Shadows often introduce errors in the performance of computer vision algorithms, such as object detection and tracking. This paper proposes a method to remove shadows from real images based on a probability shadow map. The probability shadow map identifies how much light is impinging on a surface. The lightness of shadowed regions in an image is increased and then the color of that part of the surface is corrected so that it matches the lit part of the surface. The result is compared with two other shadow removal frameworks. The advantage of our method is that after removal, the texture and all the details in the shadowed regions remain intact.
Knowledge of lens specifications is important to identify the best lens for a given capture scenario and application. Lens manufacturers provide many specifications in their data sheets, and multiple initiatives for testing and comparing different lenses can be found online. However, due to the lack of a suitable metric or technique, no evaluation of axial chromatic aberration is available. In this paper, we propose a metric, Axial Aberration Magnitude or AAM, that assesses the degree of axial chromatic aberration of a given lens. Our metric is generalizable to multispectral acquisition systems and is very simple and cheap to compute. We present the entire procedure and algorithm for computing the AAM metric, and evaluate it for two spectral systems and two consumer lenses.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.