Target Detection Based on Fractal Features

Application Context: Fractal features describe the roughness of surface textures, with fractal dimension mathematically quantifying these characteristics. Current research indicates that while artificial targets generally possess smooth surfaces and natural backgrounds exhibit rough textures, exploiting dimensional differences for target detection represents a novel approach. Thus, further exploration of this methodology and its practical application potential is warranted.

Core Technology: The project employs the box-counting method to compute fractal dimensions of image regions, achieving target localization through dimensional variance analysis. For enhanced computational efficiency, we developed an optimized fast box-counting algorithm that improves upon the standard method by implementing dynamic step sizing and matrix-based operations. The implementation involves calculating dimension curves through multi-scale grid coverage, with key functions including region segmentation, dimension mapping, and threshold-based classification. Additionally, we integrated advanced image processing algorithms incorporating Gaussian pyramid decomposition and morphological operations to boost detection accuracy and processing speed.

Through these innovations and improvements, we anticipate this research will advance the target detection field by providing more reliable and efficient solutions for practical applications, particularly in remote sensing and automated surveillance systems.