Estimating the Complexity of Objects in Images

A new method for estimating the complexity of geometric shapes (spots) is proposed that takes into account the internal structure of the spots in addition to their external contour. The task of calculating the degree of complexity of objects is divided into the subtasks of segmenting the spots and estimating the complexity of isolated spots. The new method has a relatively low computational complexity compared to the alternative methods considered in the work. Using the new method, an algorithm based on parallel computing in the CUDA architecture for GPUs is implemented, which further increases the performance of the method. A qualitative and quantitative analysis of existing (alternative) methods is carried out, and their advantages and disadvantages in comparison with the proposed method and with each other are revealed. The algorithm implemented on the basis of the new method was tested on both artificial and real digital images.