Video Surveillance with NPR Image Fusion
We have developed a class of techniques to enhance context in images and videos. The basic idea is to increase the information density in a set of low quality images by exploiting the context from a higher quality image captured under different conditions from the same view. For example, a nighttime surveillance video is enriched with information available in daytime images or video in the infrared frequency range is augmented with video in visible light spectrum.
Background & Objective: An image is traditionally enhanced using information included within the same image. We exploit the idea that, for fixed cameras, the image of a scene can be captured under different conditions over time, e.g. illumination, wavelength, atmospheric conditions and containing static or dynamic objects. We propose a new image fusion approach to combine images with sufficiently different appearance into a seamless rendering. The method maintains fidelity of important features and robustly incorporates background contexts avoiding traditional problems such as aliasing, ghosting and haloing.
Technical Discussion: Our method first encodes the importance based on local variance in input images or videos. Then, instead of a convex combination of pixel intensities, we combine the intensity gradients scaled by the importance. The image reconstructed from the gradients achieves a smooth blend and at the same time preserves the detail in the input images. We have obtained results on indoor as well as outdoor scenes.
Outside Collaborations: Adrian Ilie and Jingyi Yu at University of North Carolina at Chapel Hill.
Contact: Jay Thornton
Technology Area: Computer Vision
Modification Date: July 7, 2008
