When light is reflected from object surface, its spectral characteristics will be affected by the surface's elemental composition, and its polarimetric characteristics will be governed by the surface's roughness and conductance. Polarimetric and multispectral imaging can provide complementary discriminative information in applications such as object separation. However, few methods have been proposed to fuse the information provided by polarimetric and multispectral imagery for better object separation results. Considering that the metal and dielectric materials, and the manmade objects and natural background have different polarimetric and multispectral features, in this paper we propose a simple yet powerful method for object separation by using the polarimetric and spectral characteristics of specular and diffuse reflected light. A polarimetric imagery fusion algorithm is first proposed based on the degree of linear polarization modulation to distinguish different objects. Then the spectral and polarimetric information, which can be extracted from the specular and diffuse reflected light, is fused by using the HSI color space mapping for more robust object separation. Experiments on real outdoor and indoor images are performed to evaluate the efficiency of the proposed scheme.
- Image segmentation
- Object separation
ASJC Scopus subject areas
- Signal Processing
- Computer Vision and Pattern Recognition