Raytheon Company

Ratheon Company

Richard W Ely

Jody Dale Verret

Stephen J Raif

Grant B Boroughs

Torsten Staab

Corey J Collard

Steven B Seida

Thomas G Ribardo, Jr

Matthew A Schaefer

Allen Hainline

Jerry O Moore

Wyatt D Sharp, Iii

Mark J Graebner

Robert V Opalecky

Kevin M Grange

James G Hayes

Robert T Pierce

Robert M Willer

Kathryn A Welin

Ali J Arik

Devices, systems, and methods for three-dimensional (3D) evaluation point (3DEP) identification; wherein a method can include receiving a first conjugate point of a first real two-dimensional (2D) image, receiving a second conjugate point of a second real 2D image, the first and second conjugate points corresponding to a same geographical location, determining a first set of points of a 3D point set that project to within a specified distance of the first conjugate point in the first real 2D image, determining a second set of points of the 3D point set that project to within the specified distance of the second conjugate point in the second real 2D image, identifying a common point in both the first set of points and the second set of points that satisfies a specified heuristic relative to all other points in both the first set of points and the second set of points, and using the point as the 3DEP.

Extraction of 3D evaluation points for image conjugates

Subject matter regards generating a 3D point cloud and registering the 3D point cloud to the surface of the Earth (sometimes called 'geo-locating'). A method can include capturing, by unmanned vehicles (UVs), image data representative of respective overlapping subsections of the object, registering the overlapping subsections to each other, and geo-locating the registered overlapping subsections.

Collaborative 3D mapping and surface registration

A method can include identifying a geolocation of an object in an image, the method comprising receiving data indicating a pixel coordinate of the image selected by a user, identifying a data point in a targetable three-dimensional (3D) data set corresponding to the selected pixel coordinate, and providing a 3D location of the identified data point.

Image targeting via targetable 3D data

Discussed herein are devices, systems, and methods for multi-image ground control point (GCP) determination. A method can include extracting, from a first image including image data of a first geographical region, a first image template, the first image template including a contiguous subset of pixels from the first image and a first pixel of the first image indicated by the GCP, predicting a first pixel location of the GCP in a second image, the second image including image data of a second geographical overlapping with the first geographical region, extracting, from the second image, a second image template, the second image template including a contiguous subset of pixels from the second image and a second pixel corresponding to the pixel location, identifying a second pixel of the second image corresponding to a highest correlation score, and adding a second pixel location of the identified pixel to the GCP.

Automatic multi-image 3D ground control point extraction

Discussed herein are devices, systems, and methods for building mask generation. A method can include setting a respective pixel value of an image to a first specified value if the respective pixel corresponds, according to a three-dimensional (3D) point set, to an elevation greater than a specified Z threshold, otherwise setting the respective pixel value to a second, different specified value, grouping contiguous pixels set to the first specified value into one or more groups, determining a feature of each of the one or more groups, comparing the determined feature to a threshold and retaining the group if the feature is greater than a threshold, otherwise removing the group, and providing a building mask that includes pixels of the retained group set to a value and other pixels set to a different value.

Building mask generation from 3D point set

Discussed herein are devices, systems, and methods for image processing. A method can include generating a synthetic image based on a two-dimensional (2D) image of the geographical region, performing a coarse registration to grossly, register the synthetic image to the 2D image, and performing a fine registration following the coarse registration to improve the registration between the synthetic image and the 2D image.

Image registration to a 3D point set

A system receives digital images of a geographic location, associates each digital image with ground control points in a set of reference stereo images, and associates each digital image to each other digital image via image to image tiepoints. The system updates a geometry of each image via a bundle adjustment, and uses a prioritized stacking order to establish piecewise linear seam lines between each of the images. The system finally builds a prioritized map in a mosaic space specifying the source image pixels that are used in each region of the output mosaic, and forms the mosaic image using the prioritized map.

Ortho-image mosaic production system

Discussed herein are devices, systems, and methods for synthetic image generation. A method can include projecting a three-dimensional (3D) point set of a first geographical region to an image space of an image of a second geographical region to generate synthetic image data, identifying control points (CPs) between the image and the synthetic image data, adjusting a geometry of the synthetic image data based on the identified CPs, determining metadata for the synthetic image based on metadata of the image, and associating the determined metadata with the synthetic image data to generate the synthetic image.

