Target Detection Based on Fractal Features

Application Context Fractal features characterize the roughness of object surface textures, with fractal dimension serving as their mathematical representation. Since man-made targets typically exhibit smooth surfaces while natural backgrounds tend to have rough textures, leveraging their dimensional differences provides a distinctive approach for target detection. Core Technology This project implements the box-counting method to calculate fractal dimensions of image regions, utilizing inter-regional dimensional variations for target localization. To enhance computational efficiency, we developed an improved fast box-counting algorithm that optimizes the conventional method through algorithmic refinements and parallel processing techniques.