Abstract: A system and method for stabilizing video using a census kernel having test points. A subset form a connected circle centered on the kernel's center point. For each candidate pixel, a signature is calculated based on the relative brightness of the candidate pixel and of the pixels corresponding to test points when the kernel overlays the image with the center point over the candidate pixel. A candidate pixel is designated to be a corner pixel by processing the signature to determine that the candidate pixel is significantly brighter or darker than the test point pixels. For each corner pixel, a corresponding pixel in the previous frame is identified by comparing the signature of the corner pixel with signatures of pixels in the previous frame, and a corresponding motion vector is established. A motion model is calculated based on the motion vectors and applied to the digital frame.

Abstract: A system and method for stabilizing video using a census kernel having test points. A subset form a connected circle centered on the kernel's center point. For each candidate pixel, a signature is calculated based on the relative brightness of the candidate pixel and of the pixels corresponding to test points when the kernel overlays the image with the center point over the candidate pixel. A candidate pixel is designated to be a corner pixel by processing the signature to determine that the candidate pixel is significantly brighter or darker than the test point pixels. For each corner pixel, a corresponding pixel in the previous frame is identified by comparing the signature of the corner pixel with signatures of pixels in the previous frame, and a corresponding motion vector is established. A motion model is calculated based on the motion vectors and applied to the digital frame.

Abstract: Calibration methods use a calibration target for obtaining the intrinsic and extrinsic camera parameters of one or more cameras are. The methods can include acquiring, with each camera, a sequence of target images representing the calibration target in different target poses and at different acquisition times. The methods can include identifying reference images from the target images, and defining volume bins, angle bins and multi-camera bins into which the reference images are stored. The reference images can be used to determine the intrinsic and extrinsic parameters of the one or more cameras. In some implementations, the calibration methods can enable a user to monitor the progress of the calibration process, for example by providing an interactive calibration target including an input/output user interface to guide the user in real-time during the acquisition of the target images and/or sensors to provide positional information about the target poses.

Abstract: Calibration methods use a calibration target for obtaining the intrinsic and extrinsic camera parameters of one or more cameras are. The methods can include acquiring, with each camera, a sequence of target images representing the calibration target in different target poses and at different acquisition times. The methods can include identifying reference images from the target images, and defining volume bins, angle bins and multi-camera bins into which the reference images are stored. The reference images can be used to determine the intrinsic and extrinsic parameters of the one or more cameras. In some implementations, the calibration methods can enable a user to monitor the progress of the calibration process, for example by providing an interactive calibration target including an input/output user interface to guide the user in real-time during the acquisition of the target images and/or sensors to provide positional information about the target poses.