Method of object recognition in image data using combined edge magnitude and edge direction analysis techniques
First Claim
1. A method for identifying targets by detecting and analyzing hotspots in 2-dimensional infrared (IR) images through the combined use of Sobel magnitude and Sobel direction images comprising:
- receiving as input an infra-red (IR) intensity image, a thresholded Sobel magnitude image, a thresholded Sobel direction image, and target size parameters;
detecting potential targets by morphologically processing said Sobel magnitude image and combining it with said intensity image to create a masked image including detected hotspots;
filtering out incorrectly detected hotspots by checking each hotspot for a minimum number of distinct directions in said Sobel direction image and removing potential distortions from horizon or littoral lines; and
determining if the filtered hotspots are valid targets by evaluating the filtered hotspots against at least one of intensity, size, or width thresholds contained in said target size parameters, and discarding as non-targets the filtered hotspots that are not within a certain percentage of said thresholds.
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Abstract
Methods for detecting areas of interest in an image using combined edge magnitude and edge direction analysis techniques are presented. One embodiment features using thermal imaging data to detect hotspots in maritime settings that may be potential targets for tracking or weapons systems. The edge magnitude and edge direction data are derived from the intensity image and then combined with the intensity image and analyzed morphologically to remove noise and background elements. The combined image data is then selectively filtered to remove horizontal non-target elements and then analyzed further against target size information to determine which detected and analyzed hotspots are valid targets. Another embodiment features receiving as input an intensity image along with its associated edge magnitude and edge direction images, which have both been created by a means outside the detection method. Yet another embodiment features a detection method that does not selectively filter out horizontal image elements.
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Citations
25 Claims
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1. A method for identifying targets by detecting and analyzing hotspots in 2-dimensional infrared (IR) images through the combined use of Sobel magnitude and Sobel direction images comprising:
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receiving as input an infra-red (IR) intensity image, a thresholded Sobel magnitude image, a thresholded Sobel direction image, and target size parameters; detecting potential targets by morphologically processing said Sobel magnitude image and combining it with said intensity image to create a masked image including detected hotspots; filtering out incorrectly detected hotspots by checking each hotspot for a minimum number of distinct directions in said Sobel direction image and removing potential distortions from horizon or littoral lines; and determining if the filtered hotspots are valid targets by evaluating the filtered hotspots against at least one of intensity, size, or width thresholds contained in said target size parameters, and discarding as non-targets the filtered hotspots that are not within a certain percentage of said thresholds.
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2. A method for detecting areas of interest in an image by combining edge magnitude and direction information comprising:
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receiving input image data and detection parameters; detecting potential areas of interest using edge magnitude by morphologically processing said magnitude image to fill gaps and eliminate noise from the image data; combining said morphologically processed image and original intensity image into a masked image; and identifying and counting said areas of interest in said masked image; filtering out incorrectly identified areas of interest using edge direction and edge magnitude; and determining if said areas of interest meet said detection parameters. - View Dependent Claims (3, 4, 5, 6, 7, 8, 9, 10, 11, 12)
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13. A method for detecting areas of interest in an image by combining edge magnitude and direction information comprising:
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receiving input image data and detection parameters; detecting potential areas of interest using edge magnitude; filtering out incorrectly identified areas of interest using edge direction and edge magnitude by evaluating a detected area of interest with a directional constraints filter; removing background elements from said detected area of interest; enhancing said detected area of interest with a weighting factor; and performing a convolution on said detected area of interest with a predefined kernel; and determining if said areas of interest meet said detection parameters. - View Dependent Claims (14, 15, 16, 17, 18, 19, 20)
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21. A method for detecting areas of interest in an image by combining edge magnitude and direction information comprising:
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receiving input image data and detection parameters; detecting potential areas of interest using edge magnitude; filtering out incorrectly identified areas of interest using edge direction and edge magnitude; and determining if said areas of interest meet said detection parameters by applying threshold parameters to remaining detected areas of interest; calculating centroid, size, and width of said remaining detected areas of interest; and discarding said remaining detected areas of interest which do not meet size a threshold; where said applying threshold parameters to remaining detected areas of interest includes identifying and counting said remaining detected areas of interest using morphological image analysis to identify contiguous groups of pixels; and generating and applying a threshold value to said remaining detected areas of interest if; more than one said detected area of interest remains, horizontal pixels were removed from said remaining detected area of interest, said removing background elements from said detected area of interest process indicated the presence of removable background elements in said image data, or said detection parameters indicate the presence of known areas of interest separated in said image data by less than the size of one of said known areas of interest. - View Dependent Claims (22, 23, 24)
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25. A non-transitory computer readable medium having stored thereon a computer executable program for detecting areas of interest in an image, comprising:
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receiving input image data and detection parameters; detecting magnitude and direction of edges in said image to generate a magnitude image and a direction image; detecting potential areas of interest using edge magnitude by morphologically processing said magnitude image to fill gaps and eliminate noise from the image data; combining said morphologically processed image and original intensity image into a masked image; and identifying and counting said areas of interest in said masked image; filtering out incorrectly identified areas of interest using edge direction and edge magnitude; and determining if said areas of interest meet said detection parameters.
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Specification