algorithm performances vary. This paper studies the insect segmentation in complex background. The segmentation methodology on insect images consists of five steps. Firstly, the original image of RGB space is converted into Lab color space. In the second step ‘a’ component of Lab color space is extracted. Then segmentation by two-dimension OTSU of automatic threshold in ‘a-channel’ is performed. Based on the color segmentation result, and the texture differences between the background image and the required object, the object is extracted by the gray level co-occurrence matrix for texture segmentation. The algorithm was tested on dreamstime image database and the results prove to be satisfactory.
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