Abstract: A system for processing video signal information to identify those pixels associated with a moving object in the presence of platform and/or sensor pointing induced motion. Frame differencing with self-adjusting noise thresholds is implemented to detect pixels associated with objects that are in motion with respect to the background and a field-by-field motion pixel map of pixels associated with the moving object is generated. A two (2) step pixel grouping process is used where the first pass runs in real-time as the video signal is received and writes the links between pixel groups into entries in a table. The second pass operates on a smaller set of link data and only needs to reorder entries in the table.

Abstract: A system for processing video signal information to identify those pixels associated with a moving object in the presence of platform and/or sensor pointing induced motion. Frame differencing with self-adjusting noise thresholds is implemented to detect pixels associated with objects that are in motion with respect to the background and a field-by-field motion pixel map of pixels associated with the moving object is generated. A two (2) step pixel grouping process is used where the first pass runs in real-time as the video signal is received and writes the links between pixel groups into entries in a table. The second pass operates on a smaller set of link data and only needs to reorder entries in the table.

Abstract: A system for processing video signal information to identify those pixels associated with a moving object in the presence of platform and/or sensor pointing induced motion. Frame differencing with self-adjusting noise thresholds is implemented to detect pixels associated with objects that are in motion with respect to the background and a field-by-field motion pixel map of pixels associated with the moving object is generated. A two (2) step pixel grouping process is used where the first pass runs in real-time as the video signal is received and writes the links between pixel groups into entries in a table. The second pass operates on a smaller set of link data and only needs to reorder entries in the table.

Abstract: A system for processing video signal information to identify those pixels associated with a moving object in the presence of platform and/or sensor pointing induced motion. Frame differencing with self-adjusting noise thresholds is implemented to detect pixels associated with objects that are in motion with respect to the background and a field-by-field motion pixel map of pixels associated with the moving object is generated. A two (2) step pixel grouping process is used where the first pass runs in real-time as the video signal is received and writes the links between pixel groups into entries in a table. The second pass operates on a smaller set of link data and only needs to reorder entries in the table.

Abstract: A method for integrating image frames includes receiving for each pixel in at least one image frame a value representative of a sensor measurement. The method includes calculating an average difference of the value representative of the sensor measurement over a subset of the plurality of image frames. The method includes detecting motion in at least one pixel of the image frame in the plurality of image frames based on the calculated average difference of the value representative of the sensor measurement over the subset of the plurality of image frames. The method also includes generating an integrated image frame wherein each pixel having detected motion is integrated by an amount less than that of those pixels for which motion is not detected. The amount of frame integration is based on contrast levels, expected rates of motion, and noise characteristics of sensor input image data.

Abstract: A method for integrating image frames includes receiving for each pixel in at least one image frame a value representative of a sensor measurement. The method includes calculating an average difference of the value representative of the sensor measurement over a subset of the plurality of image frames. The method includes detecting motion in at least one pixel of the image frame in the plurality of image frames based on the calculated average difference of the value representative of the sensor measurement over the subset of the plurality of image frames. The method also includes generating an integrated image frame wherein each pixel having detected motion is integrated by an amount less than that of those pixels for which motion is not detected. The amount of frame integration is based on contrast levels, expected rates of motion, and noise characteristics of sensor input image data.

Abstract: An image registration system for aligning first and second images. The novel system includes a first system for extracting a region of interest (ROI) from each image and a second system for coarsely aligning the regions of interest. The first system determines the size and location of the ROI based on the number of features contained within the region. The size of the ROI is enlarged until a number of features contained in the ROI is larger than a predetermined lower bound or until the size is greater than a predetermined upper bound. The second system computes a cross-correlation on the regions of interest using a plurality of transforms to find a coarse alignment transform having a highest correlation. The image registration system may also include a third system for performing sub-pixel alignment on the regions of interest.

Abstract: The technology described herein includes integration of image frames. The integration of image frames includes determining a sharpness metric for each image frame in a plurality of image frames. The sharpness metric is indicative of at least one of edge content and an edge size of the image frame. The integration of image frames further includes determining a noise metric for each image frame in the plurality of image frames. The noise metric is indicative of a variation in brightness or color in the image frame. The integration of image frames further includes determining a jitter metric for each image frame in the plurality of image frames. The jitter metric is indicative of spatial shifts between the image frame and other image frames in the plurality of image frames. The integration of image frames further includes generating an integrated image frame from one or more of the plurality of image frames based on the sharpness metric, the noise metric, and the jitter metric.

