Westinghouse Electric Corporation

Northrop Grumman Systems Corporation

Philipp F Schweizer

Walter J Petlevich, Jr

Paul H Haley

Barry F Cooney

James J Oravec

Ellen K Hughes

Jean A Seidel

William P Zachar

An automated system for identifying nuclear power plant components includes a camera which provides an input signal from an image of the nuclear power plant components. A digitizer generates a digitized image of the nuclear power plant components from the input signal. A workstation locator routine locates the component identifier of the nuclear power plant components from pixel elements of the digitized image and a workstation determining routine determines the component identifier. The determining routine includes plural recognizer routines each having an output providing an intermediate recognition of the component identifier, and a combining routine combining the outputs of the recognizer routines to recognize the component identifier. Each of the outputs includes an intermediate identifier and a corresponding confidence value.

Nuclear power plant component identification and verification system and

A method and apparatus for classifying objects in images utilize means for selecting portions of those images which contain objects and means for classifying those objects based upon parameters of the selected portions, which parameters are useful for classifying the objects. The selecting means preferably is a shadow and highlight detector, a statistical window detector and a neural network window detector whose output is combined in a combined cuer. The parameters are determined from the greylevels and positions of pixels using one or more modules which perform certain mathematical operations on this data. Such parameters include edge parameters, smoothness, clutter, presence and characteristics of highlights and shadows, and texture. The invention is particularly useful for classifying objects in sonar images as natural or man-made.

Method and apparatus for classifying objects in sonar images

A method and apparatus for automatically identifying targets in sonar images utilizes three processing systems which preferably operate simultaneously. After the image has been filtered and Fourier transformed, a highlight-shadow detector classifies portions of the image as a highlight, a shadow or background according to greyness levels of pixels in such portions. A statistical cuer selects those portions which have been classified as a highlight or a shadow. A neural network then classifies the sets of highlight and shadow reports as targets or background.

Method and apparatus for automatically identifying targets in sonar

To detect and classify features in a sonar image comprised of a matrix of pixels each pixel having a known greyness level a set of windows is defined such that each window is comprised of a set of adjacent pixels such that every pixel of the image is included in at least one window. Properties of the greyness level for the pixels in each window are defined to enable selection of those windows having specific features of greyness level in excess of a predetermined threshold. Matched filter correlations are performed on the pixels in the selected windows to identify highlights and shadows. The identifying highlights and shadows are classified as targets, anomalies or background.

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Monroeville, PA, United States of America

Philipp F Schweizer