Change detection based on features invariant to monotonic transforms and spatial constrained matching

Discovering regions that have changed in a set of images acquired from a scene at different times and possibly from different view points and cameras is a crucial step for many image processing applications. Remote sensing, visual surveillance, medical diagnosis and treatment, civil infrastructure, and underwater sensing are some examples of such applications. This work proposes a novel approach to detect changes automatically without a learning step by using image analysis techniques and segmentation based on superpixels. Unlike most common approaches, which are pixel-based, we present an approach that combines super-pixel extraction, hierarchical clustering and segment matching. The experimental results show the effectiveness of the proposed approach comparing it a background subtraction technique, demonstrating the robustness of our algorithm to illumination variations, non-uniform attenuations, atmospheric absorption and swaying trees.