Abstract: A method for blur correction determines a first constant for a first pixel segment and a second constant for a second pixel segment for each of the multipliers that results in a first mean error for the first pixel segment being and a second mean error for the second pixel segment being within a mean error threshold. A first multiplier is selected from the multipliers with the corresponding first constant for the first pixel segment and second multiplier is selected from the multipliers with the corresponding second constant for the second segment that has the probability of the frames with the measured ranges being within the selected error threshold. A data file comprising the first multiplier, the corresponding first constant for the first multiplier, the second multiplier, and the corresponding second constant for the second multiplier is created for a platform computer system for a platform.

Abstract: Embodiments herein describe tracking the location and orientation of a target in a digital image. In an embodiment, this tracking can be used to control navigation for a vehicle. In an embodiment, a digital image can be captured by a visual sensor is received. A first array including a plurality of binary values related to the pixel velocity of a first plurality of pixels in the digital image as compared to corresponding pixels in a first one or more prior digital images can be generated. A second array including a plurality of values related to the standard deviation of pixel intensity of the first plurality of pixels in the digital image as compared to corresponding pixels in a second one or more prior digital images can be further generated. A plurality of thresholds relating to the values in the second array can be determined.

Abstract: Embodiments herein describe tracking the location and orientation of a target in a digital image. In an embodiment, this tracking can be used to control navigation for a vehicle. In an embodiment, a digital image can be captured by a visual sensor is received. A first array including a plurality of binary values related to the pixel velocity of a first plurality of pixels in the digital image as compared to corresponding pixels in a first one or more prior digital images can be generated. A second array including a plurality of values related to the standard deviation of pixel intensity of the first plurality of pixels in the digital image as compared to corresponding pixels in a second one or more prior digital images can be further generated. A plurality of thresholds relating to the values in the second array can be determined.

Abstract: Embodiments herein describe tracking the location and orientation of a target in a digital image. In an embodiment, this tracking can be used to control navigation for a vehicle. In an embodiment, a digital image can be captured by a visual sensor is received. A first array including a plurality of binary values related to the pixel velocity of a first plurality of pixels in the digital image as compared to corresponding pixels in a first one or more prior digital images can be generated. A second array including a plurality of values related to the standard deviation of pixel intensity of the first plurality of pixels in the digital image as compared to corresponding pixels in a second one or more prior digital images can be further generated. A plurality of thresholds relating to the values in the second array can be determined.

Abstract: Embodiments herein describe tracking the location and orientation of a target in a digital image. In an embodiment, this tracking can be used to control navigation for a vehicle. In an embodiment, a digital image can be captured by a visual sensor is received. A first array including a plurality of binary values related to the pixel velocity of a first plurality of pixels in the digital image as compared to corresponding pixels in a first one or more prior digital images can be generated. A second array including a plurality of values related to the standard deviation of pixel intensity of the first plurality of pixels in the digital image as compared to corresponding pixels in a second one or more prior digital images can be further generated. A plurality of thresholds relating to the values in the second array can be determined.

Abstract: A system is provided that includes an array of reflectors, a detector, and a processing unit. The array of reflectors is configured to be disposed on an object. The detector acquires range information and intensity information corresponding to the reflectors. The processing unit is coupled to the detector, and is configured to acquire the range information and the intensity information from the detector; determine locations of the reflectors using the intensity information; correlate the locations of the reflectors with the range information to provide correlated locations; and identify the object using the correlated locations.

Abstract: A method and apparatus for resolving a set of objects in an image of an area. A partition that captures a set of objects is identified using the image. The partition is comprised of a group of contiguous object pixels. A number of local max pixels are identified from the group of contiguous object pixels in the partition. A quantitative resolution of the set of objects captured in the partition is performed based on the number of local max pixels identified.

Abstract: A method and apparatus for resolving a set of objects in an image of an area. A partition that captures a set of objects is identified using the image. The partition is comprised of a group of contiguous object pixels. A number of local max pixels are identified from the group of contiguous object pixels in the partition. A quantitative resolution of the set of objects captured in the partition is performed based on the number of local max pixels identified.

Abstract: A method of navigating a space vehicle. An image of a planet surface is received. The received image is processed to identify edge pixels and angle data. The edge pixels and angle data are used to identify planetary features by shape, size, and spacing relative to other planetary features. At least some of the planetary features are compared with a predefined planet surface description including sizes and locations of planet landmarks. One or more matches are determined between the planetary feature(s) and the planet surface description. Based on the match(es), a location of the space vehicle relative to the planet is determined.

Abstract: A method and system for increasing the certainty of a silhouette matching process, where the process is being used for attitude determination of an object of interest, for example an aircraft. The method involves using one or more mask images that include structure or features that may or may not always be associated with the object of interest, and overlaying the mask image(s) onto a library image of the aircraft. Each pixel of the library image is compared against corresponding pixels of the mask image(s) to determine which pixels represent ambiguous areas of the library image. Those pixels are eliminated from consideration in determining a Fit score, where the Fit score represents a percentage value indicative of a certainty of the matching process in identifying the attitude of the aircraft. The method and system is applicable to a wide ranging variety of object detection applications.

Abstract: A method of navigating a space vehicle. An image of a planet surface is received. The received image is processed to identify edge pixels and angle data. The edge pixels and angle data are used to identify planetary features by shape, size, and spacing relative to other planetary features. At least some of the planetary features are compared with a predefined planet surface description including sizes and locations of planet landmarks. One or more matches are determined between the planetary feature(s) and the planet surface description. Based on the match(es), a location of the space vehicle relative to the planet is determined.

Abstract: A method and system for optically detecting an object within a field of view where detection is difficult because of background clutter within the field of view that obscures the object. A camera is panned with movement of the object to motion stabilize the object against the background clutter while taking a plurality of image frames of the object. A frame-by-frame analysis is performed to determine variances in the intensity of each pixel, over time, from the collected frames. From this analysis a variance image is constructed that includes an intensity variance value for each pixel. Pixels representing background clutter will typically vary considerably in intensity from frame to frame, while pixels making up the object will vary little or not at all. A binary threshold test is then applied to each variance value and the results are used to construct a final image. The final image may be a black and white image that clearly shows the object as a silhouette.

Abstract: A method and system for increasing the certainty of a silhouette matching process, where the process is being used for attitude determination of an object of interest, for example an aircraft. The method involves using one or more mask images that include structure or features that may or may not always be associated with the object of interest, and overlaying the mask image(s) onto a library image of the aircraft. Each pixel of the library image is compared against corresponding pixels of the mask image(s) to determine which pixels represent ambiguous areas of the library image. Those pixels are eliminated from consideration in determining a Fit score, where the Fit score represents a percentage value indicative of a certainty of the matching process in identifying the attitude of the aircraft. The method and system is applicable to a wide ranging variety of object detection applications.