Compressive Bidirectional Reflection Distribution Function-Based Feature Extraction Method for Camouflaged Object Segmentation

Abstract

Camouflaged target segmentation has been widely used in both civil and military applications, such as wildlife behaviour monitoring, crop pest control, and battle reconnaissance. However, it is difficult to distinguish camouflaged objects and natural backgrounds using traditional grey-level feature extraction. In this paper, a compressive bidirectional reflection distribution function-based feature extraction method is proposed for effective camouflaged object segmentation. First, multidimensional grey-level features are extracted from multiple images with different illumination angles in the same scene. Then, the multidimensional grey-level features are expanded based on Chebyshev polynomials. Next, the first several coefficients are integrated as a new optical feature, which is named the compressive bidirectional reflection distribution function feature. Finally, the camouflaged object can be effectively segmented from the background by compressive feature clustering. Both qualitative and quantitative experimental results prove that our method has remarkable advantages over conventional single-angle or multi-angle grey-level feature-based methods in terms of segmentation precision and running speed.