Image Dilution using Harris Corner Detection and Geometric Kernels

Image files contain large amounts of data. An image is essentially a matrix of values, often represented as ARGB (alpha, red, green, blue) arrays. A large three dimensional matrix with a height and width likely in the hundreds can take long for a program to read. However, many applications may only require specific key features to understand an image. The majority of pixel data is relatively unimportant when determining the contents of the image file. In fact, such extra data can sometimes deceive the machine, or in the case of a hybrid image [1], the human viewer. Using only basic matrix calculations and matrix convolution, specifically Harris Corner Detection and Edge Detection kernels, we have extracted the key features of an image in order to dilute its data. The results show that any confusion, such as noise or the low frequencies of a hybrid image, becomes weaker, and that the computation is very efficient and robust.