Image Convolution to Obtain Color ROI after Segmentation Process with Fuzzy Cmeans

Abstract

Image segmentation is still an important concern in terms of digital image processing. Segmentation refers to dividing an image into several parts based on similar characteristics or uniformity. Its use is quite important, especially related to the analysis and application of digital image processing. The challenge faced is separating the object image from its background in images with complex backgrounds. The aim of this research is to separate tomatoes from simple to complex backgrounds. This paper proposes a convolution method of segmented binary images and RBG images all based on contours using Fuzzy C-means and reconstruction operations to obtain the foreground from an image with a complex background. This method has been tested on ripe tomatoes with various backgrounds. This method has Indicated Performance Achievement Sc = 99.2%, Fpe = 0.6% and FNe = 0.4%. This shows that the method is suitable and robust for the dataset used in this study, especially if it will be continued for further work related to the classification of tomato maturity assessment.

Keywords

• background, foreground, fuzzy cmean, convolution, segmentation