Projection Kernel Regression for Image Registration and Fusion in Video-Based Criminal Investigation

A projection kernel regression framework is set and applied for image registration and fusion in video-based criminal investigation. In image registration, a dominant point is defined to capture the local variation property of an image when the resolution is very low and thus most algorithms proposed till now for control point extraction fail. The order relationship between the sorted dominant point sequences, extracted respectively from the reference image and the target image, matches the two images, while the location relationship between the two sequences determines the input-output pairs in projection kernel regression, for approximation of the coordinate mapping function of the reference image and the target image. In image reconstruction, an analogue image is firstly estimated by the projection kernel regression using all of the registered images, and then re-sampled to obtain an enlarged image with arbitrary resolution. Experimental results on a section of surveillance video for criminal investigation show that the presented method is effective in solving the image registration and fusion problems in the aforementioned case.