Morpho-Hawk applies projectional mathematical morphology in a fundamentally new way. Morpho-Hawk extracts object information from digital images by filtering with simple geometrical figures such as rectangles or simple curves. Two core algorithms are used to accomplish this: 1) Object Detection and Feature Extraction from Images: Using projectional morphology, Morpho-Hawk finds features of interest within an image, such as contours, shapes, colors, infrared spectra, and more. Because Morpho-Hawk identifies features based on shape or form, the method can uniquely handle different image or object conditions and directions. MorphoHawk can evaluate all possible images of the analyzed scene using the same transformations that are applied just to one image of that scene. Hence, recognizing a defect within an image or part of an image provides information to assist in recognizing other objects in the image. In addition, known background image information can be morphologically subtracted out in order to focus on the appearance or disappearance of the features of interest. 2) Object Analysis: Upon detection of an object of interest, Morpho-Hawk can analyze the object based learned information from prior images or end-user defined criteria for certain object of special interest by selecting feature (e.g., color, size, shape, apparent volume) and/or symbol (e.g., letter, number). The novel algorithm can analyze targeted objects, even as conditions of such as illumination, shadows, and spectral regions change strongly in following images as compared to the original image. This enables robust recognition, interpolation and prediction. As the analysis shows the presence of these features in the optical signal, the algorithm can make a determination based on user defined probability. The object information can be stored for later analysis using simple morphological data compression methods and/or retention of the original optical images.
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@misc{osti_1232366,
title = {Morpho-Hawk, Version 00},
author = {Glazoff, Michael V. and Gering, Kevin L. and Pytev, Yuri P.},
abstractNote = {Morpho-Hawk applies projectional mathematical morphology in a fundamentally new way. Morpho-Hawk extracts object information from digital images by filtering with simple geometrical figures such as rectangles or simple curves. Two core algorithms are used to accomplish this: 1) Object Detection and Feature Extraction from Images: Using projectional morphology, Morpho-Hawk finds features of interest within an image, such as contours, shapes, colors, infrared spectra, and more. Because Morpho-Hawk identifies features based on shape or form, the method can uniquely handle different image or object conditions and directions. MorphoHawk can evaluate all possible images of the analyzed scene using the same transformations that are applied just to one image of that scene. Hence, recognizing a defect within an image or part of an image provides information to assist in recognizing other objects in the image. In addition, known background image information can be morphologically subtracted out in order to focus on the appearance or disappearance of the features of interest. 2) Object Analysis: Upon detection of an object of interest, Morpho-Hawk can analyze the object based learned information from prior images or end-user defined criteria for certain object of special interest by selecting feature (e.g., color, size, shape, apparent volume) and/or symbol (e.g., letter, number). The novel algorithm can analyze targeted objects, even as conditions of such as illumination, shadows, and spectral regions change strongly in following images as compared to the original image. This enables robust recognition, interpolation and prediction. As the analysis shows the presence of these features in the optical signal, the algorithm can make a determination based on user defined probability. The object information can be stored for later analysis using simple morphological data compression methods and/or retention of the original optical images.},
doi = {},
url = {https://www.osti.gov/biblio/1232366},
year = {Thu Sep 10 00:00:00 EDT 2015},
month = {Thu Sep 10 00:00:00 EDT 2015},
note =
}
