Utilization of K-means Clustering and Color Homography for Automatic Color Calibration in Image Processing

Often camera calibration in terms of lighting has become a challenge in machine learning. Large training datasets are usually required due to various light conditions that affect the colors on the images, making objects difficult to recognize. This paper proposes the utilization of K-means clustering to extract colors on the images to be used in combination of color Homography to correct colors in low light images automatically. This method aims to solve tedious camera calibration in terms of color and reduce the number of datasets.