Harris Corporation

J Harlan Yates

Mark Donald Rahmes

Timothy B Faulkner

Michael McGonagle

Patricia Brown

Robert Haden

Tariq Bakir

Stephen Connetti

Stephan Walker Miller

Rufus Williams

Systems () and methods () for efficient comparative non-spatial image data analysis. The methods involve ranking a plurality of non-spatial images () based on at least one first attribute thereof; generating a screen page (-) comprising an array () defined by a plurality of cells () in which at least a portion of the non-spatial images are simultaneously presented; and displaying the screen page in a first GUI window () of a display screen. Each cell comprises only one non-spatial image. The non-spatial images are presented in an order defined by the ranking thereof.

Systems and methods for efficient comparative non-spatial image data analysis

Systems () and methods () for efficient video analysis. The methods involve: automatically identifying features of at least one feature class which are contained in a first video stream; simultaneously generating a plurality of first video chips () using first video data defining the first video stream; displaying an array comprising the first video chips within a graphical user interface window; and concurrently playing the first video chips. Each of the first video chips comprises a segment of the first video stream which includes at least one identified feature.

Systems and methods for efficient video analysis

Systems () and methods () for efficient spatial feature data analysis. The methods involve simultaneously generating chip images using image data defining at least a first image and video chips using video data defining at least a first video stream. Thereafter, an array is displayed which comprises grid cells in which at least a portion of the chip images is presented, at least a portion of the video chips is presented, or a portion of the chip images and a portion of the video chips are presented. Each chip image comprises a panned-only view, a zoomed-only view, or a panned-and-zoomed view of the first image including a visual representation of at least one first object of a particular type. Each of the video chips comprises a segment of the first video stream which include a visual representation of at least one second object of the particular type.

Systems and methods for efficient feature based image and video analysis

Systems () and methods () for efficiently and accurately detecting changes in feature data. The methods generally involve: determining first vectors for first features extracted from a first image using pixel information associated therewith; comparing the first vectors with second vectors defined by spatial feature data; classifying the first features into a plurality of classes based on the results of the vector comparisons; and analyzing the first image to determine if any one of the first features of at least one of the plurality of classes indicates that a relevant change has occurred in relation to an object represented thereby.

Systems and methods for efficiently and accurately detecting changes in spatial feature data

Systems () and methods () for efficient spatial feature data analysis. The methods involve simultaneously generating two or more chip images () using image data defining a first image (); concurrently displaying an array () comprising all or a portion of the chip images in a plug-in window (); and displaying in the plug-in window information relating to an attribute (a, a) of a feature (A) contained in a selected one of the displayed chip images (). The chip images are generated in response to a user selection of a feature (A) contained in at least a portion of the first image displayed in an application window (). Each of the chip images comprises a panned view, a zoomed view, or a panned-and-zoomed view of the first image including one or more features of a user-selected feature class.

Systems and methods for efficient spatial feature analysis

Systems () and methods () for efficient feature data analysis. The methods involve: determining a first number of screen pages needed to verify that each of a plurality of clusters of detected features comprises only detected features which were correctly identified during feature extraction/detection operations as being of the same feature class as a selected feature of an image; determining a second number of screen pages needed to verify that each of a plurality of singular detected features was correctly identified during the feature extraction/detection operations as being of the same feature class as the selected feature of the image; selecting one of a plurality of different validation processes based on values of the first number of screen pages and the second number of screen pages; and performing the selected validation process to verify that each of the detected features does not constitute a false positive.

Systems and methods for efficient feature extraction accuracy using imperfect extractors

A geospatial modeling system may include a geospatial database for storing geospatial data points each having respective elevations associated therewith. The system may further include a processor for data thinning the geospatial data points by selecting discriminant points therefrom. The processor may select discriminant points based upon: (a) identifying a current patch having a plurality of spaced-apart current geospatial data boundary points; (b) selecting a current discriminant point within the current patch based upon sequentially stepping through the geospatial data points therein to determine respective elevation differences relative to the current geospatial boundary points and comparing the elevation differences to a threshold range; (c) identifying a plurality of new patches, each having a plurality of spaced-apart new geospatial boundary points including the current discriminant point; and (d) iteratively repeating steps (b) and (c) for each of the new patches until no further new discriminant point is selected.

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Melbourne, FL, United States of America

J Harlan Yates