Xerox Corporation

Hon Hai Precision Industry Co., Ltd.

Bank One Corporation

Jeng-nan Shiau

David Jon Metcalfe

Leon C Williams

Stuart Alan Schweid

Zhigang Fan

Raymond J Clark

Ying-wei Lin

Barbara Lynn Farrell

Shriram V Revankar

Kathy Ryall

Raja Bala

Xing Li

Francis Kapo Tse

Martin Edward Banton

Gaurav Sharma

Yeqing Zhang

Roger Lee Triplett

Terri A Clingerman

Donald R Lehmbeck

Disclosed is a method and system for processing image data, which may be generated by a scanning subsystem, and the segmentation and treatment of leaky windows or segments within the image. In addition to the identification of window regions or segments having leaky boundaries, the method and system include the subsequent control of enhancement and other image processing techniques applied to such images so as to reduce or eliminate artifacts that result from the processing of leaky window regions.

System and method for processing images with leaky windows

A device () for manufacturing a color printing includes a printhead (), a device () for additional printing of the color printing, and a printhead motion and jetting control (). The device () also provided herein includes a printhead identification unit () and the host processor (). The printhead identification unit is configured for identifying type of the N jets, calculating the numbers of the defective jets, and then outputting measurement information of the N jets and the defective jets. The host processor is configured for receiving the measurement information from the printhead identification unit and outputting movement instructions of the printhead with a starting position of the printhead in the first printing process.

Device for manufacturing color printing and method using same

Methods and apparatus for segmenting image data into windows and rendering a best-estimate outline delineating document windows may include adjusting a background white threshold differently than adjusting a white threshold normally used to classify pixels as white for video processing purposes. Such an independently arrived at threshold may be more suitable for the different task of differentiating between document background and document content.

Apparatus and method for auto windowing using multiple white thresholds

A system and method remove at least one color from a color image in a chrominance-luminance color space. The processing method includes generating screen values for machine independent color space image pixels, comparing the screen values for the pixels with pixel values for one component of the machine independent color space, and generating a dropout pixel for each pixel having a screen value that is less than the pixel value for the one component of the machine independent color space. The screen value for a pixel may be compared to the pixel value for the luminance component to determine whether to generate a dropout pixel or not.

Method and system for processing color image data

A method for dynamic range adjustment of image data of a captured image by determining a white point of an image. The method also involves determining a black point of the image, classifying pixels of the image, and determining an offset value for a pixel of the image based on the determined black point of the image and the determined classification of the pixel. Dynamic range adjustment of the image data is performed using the determined offset value for the pixels of the image and the determined white point of the image.

Systems and methods for optimal dynamic range adjustment of scanned images

A method and apparatus are provided for the processing of an image, such as a document. The invention determines the location of differing content types within the document allowing specialized processing of various content types. The invention performs the identification of pixels having similar content characteristics into windows during the first scanning pass of the document by the use of an identifier equivalence table to update selected memory locations to a base identifier during processing. A second pass processing is available to enhance or alter the image by the use of the information gathered during first pass processing. The present invention benefits from a very low memory requirement while being able to determine windows extending the length or width of the image.

Fast efficient window region coalescing in a two-pass auto-windowing environment

A method to detect frequency and angle of a binary halftone pattern. The method employs an exclusive-or operation which is applied locally to a region of a binary bit map and its spatially shifted version. The resulting bits from the exclusive-or operation are summed over the region. The exclusive-or operation is repeated for a range of shift values. In a halftone region, the shift at which the minimum sum occurs reflects the angle and the frequency of the halftone.

Binary halftone detection

A continuously variable screening tool is used to generate a unique texture pattern for every color in a multicolor image. The continuously variable screening tool allows a single colorant version of the image to be generated with less information loss than typically suffered in the multicolor to single color transformation process. The continuously variable screening tool is generated by blending patterns from a set of reference screens. The reference screens are associated with selected reference colors in, for example, a machine independent color space. A calculated screen is generated through a weighted blend of reference screens located near the arbitrary color in the machine independent color space. Typically, the weights depend on the distance the arbitrary color is from each of the reference colors. The screens consist of arrays of threshold values. Each threshold value is associated with a dot position and an image pixel. Where a pixel value exceeds the associated threshold value, provision is made to place a mark in the associated dot position. An image processor operative to carry out the method comprises a continuously variable screening tool generator. An embodiment of the image processor includes a reference screen storage device and a screen blender.

