E.i. Dupont De Nemours and Company

James Arthur Whitcomb

Akhileswar G Vaidyanathan

Michael Joseph Piovoso

Aaron J Owens

Mark David Wetzel

Akhileswar Ganesh Vaidyanathan

A distributed hierarchical evolutionary modeling and visualization of empirical data method and machine readable storage medium for creating an empirical modeling system based upon previously acquired data. The data represents inputs to the systems and corresponding outputs from the system. The method and machine readable storage medium utilize an entropy function based upon information theory and the principles of thermodynamics to accurately predict system outputs from subsequently acquired inputs. The method and machine readable storage medium identify the most information-rich (i.e., optimum) representation of a data set in order to reveal the underlying order, or structure, of what appears to be a disordered system. Evolutionary programming is one method utilized for identifying the optimum representation of data.

Distributed hierarchical evolutionary modeling and visualization of empirical data

When identifying an object in an image, very often redundant identifications of the object may occur. The present invention relates to methods of image analysis for resolving such redundant identifications of an object. More specifically, the present invention relates to object identification schemes which obtain multiple representations of the same object. In addition, such identification schemes may identify void, or donut-shaped objects when a single search, or multiples searches, of the image are performed. Further, the present invention is useful in resolving several distinct objects where other identification schemes employing either single or multiple searching have identified these distinct objects as a single clumped object. The present invention may be used, for instance, to determine whether a smaller object is contained within another, larger object.

Method for resolving redundant identifications of an object

Image analysis methods and systems are used for identifying objects in a background by defining a data space, such as a histogram or a color space. The data space comprises a plurality of sub-spaces, which could be selected based, for example, on a histogram, or on the way pixel values or color parameters cluster in their respective spaces. The threshold values in the data space are selected, a list of all ordered pairs of thresholds is generated to define multiple data sub-spaces, and the image is multiply searched, once in each sub-space for at least one representation of a candidate object, where the candidate object has at least one predetermined attribute value. Valid objects are identified by comparing the candidate object attribute values to a defined set of valid object attribute values contained in a driver.

Adaptive vision system using dual thresholding

The present invention relates to a method of designing a Kaiser window filter. The filter is applied to an input image to generate a resized output image while preserving the information content of the input image. The method of mapping the output image space to the input image space of the present invention aligns the boundaries of the input image space with the boundaries of a set of new input pixels, and the coordinate points of the output pixels map to corresponding coordinate points of the new input pixels.

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Wilmington, DE, United States of America

James Arthur Whitcomb