초록 ▼

An embodiment of the invention is a system and process for true multi-image alignment that does not rely on the measurements of a reference image being distortion free. For instance, lens distortion is a common imaging phenomenon. When lens distortion is present, none of the images can be assumed to

An embodiment of the invention is a system and process for true multi-image alignment that does not rely on the measurements of a reference image being distortion free. For instance, lens distortion is a common imaging phenomenon. When lens distortion is present, none of the images can be assumed to be ideal. In an embodiment of the invention, all the images are modeled as intensity measurements represented in their respective coordinate systems, each of which is related to a reference coordinate system through an interior camera transformation and an exterior view transformation. Motion parameters determined in accordance with an embodiment of the invention dictate the position of the input frames within the reference frame. A reference coordinate system is used, but not a reference image. Motion parameters are computed to warp all input images to a virtual image mosaic in the reference coordinate system of the reference frame. Each pixel in the virtual image mosaic may be predicted by intensities at corresponding pixel positions from more than one image. The error measure, which is the sum of the variances of predicted pixel intensities at each pixel location summed over the virtual image mosaic, is minimized. The embodiment of the invention advantageously maximally uses information present in all images.

대표청구항 ▼

[ We claim:] [1.] Method for multi-view image registration comprising the steps of:a) receiving a plurality of images;b) establishing an ideal coordinate system;c) processing the received images to register the images to the ideal coordinate system using none of the plurality of images as a referenc

[ We claim:] [1.] Method for multi-view image registration comprising the steps of:a) receiving a plurality of images;b) establishing an ideal coordinate system;c) processing the received images to register the images to the ideal coordinate system using none of the plurality of images as a reference image, including the steps of:c1) selecting at least two images from the plurality of images;c2) computing a plurality of motion parameters to warp the at least two images to the ideal coordinate system as at least two warped images respectively; andc3) minimizing an objective function that characterizes differences between the at least two warped images in the ideal coordinate system by iteratively deriving the motion parameters in a coarse-to-fine manner over a set of image pyramids to produce the further set of parameters which define the parametric transformation.