An image capture device may capture a video while experiencing motion. Motion of the image capture device during video capture may be determined based on optical flow and structure from motion solve of the video. Optical flow derived motion and/or the motion solve derived motion of the image capture device may be selected to perform stabilization of the video.

An image capture device may capture a video while experiencing motion. Motion of the image capture device during video capture may be determined based on optical flow and structure from motion solve of the video. Optical flow derived motion and/or the motion solve derived motion of the image capture device may be selected to perform stabilization of the video.

An image processing apparatus extracts a foreground image corresponding to an object included in a processing image using a background image corresponding to the processing image, and generates the background image from the processing image. The image processing apparatus determines whether it is allowed to update the background image for use in the extraction, and based on a result of the determination, updates the background image for use in the extraction using the generated background image.

An image processing apparatus extracts a foreground image corresponding to an object included in a processing image using a background image corresponding to the processing image, and generates the background image from the processing image. The image processing apparatus determines whether it is allowed to update the background image for use in the extraction, and based on a result of the determination, updates the background image for use in the extraction using the generated background image.

A method for recognizing image artifacts in a chronological sequence of recordings recorded by means of a lighting device and an optical sensor is disclosed. A difference movement field is obtained by removing from a movement field all movement field vectors to be expected due to inherent movement of a lighting device and an optical sensor. The movement field vectors in the difference movement field are combined into one or more objects according to at least one grouping criterion. The objects in the image are classified as plausible or as implausible pursuant to a movement plausibility test. An object classified as implausible is recognized as an image artifact.

A computer-implemented system and method of image cross-correlation improves the sub-pixel accuracy of the correlation surface and subsequent processing thereof. One or both of the template or search windows are resampled using the fractional portions of the correlation offsets X and Y produced by the initial image cross-correlation. The resampled window is then correlated with the other original window to produce a resampled cross-correlation surface. Removing the fractional or sub-pixel offsets between the template and search windows improves the “sameness” of the represented imagery thereby improving the quality and accuracy of the correlation surface, which in turn improves the quality and accuracy of the FOM or other processing of the correlation surface. The process may be iterated to improve accuracy or modified to generate resampled cross-correlation surfaces for multiple possible offsets and to accept the one with the most certainty.

A computer-implemented system and method of image cross-correlation improves the sub-pixel accuracy of the correlation surface and subsequent processing thereof. One or both of the template or search windows are resampled using the fractional portions of the correlation offsets X and Y produced by the initial image cross-correlation. The resampled window is then correlated with the other original window to produce a resampled cross-correlation surface. Removing the fractional or sub-pixel offsets between the template and search windows improves the “sameness” of the represented imagery thereby improving the quality and accuracy of the correlation surface, which in turn improves the quality and accuracy of the FOM or other processing of the correlation surface. The process may be iterated to improve accuracy or modified to generate resampled cross-correlation surfaces for multiple possible offsets and to accept the one with the most certainty.

A method for determining a location of a trailer in an image includes obtaining at least one real-time image from a vehicle. The at least one real-time image is processed with a controller on the vehicle to obtain a feature patch describing at least one real-time image. A convolution is performed of the feature patch and each filter from a set of filters with the filter being based on data representative of known trailers. A location of a trailer is determined in the at least one real-time image based on the convolution between the feature patch and each filter from the set of filters.

A speed estimation system includes: a detection module configured to: detect an object on a surface in an image captured using a camera; and generate a bounding box around the object; a Jacobian module configured to generate a Jacobian for the object based on the bounding box; and a speed module configured to determine a speed that the object is traveling on the surface based on the Jacobian.

A speed estimation system includes: a detection module configured to: detect an object on a surface in an image captured using a camera; and generate a bounding box around the object; a Jacobian module configured to generate a Jacobian for the object based on the bounding box; and a speed module configured to determine a speed that the object is traveling on the surface based on the Jacobian.