Xerox Corporation

Barbara Lynn Farrell

Xing Li

Ryan David Metcalfe

Peter D McCandlish

Jeng-nan Shiau

Meng Yao

Ramesh Nagarajan

Francis Kapo Tse

David Jon Metcalfe

John L Rourke

Robert F Buchheit

Thomas I Yeh

Richard S Fox

George W Lahue

Andre M Blaakman

Methods and systems for performing resolution expansion with high quality edge enhancement capability utilizing an edge detector and a color estimator. An edge tag with respect to an input image can be detected via an edge detector and an edge detection approach. A resolution expansion can be separately applied to the edge tag and a binary output generated by other processing functions. Multiple output lines corresponding to the input image from the color estimator with different threshold array values for an edge pixel can be generated by a halftoning module. A binary domain resolution expansion can be applied for a non-edge pixel. A final output for each pixel can be selected from a resolution expanded result and/or from the result of the halftoning for the edge based on the edge tag with respect to that pixel.

Method and system for performing resolution expansion with high quality edge enhancement

What is disclosed is a system and method for determining an orientation direction of a color edge at a given pixel location in a binary color image. The orientation direction of the color edge is determined from eight pixel counts with each pixel count being a total number of pixels in each of eight regions of a window centered about a candidate pixel which resides along the color edge. The eight regions are associated with 8 compass points. The orientation of the edge is determined by a 1, 2and 3tier control bits which are based upon the pixel counts of each region. The 1, 2and 3tier control bits collectively form a 3-bit word. The 3-bit word defines the orientation direction. The teachings hereof provide an efficient way of performing binary edge orientation detection by making uses of intermediate results to simultaneously encode the edge orientation.

Determining an orientation direction of a color edge at a pixel location in a color image

Disclosed herein is a method for detecting thin lines in image data. The method is performed by a processor to process contone image data. The processing includes thresholding a window of pixels established in the contone domain to generate a binary window of image data, and then determining characteristics associated with on pixels or runs of the binary data. The characteristics (start and end locations, length of on runs) are then thresholded. The processing in the contone and binary domain are used to determine if a thin line exists in the window of image data. The disclosed method produces better quality output images and reduces the addition of false lines in an image.

Thin line detection in scanned image data

Disclosed herein is a method for detecting thin lines in image data. The method is performed by a processor to process contone image data. The processing includes thresholding a window of pixels using a first set of thresholds established in the contone domain, and then counting and thresholding the binary pixels using a second set of thresholds. The processing in the contone and binary domain are used to determine if a thin line exists and if a pixel of interest in the window is an edge pixel that is part of a thin line. The disclosed method produces better quality output images and reduces the addition of false lines in an image.

Fuzzy method to detect thin lines in scanned image

Disclosed herein is a method for detecting thin lines in image data. The method is performed by a processor to process contone image data. The processing includes combining a first result of thin line detection using a first method and a second result of thin line detection using a second method to produce an improved thin line determination in the image data. The methods include processing and thresholding in the contone and binary domain to determine if a thin line exists in the window of image data. The thin line determination may also be merged with the image data as processed using other image segmentation techniques. The disclosed method produces better quality output images and reduces the addition of false lines in an image.

Image segmentation system and method with improved thin line detection

A method for identifying document edges collects a predetermined number scanlines of the document. Both first order and second order statistics are then calculated based on the scanned data. A first slope value being a function of the difference between a plurality of the first value and a second slope value being a function of the difference between a plurality of the second value are calculated. A document edge is detected using the first and second slope values.

System and method for automatically detecting edges of scanned documents

A method for identifying document edges collects a predetermined number scanlines of the document against a light backing and a dark backing. A first set of first-order statistics are calculated from the image data obtained by scanning the document against the light backing. A second set of first-order statistics are calculated from the image data obtained by scanning the document against the dark backing. A document edge is detected using the first and second sets of first-order statistics.

An image desired to be reproduced is scanned to determine its video pixel grey values. A histogram generator generates a histogram distribution representing a frequency of the grey values. The histogram distribution is analyzed to determine a minimum and maximum input grey values which define input boundaries. A segment point is computed between the input boundaries based on the histogram data. The segment point defines a plurality of input segments between the input boundaries. A dynamic output range is selected. Each input segment is mapped to an output segment based on a linear transformation for the corresponding segment. In this manner, a tone reproduction curve map having a piecewise linear transformation is automatically generated from the image histogram data.

