Color Image Segmentation and Detection of Barrels Using Gaussian Mixture Model: Color Image Segmentation and Detection of Barrels Using Gaussian Mixture Model

Susanta Kumar Satpathy; Devendra Joshi; G. Ravi Kumar; Sarita Satpathy

doi:10.30732/IJBBB.20210503002

Authors

Susanta Kumar Satpathy Department of Computer Science and Engineering, Vignan’s Foundation for Science, Technology and Research, Vadlamudi, A.P.
Devendra Joshi University, Teaching Department, Chhattisgarh Swami Vivekanand Technical University
G. Ravi Kumar Department of Computer Science and Engineering, Vignan’s Institute of Information Technology, Duvvada, Visakhapatnam, India,
Sarita Satpathy Department of Management Studies, Vignan’s Foundation for Science, Technology and Research, Vadlamudi, A.P.

DOI:

https://doi.org/10.30732/IJBBB.20210503002

Keywords:

Gaussian Mixture Model, Color Segmentation, Linear Regression Model, Color Image Segmentation

Abstract

This paper is based on segmentation and detection of the color barrels. The main goal is to identify the barrels, there may be one or more barrels in the test and the training set in the given RGB images after managing segmentation color. Besides, Gaussian Mixture Model, Bayesian Model, linear regression method and other techniques are used to obtain more precise results of training images as well as prediction. We identify different color barrels such as red, blue, green and the barrel mean, variance and distance is approximately estimated by importing functions in this class by using linear regression model. GMM that is modeled as pixels and color classes, for the categorization of pixels by the class of color, and then we can differentiate the area of pixels of the barrel from non-barrel. The distance is estimated by two purposes interior One to detect the barrel and other to find the bounding box information counting four vertexes, height and width of the barrel.

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Color Image Segmentation and Detection of Barrels Using Gaussian Mixture Model