Color to black and white converter

A method and system for image processing, in conjunction with classification of images between natural pictures and synthetic graphics, using SGLD texture (e.g., variance, bias, skewness, and fitness), color discreteness (e.g., R_L, R_U, and R_V normalized histograms), or edge features (e.g., pixels per detected edge, horizontal edges, and vertical edges) is provided. In another embodiment, a picture/graphics classifier using combinations of SGLD texture, color discreteness, and edge features is provided. In still another embodiment, a 'soft' image classifier using combinations of two (2) or more SGLD texture, color discreteness, and edge features is provided. The 'soft' classifier uses image features to classify areas of an input image in picture, graphics, or fuzzy classes.

Soft picture/graphics classification system and method

A method and apparatus are provided for determining a weighted average measured reflectance parameter R for pixels in an image for use in integrated cavity effect correction of the image. For each pixel of interest P in the image, an approximate spatial dependent average A , B of video values in a region of W pixels by H scan lines surrounding the pixel of interest P is computed by convolving video values V of the image in the region with a uniform filter. For each pixel of interest P a result of the convolving step is used as the reflectance parameter R . The apparatus includes a video buffer for storing the pixels of the original scanned image, and first and second stage average buffers for storing the computed approximate spatial dependent averages A , B . First and second stage processing circuits respectively generate the first and second stage average values A , B by convolving the video values of the image in a preselected region with a uniform filter.

Method and apparatus for implementing integrated cavity effect correction in scanners

A system and method classify a pixel of image data as one of a plurality of image types. A first image characteristic value for the pixel, a second image characteristic value for the pixel, a third image characteristic value for the pixel, and a fourth image characteristic for the pixel is determined. Some of these determinations may be resolution dependent. The values from these determination are utilized in assigning an image type classification to the pixel. Moreover, if at least one of the image characteristic values is greater than a predetermined threshold value the pixel is classified as a halftone peak value. The system includes a plurality of microclassifiers for determining a distinct image characteristic value of the pixel; a plurality of macroreduction circuits connected to the plurality of microclassifiers for performing further higher level operations upon the distinct image characteristic values of the pixel to produce reduced values; and a classification circuit to classify the pixel as an image type based on the reduced values from the macroreduction circuits. The system also includes a circuit to detect flat peaks without detecting multiple peaks and a rectangular blur filtering system.

Method and system for classifying and processing of pixels of image data

A method and apparatus are provided for use in single channel segmentation of color images for reclassifying pixels which were inappropriately classified as belonging to a &ldquo;black&rdquo; or &ldquo;white&rdquo; class. The apparatus includes a re-classification circuit receiving first classification data generated by a single channel segmentation circuit operative to classify pixels of a multi-color channel input image. The re-classification circuit selectively re-classifies pixels that were previously classified by the single channel segmentation circuit into an &ldquo;other&rdquo; segmentation class based on a comparison of classification data associated with the pixels relative to a set of predefined classification data types. Particularly, the re-classification circuit reclassifies selected ones of the pixels into the &ldquo;other&rdquo; segmentation class when first classification data generated by the single channel segmentation circuit labels the pixels as a &ldquo;white&rdquo; or &ldquo;black&rdquo; data type.

Method and apparatus for re-classifying color image pixels classified by single channel segmentation

A color conversion table designed to produce 0% under-color removal and a 100% under-color removal TRC are configured as a matched pair and are used to render an image from fuzzy detected color signals, including providing variable under-color removal to obtain fuzzy black conversion. The output of the color conversion table and the TRC are multiplied by a weighted value which depends on a value of a received neutral tag. The weighted outputs of the color conversion table and TRC are added such that a varying weighted output is generated. The generated output is used in image rendering to produce a smooth transition from a full color to monochrome, where variable amounts of under-color removal are obtained using the color correction table and TRC.

Fuzzy black color conversion using weighted outputs and matched tables

Image information is re-calibrated based on the type of marking process and materials by which the image corresponding to the image information was marked on a substrate. The marking process is automatically detected by using, for example, at least one spatial characteristic obtained from the image information.

Methods and apparatus for modifying image data based on identification of marking process

A method and system implements a dynamic error diffusion process. A grey level value representing a pixel is received. An image segmentation circuit determines an image characteristic of the pixel being processed. A threshold circuit thresholds the grey level value (pixel) and generates an error value as a result of the threshold process. A portion of the error value is diffused to adjacent pixels on a next scanline. The distribution of this error value is dynamic in that different sets of weighting coefficients are used based the image characteristic of the processed pixel. One set of coefficients are utilized in processing a pixel having a first image characteristic, and a second set of coefficients are utilized in processing a pixel having a second image characteristic.

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Webster, NY, United States of America

Jeng-nan Shiau