Automatic method for determining piecewise linear transformation from an image histogram

An image desired to be reproduced is scanned to determine its video pixel gray values. A histogram generator generates a histogram distribution representing a frequency of the gray values. The histogram distribution is analyzed to determine a background peak gray value of the image and a standard deviation of the histogram distribution based on a Gaussian approximation. A thresholding circuit dynamically adjusts the background peak value based on the standard deviation and a selected scaling factor to generate a background threshold value. The background threshold value expands a range of background gray values in the image which are eliminated during image reproduction. Eliminating substantially all background gray values improves the quality of the reproduced image.

System and method for eliminating background pixels from a scanned image

The background grey-level of a document is determined based on the gain of the document. A histogram is generated and compressed. The standard deviation of the distribution curve of the compressed histogram is determined. A gain factor is determined using the mean and standard deviation. Using the background grey-level, the dynamic range of the document is adjusted.

Method for determining document background for adjusting the dynamic range of an image of the document

A system and method generates a histogram of a scanned image by determining a number of pixels of the image to be scanned to generate the histogram. It is then determined whether the determined number of pixels is less than or equal to a capacity of a histogram buffer. The image is scanned to generate pixels having grey values associated therewith and a portion of the generated pixels is stored in the histogram buffer if it is determined that the number of pixels exceeds the capacity of the histogram buffer, thereby generating a histogram of the image.

Method and system for generating a histogram of a scanned image

A system generates image background and black threshold grey values associated with an image. The system includes a histogram buffer and a peak and valley detection circuit, operatively connected to the histogram buffer, to determine a first grey value having a peak frequency associated with a background, a second grey value having a peak frequency associated with a black threshold, and a third grey value having a minimum frequency associated with a histogram of the image. A quarter peak detection circuit, operatively connected to the histogram buffer, determines a fourth grey value having a frequency equal to one quarter of the peak frequency associated with the first grey value and a fifth grey value having a frequency equal to one quarter of the peak frequency associated with the second grey value. A sigma peak detection circuit, operatively connected to the histogram buffer, determines a sixth grey value having a frequency equal to five-eighths of the peak frequency associated with the first grey value and a seventh grey value having a frequency equal to five-eighths of the peak frequency associated with the second grey level. A neighboring bin detection circuit, operatively connected to the histogram buffer, determines a valley or peak relationship between a grey value being presently analyzed and neighboring grey values, and a parameter generation circuit generates an image background grey value and a black threshold grey value based on the first through seventh grey values.

System and method for generating and utilizing histogram data from a

A method and system generates a histogram from a scanned image by creating a sample window defined by a number of scanlines to be sampled, a number of leading scanlines to be skipped in a slowscan direction before sampling, a number of leading pixels to skip in a fastscan direction before sampling begins, and a number of pixels within a scantine to be sampled. It is then determined if a number of pixels in the sample window is greater than a capacity of a histogram buffer. If it is determined that the number of pixels in the sample window is greater than the capacity of the histogram buffer subsample windows are created within the sample window. The image is scanned and a predetermined number of pixels within each subsample window is processed to generate a histogram, the predetermined number being less than a total number of pixels in a subsample window if it is determined that the number of pixels in the sample window is greater than the capacity of the histogram buffer, thereby processing a total number of pixels that is less than or equal to the capacity of the histogram buffer.

Method and system for generating histograms from a scanned image

A system and method for determining a black threshold grey value for an image. A histogram is generated corresponding to possible grey values of the image and a first grey value having a peak frequency associated with a black distribution is determined from the histogram. A second grey value having a peak frequency equal to one quarter of the peak frequency associated with the first grey value, a third grey value having a peak frequency equal to 5/8 of the peak frequency associated with the first grey value, a fourth grey value located on a negative slope-side of the first grey value having a peak frequency equal to one quarter of the peak frequency associated with the first grey value, and a fifth grey value having a minimum peak frequency associated with the histogram are also determined. A black threshold grey value for the image is calculated from the first, second, third, fourth, and fifth grey values.

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Ontario, NY, United States of America

Barbara Lynn Farrell