Synthetic image generation from 3D-point cloud

A method can include registering a first image of a region to a three-dimensional (3D) point set of the region to generate a registered first image, registering a second image of the region to the 3D point set to generate a registered second image, identifying, based on the 3D point set, geometric tie points of the registered first image and the registered second image, projecting, using an affine transformation determined based on the identified geometric tie points, pixels of the registered first image to an image space of the registered second image to generate a registered and transformed first image, and displaying the registered and transformed first image and the registered second image simultaneously.

Multi-source image fusion

A method can include receiving, first and second digital elevation models (DEMs) including first elevation data of a geographic location and second elevation data of the geographic location, respectively, identifying differences between the first elevation data and the second elevation data that are greater than a first threshold, determining, for a point in the second elevation data identified to correspond to a difference greater than the first threshold, a slope of the geographic location around and including the point, and altering, in response to determining the difference is greater than a second threshold determined based on the determined slope, elevation data of the second DEM corresponding to the point.

Digital elevation model quality control

A method can include determining, based on elevation data of a geographic region corresponding to a location at which an image was captured and a solar elevation angle at a time the image was captured, whether each pixel of the image is a shadow or a non-shadow to create a shadow mask of the image, generating an eroded shadow mask that includes the shadow mask with a specified number of pixels from a perimeter of each shadow in the shadow mask changed to respective values corresponding to non-shadows, generating a dilated shadow mask that includes the specified number of pixels in the shadow mask changed to values corresponding to shadows, and refining the shadow mask using the eroded shadow mask and the dilated shadow mask to create a refined shadow mask.

Shadow mask generation using elevation data

Generally discussed herein are systems, apparatuses, and methods for moving object detection. In one or more embodiments, a method can include computing a central value for each color of each pixel in a series of frames from a static imaging device to create a static reference frame, computing a difference between pixel values of a first frame captured after the series of frames by the static imaging device and a central value of a corresponding pixel value in the static reference frame, comparing the computed differences to a difference threshold, aggregating contiguous pixels corresponding to differences greater than the difference threshold into contiguous segments of pixels, and determining if the contiguous segments correspond to a moving object by registering the contiguous segments to pixels of a frame preceding the first frame.

Systems and methods for 3D point cloud processing

Generally discussed herein are systems, apparatuses, and methods for. In one or more embodiments, a method can include recording imaging device external orientation parameters of a rigidly mounted imaging device, capturing a reference frame using the imaging device in an orientation corresponding to the recorded parameters, capturing a later frame after the reference frame, correlating the later frame to the reference frame to determine pixel orientation in the reference frame relative to pixel orientation in the later frame, determining an imaging device movement required to return the imaging device back to the orientation associated with the recorded imaging device external orientation parameters based on the determined pixel orientation in the reference frame relative to the pixel orientation in the later frame, and updating the imaging device external orientation parameters to account for the determined imaging device movement in accord with the imaging device external orientation parameters.

System and method for imaging device motion compensation

A system and methods can create a synthetic image of a target from a 3D data set, by using an electro-optical (EO) image and sun geometry associated with the EO image. In some examples, a 3D surface model is created from a 3D data set. The 3D surface model establishes a local surface orientation at each point in the 3D data set. A surface shaded relief (SSR) is produced from the local surface orientation, from an EO image, and from sun geometry associated with the EO image. Points in the SSR that are in shadows are shaded appropriately. The SSR is projected into the image plane of the EO image. Edge-based registration extracts tie points from the projected SSR. The 3D data set converts the tie points to ground control points. A geometric bundle adjustment aligns the EO image geometry to the 3D data set.

System and method for automatically registering an image to a three-dimensional point set

Method for recognizing boats in a port includes: establishing land/water boundaries from an image of the port including one or more boats, using a land mask and image statistics, without requiring a site model; utilizing the established land/water boundaries to determine thresholds and normalization parameters; detecting one or more bow candidates for the boats in the image; computing an orientation of each of the boats with detected bows; computing features along the bow candidates to eliminate false bow candidates; fitting boat models to the detected one or more bow candidates; determining one or more best fit models from the fitted boat models; calculating features in an interior, exterior and outline of the best fit models; applying a statistical classifier to score each of the one or more best fit models and to eliminate false alarms; and eliminating lower scoring best fit models.

Growing community of inventors

Lewisville, TX, United States of America

Richard W Ely