Image segmentation in a quaternion framework for remote sensing applications

Image segmentation is the critical step in imaging including applications such as video surveillance and security in controlled areas: detection and recognition of objects, their classification, analysis of crowd behavior, for identification (face recognition), for remote sensing for objects of critical infrastructure for manmade disasters and other hazards. Recently several image segmentations tools have been developed. However, these tools have limitations and sometimes not aureate since the capture devices usually generate low-resolution images, which are mostly noise and blurry. The goal of this study are: (1) To map optimally images into color images to enhance their contrast and the visibility of otherwise obscured details; (2) To perform an automated segmentation analysis using modified Chan and Vese method; and (3) To study the impact of the segmentation evaluation method. Computer simulations on the thermal dataset show that the new segmentation algorithm exhibits better results compared to state-of-the-art techniques.