Character of images development on Gaussian copula model using distribution of cumulative distribution function

Abstract

This paper is a preliminary study that aims to build the character of color image object through the Gaussian copula model. The development of the Gaussian copula model is carried out with the distribution of Cumulative Distribution Function (CDF) of the pixels as input. In contrast to other studies that use image pixel values. The random variables used are the CDF value at the point of differentiation determined by the Kulback-Leibler Divergence (KLD). The case discussed in this paper is the image modeling of red apples and green apples. The model is built on each Red, Green, and Blue (RGB) image with three distinguishing points. The result obtained is a Gaussian copula model with different parameters for red apples and green apples. The character of joint distribution is also different. The joint distribution value of green apples is greater than that of red apples in R and G images. The difference in the models obtained shows that the character of this image is different. Future research will use the acquired models to identify new images, whether they are recognized as red and green apples or not. Further development in the field of healthy vision. This model can be used to identify fruit images in an application.