Horizontal, Vertical, and Custom Diagonal Projections for Grayscale Images

When processing grayscale images, we can perform horizontal, vertical, and diagonal projections. The diagonal projection algorithm is my original design and has been successfully tested through execution. Horizontal projection calculates the pixel density distribution along the horizontal axis of the image, enabling analysis and processing of horizontal lines within the image. This is typically implemented by summing pixel values row-wise across the image matrix, producing a one-dimensional array where each element represents the cumulative intensity of corresponding image rows. Vertical projection computes the pixel density distribution along the vertical axis, facilitating the analysis and processing of vertical lines in the image. This is achieved through column-wise summation of pixel values, generating a profile that reveals intensity variations along the image's vertical dimension. Diagonal projection represents a specialized projection method that extracts and analyzes diagonal lines within the image, thereby capturing oblique line information. The custom implementation involves traversing the image matrix along diagonal trajectories (both main and anti-diagonals) while accumulating pixel intensities, effectively highlighting diagonal features that might be missed by standard horizontal/vertical projections. These projection techniques provide valuable tools for better understanding and processing grayscale images, forming an essential foundation for subsequent image processing tasks such as line detection, segmentation, and feature extraction.