Vision-Based Change Detection Using Comparative Morphology

The chapter addresses the theoretical and practical aspects of the scene change detection problem with the use of computer vision techniques. It means detecting new or disappeared objects in images registered at different moments of time and possibly in various lighting, weather, and season conditions. In this chapter, we propose the new scheme of Comparative Morphology (CM) as a generalization of the Morphological Image Analysis (MIA) scheme originally proposed by Pyt’ev. The CMs are the mathematical shape theories, which solve the tasks of the image similarity estimation, image matching, and change detection by means of some special morphological models and tools. The original morphological change detection approach is based on the analysis of difference between the test image and its projection to the shape of reference image. In our generalized approach, the morphological filter-projector is substituted by the comparative morphological filter with weaker properties, which transforms the test image guided by the local shape of reference image. Following theoretical aspects are addressed in this chapter: the comparative morphology, change detection scheme based on morphological comparative filtering, diffusion morphology, and morphological filters based on guided contrasting. Following practical aspects are addressed: the pipeline for change detection in remote sensing data based on comparative morphology and implementation of change detection scheme based on both guided contrasting and diffusion morphology. The chapter also contains the results of qualitative and quantitative experiments on a wide set of real images including the public benchmark.