Hewlett-packard Company

Hewlett-packard Development Company, L.p.

Patricia D Lopez

Jeffrey P Lee

Steven J Simske

Robert E Sobol

Yucel Altunbasak

Dan S Johnson

Hal J Hansen

James B Williamson

Nancy K Mundelius

Theodore W Neff

A digital imaging system includes a color appearance handling capability that utilizes a database including actual image data and information relating to the actual image data to control illumination and color in digital images. The digital imaging system controls illumination and color with a color-by-correlation technology that includes an illuminant detection training algorithm that bases control of illumination and color on a database storing actual illuminants, scenes, and subjects from representative image acquisition devices such as digital cameras and scanners. In one example, illuminant detection training is accurately rendered for a variety of illuminants, scenes, and subjects that are expected to be of interest to users of a particular camera or group of cameras. In the example, the database is formed by user-group submissions from users of a particular camera or group of cameras. The digital imaging system accesses a database capable of supplying actual image data including illuminants, chromaticities, colors, and the like. The database also stores information relating to the actual image data including tags that identify the image acquisition device that acquires a particular image, and information relating to manufacturer, model and version of the image acquisition device, and image acquisition devices settings at the time of acquisition.

System for setting image characteristics using embedded camera tag information

A user interface method for launching an optimized final scan of a selected region of interest selected from a preview scan of a document. A user may drag the selected region of interest, presented in a preview scan of a document in a scanner window, and drop it on a software application, the desktop, or a writeable folder, which launches an optimized final scan of the selected region of interest. The image data resulting from the optimized final scan automatically resides in the software application, the desktop, or the writeable folder. In selecting a region of interest from the preview scan, scanner software parameters are updated with information about the region of interest which optimize the final scan. The image data from the optimized final scan is then formatted in the format requested and delivered to the software application, the desktop, or the writeable folder.

User interface for initiating the export of an optimized scanned document using drag and drop

An optical waveguide and a shutter are used to guide light from multiple illuminated pixels on a document being scanned onto a single photosensor element. Various methods are disclosed for selecting one pixel at a time for projection onto a single sensor element. Multiple scans are required to capture all the image pixels. The data from multiple scans are then combined to form a single scanned image that has a higher optical sampling rate than the native optical sampling rate of the sensor array. Superresolution image analysis techniques developed for reconstruction of one image from a set of lower resolution images may be applied to provide a diffraction-limited high-resolution image. The waveguide as described also provides an ability to reduce scanning time for lower resolution images. In a first example embodiment, a rotating rod lens with a pattern of black and white areas is used to block/unblock optical waveguide array elements. In a second example embodiment, a thin mask is translated over the optical waveguide array elements. In a third example embodiment, an electronic display device such as a liquid crystal array is used to block/unblock optical waveguide array elements.

Image scanner with optical waveguide and enhanced optical sampling rate

A user interface for scanner software that allows a variable resolution preview scan of a document to be presented to the user in a preview box within a scanner software window. The user clicks on a point within the preview scan, and the classification of the image immediately around the click point is determined. A selection marker is automatically drawn around the selected image, and all portions of the preview scan outside the selection marker are grayed out. Based on the classification, adjustments are made automatically, and windows having control tools for adjusting various aspects of the selected image are automatically enabled or disabled. Once the desired area is properly selected and any adjustments have been made, the user can drag and drop the selected image with a mouse onto the desktop, a folder, or an open application to launch an optimized final scan of the selected image.

Click and select user interface for document scanning

A method for image processing combining a device-specific image processing kernel operation with a general image processing kernel operation. Device specific parameters are sent from a host computer to an imaging device. The imaging device selects appropriate device-specific convolution coefficients. The host computer selects additional kernel operations. In a first example embodiment, the device-specific coefficients are then uploaded to the host computer and the host computer convolves the host-specified operations with the device-specific operations. The combined kernel is then downloaded to the imaging device. In an alternative embodiment, the host-specified operations are downloaded to the imaging device for combining. The combined kernels are then used by the imaging device for convolution operations on an image. As a result, host computer software can modify a kernel operation within an imaging device with minimal knowledge of device-specific parameters such as native resolution.

Image processing system with alterable local convolution kernel

A method of filtering images with a filter that is dependent on image type. For binary images, a fixed Laplacian filter is used for all pixels. For non-binary images, an automatic level-sensitive Laplacian filter is used. For non-binary images, Laplacian filter coefficients and normalization divisors are made a function of intensity on a pixel by pixel basis. Pixels indicating a high intensity may be greatly changed by neighboring pixels. Pixels indicating a low intensity receive relatively little or no filtering. In a specific example embodiment, the intensity range is divided to bands, with a different filter for each band. The resulting images are visually sharpened (edge enhanced) with very little impact in darker areas of the image.

Sharpening filter for images with automatic adaptation to image type

A system that scans an image into a computer system and then locates left and right edges of information within each row of the image. The system defines top and bottom rows of information as first and last rows of a number of consecutive rows of the image that have a number of pixels between the left and right edges. The system linearizes the edge data, places a left boundary at the leftmost point of the linearized left edge, and places a right boundary at the rightmost point of the linearized right edge. Each pixel within the boundaries is analyzed to classify the pixel as white, light gray, dark gray, black, colored, or as having some color; a percentage of pixels in each class is created; and the image classification is determined using these percentages. The system displays the image and image classification, with the boundary superimposed over the image.

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Loveland, CO, United States of America

Patricia D Lopez