Abstract: An image registration system for aligning first and second images. The novel system includes a first system for extracting a region of interest (ROI) from each image and a second system for coarsely aligning the regions of interest. The first system determines the size and location of the ROI based on the number of features contained within the region. The size of the ROI is enlarged until a number of features contained in the ROI is larger than a predetermined lower bound or until the size is greater than a predetermined upper bound. The second system computes a cross-correlation on the regions of interest using a plurality of transforms to find a coarse alignment transform having a highest correlation. The image registration system may also include a third system for performing sub-pixel alignment on the regions of interest.

Abstract: An image registration system for aligning first and second images. The novel system includes a first system for extracting a region of interest (ROI) from each image and a second system for coarsely aligning the regions of interest. The first system determines the size and location of the ROI based on the number of features contained within the region. The size of the ROI is enlarged until a number of features contained in the ROI is larger than a predetermined lower bound or until the size is greater than a predetermined upper bound. The second system computes a cross-correlation on the regions of interest using a plurality of transforms to find a coarse alignment transform having a highest correlation. The image registration system may also include a third system for performing sub-pixel alignment on the regions of interest.

Abstract: The technology described herein includes integration of image frames. The integration of image frames includes determining a sharpness metric for each image frame in a plurality of image frames. The sharpness metric is indicative of at least one of edge content and an edge size of the image frame. The integration of image frames further includes determining a noise metric for each image frame in the plurality of image frames. The noise metric is indicative of a variation in brightness or color in the image frame. The integration of image frames further includes determining a jitter metric for each image frame in the plurality of image frames. The jitter metric is indicative of spatial shifts between the image frame and other image frames in the plurality of image frames. The integration of image frames further includes generating an integrated image frame from one or more of the plurality of image frames based on the sharpness metric, the noise metric, and the jitter metric.

Abstract: An image registration system for aligning first and second images. The novel system includes a first system for extracting a region of interest (ROI) from each image and a second system for coarsely aligning the regions of interest. The first system determines the size and location of the ROI based on the number of features contained within the region. The size of the ROI is enlarged until a number of features contained in the ROI is larger than a predetermined lower bound or until the size is greater than a predetermined upper bound. The second system computes a cross-correlation on the regions of interest using a plurality of transforms to find a coarse alignment transform having a highest correlation. The image registration system may also include a third system for performing sub-pixel alignment on the regions of interest.

Abstract: An image processing system and method. The image processing system acquires a first set of scan lines in at first field of image data and a second set of scan lines in a second field of image data; the second set of scan lines are interlaced relative to the first set of scan lines and performs a line by line correlation therebetween to provide an error signal or value. The first and second fields are buffered and coupled to a line-to-line correlator. The error signal is used to adjust either the first or the second set of scan lines to correct for skew or blur in the second field of image data.

Abstract: An imaging system and method with an arrangement for sensing the performance of an optical system and providing data in response thereto and electronically correcting nonuniformity in the performance of the optical system in response thereto. In the illustrative application, the nonuniformity is a porthole effect. In the preferred embodiment, the arrangement for correcting includes an arrangement for providing an inverse distortion to an output of the system electronically. The inverse distortion is applied by generating a plurality of spatial correction coefficients from the performance data, storing the coefficients and applying the coefficients to current data from the optical system. The spatial correction coefficients are statistically generated gain and level correction defect maps. The present teachings should enable a correction of optical distortion in nonideal electro-optical systems without requiring additional optics.

Abstract: An image registration system for aligning first and second images. The novel system includes a first system for extracting a region of interest (ROI) from each image and a second system for coarsely aligning the regions of interest. The first system determines the size and location of the ROI based on the number of features contained within the region. The size of the ROI is enlarged until a number of features contained in the ROI is larger than a predetermined lower bound or until the size is greater than a predetermined upper bound. The second system computes a cross-correlation on the regions of interest using a plurality of transforms to find a coarse alignment transform having a highest correlation. The image registration system may also include a third system for performing sub-pixel alignment on the regions of interest.