Abstract: The age class of a human subject is ascertained in a digital image. The subject has a redeye defect pair; each defect having one or more defect pixels. In the method, two regions of pixels corresponding to the defects are identified. The distance between the regions is measured. A region size is determined, based upon the size of at least one of the regions. An age class is determined from the distance and region size.

Abstract: A method for color correction of an image having an insignia portion in a multi-module printer (10). A first color profile is calibrated for a four-color image. A second color profile is calibrated for a four-color image with a transparent toner layer on top of the insignia portion of the four-color image. The image data is then buffered in a color input band buffer (40) and processed through the first (50) and second (60) color profiles in a color management module. The processed image data is stored in an output band buffer (70, 80) corresponding to the image data processed through each color profile. Clear toner input band data stored in a clear toner input band buffer (30) is compared with the processed image data in each output band buffer (70, 80) to select an output signal (100) on a per pixel basis to send to the multi-module printer (10).

Abstract: In a printing method and systems, segments of a first print job are distributed to respective downstream processors and are processed to provide printable frames, which are stored in respective print queues. Each print queue supplies a respective one or more printheads. The printable frames are sequentially printed on a continuously transported receiver. A maximum printing duration of each of the print queues is computed periodically during the printing. The transport speed is regulated to trend the maximum printing durations toward a predetermined baseline. The sending of a second print job to the downstream processors is delayed, while processing of the first print job is completed. The delaying is counter to the regulating and reduces the print queues of the first print job non-uniformly relative to the baseline.

Abstract: In a method and digital camera, initial evaluation images of a scene are captured with the camera. A change in scene brightness between two or more of the evaluation images is computed. When the scene brightness change is outside a predetermined brightness range, a metric of scene-to-capture mismatch in the evaluation images is determined. When the brightness change and the metric are both outside predetermined ranges, the camera is shifted to a second capture state prior to the capturing of one or more additional evaluation images. In either case, a final camera state is determined using available evaluation images and one or more final images are captured with the camera in the final camera state.

Abstract: A method for automatically determining the acceptability of an input image for a predetermined output operation, such as printing, includes the steps of: (a) identifying one or more important areas in the input image; (b) calculating a salient acceptability feature for each of the important areas; (c) determining the acceptability of the input image from the salient acceptability features of the important areas; and (d) implementing the predetermined output operation based on the acceptability of the input image determined in step (c).

Abstract: A printer dock for use with a digital data terminal, has a housing having a paper inlet portal, a first tray receiver, and a second tray receiver. The first tray receiver adjoins the paper inlet portal. The second tray receiver is disposed in spaced relation to the paper inlet portal. A printer engine is disposed in the housing. The printer engine has a paper path extending from an entrance facing the paper inlet portal. A device interface is operatively connectable to the data terminal and the printer engine. A paper tray is alternately mountable in a use position and a storage position. The paper tray, in the use position, is located in the first tray receiver and is disposed in operative relation to the printer engine. The paper tray, in the storage position, is located within the second tray receiver.

Abstract: A batch processing method for enhancing an appearance of a face located in a digital image, where the image is one of a large number of images that are being processed through a batch process, comprises the steps of: (a) providing a script file that identifies one or more original digital images that have been selected for enhancement, wherein the script file includes an instruction for the location of each original digital image; (b) using the instructions in the script file, acquiring an original digital image containing one or more faces; (c) detecting a location of facial feature points in the one or more faces, said facial feature points including points identifying salient features including one or more of skin, eyes, eyebrows, nose, mouth, and hair; (d) using the location of the facial feature points to segment the face into different regions, said different regions including one or more of skin, eyes, eyebrows, nose, mouth, neck and hair regions; (e) determining one or more facially relevant character

Abstract: An image sensor generates an image signal with a differential response to image light. The image sensor has an array of photosites divided into standard photosites and non-standard photosites. A limiter provides the standard photosites with a predetermined standard response to a light exposure and the non-standard photosites with a predetermined slower response to the same light exposure. The standard photosites and nonstandard photosites both sparsely sample the array in a predetermined pattern.

Abstract: In a parallax correction method and system, corresponding regions of first and second digital images of the same subject are upsampled to provide a pair of upsampled regions. The corresponding regions have parallax. The upsampled regions are statistically compared at a plurality of relative subpixel offsets to provide a corresponding plurality of similarity values. The similarity values define a peak similarity value and a corresponding peak subpixel offset among the plurality of relative subpixel offsets. The second digital image is shifted relative to the first digital image by the peak subpixel offset.

Abstract: In an eye detection method, a plurality of candidate eyes are located within a digital image. Pixels of interest having a predetermined characteristic and a predetermined association to respective eyes are found. Pixels of interest associated with each eye are grouped. Parametric boundaries are fit on each of the groups to define a plurality of detected eye features. The boundaries have a predefined uniform shape and a size matched to a respective group. Each of the detected eye features is scored as to a geometric relationship between the respective boundary and pixels of interest associated with the respective eye to provide eye feature scores.

Abstract: In a method and system for processing a photographic image having lightness values, L*, representing one of the colorimetric values of an original scene, the photographic image is transformed. The transformed image has a gamma as a function of CIE 1976 L*, which includes a dark region having a rising slope, a light region having a falling slope, and a plateau region having a slope constantly within 5 percent of a maximum value in said plateau region. The rising slope is at least twice as large as the absolute value of the falling slope. The plateau region is between 10 L* and 30 L* wide. Gamma is a derivative of visually perceived reproduced CIE 1976 L* versus scene CIE 1976 L*. Gamma has a maximum slope between 1.5 and 2.0.

Abstract: In a color image transformation method and system, a photographic color-positive transparency media bearing a captured image is transmission scanned to provide a scanned image. The scanned image has an overall cyan-blue color balance and has grayscale mistracking relative to colorimetric chromaticity. The scanned image is automatically transformed to intermediary color image data encoding values. Both the overall cyan-blue color balance and the mistracking are corrected during the transforming.

Abstract: A method for obtaining and automatically classifying images into events comprising the steps of: (a) obtaining a group of images from a digital source, wherein the images are in chronological order; (b) transferring the group of images to a computer system; said computer system (c) clustering the images into smaller groups based on chronological image similarity of nearby images by computing histograms of the images and comparing histogram intersection values obtained therefrom with one or more thresholds, whereby the clustering based on chronological image similarity is done in at least one stage by comparing each image with its direct neighboring images; and (d) evaluating the clustered images against a final condition related to at least one of a predetermined group maximum for the number of smaller groups and a predetermined maximum number of isolated pictures, whereby the smaller groups are classified as events if the final condition is met.

Abstract: A method of calculating a brightness balance value for a digital image including a plurality of pixels, comprising the steps of: calculating a spatial activity measure in a plurality of local neighborhoods of pixels of the digital image, each local neighborhood including a plurality of pixels; generating a weighting factor for each of the local neighborhoods of pixels as a function of the spatial activity of the local neighborhoods of pixels, the weighting factors having more than two possible values; and applying the weighting factors to the pixels of the digital image to produce the brightness balance value.

Abstract: In methods, systems, and computer program products for locating a regularly configured object within a digital image, a plurality of primary rotated integral images of the digital image are computed. Each primary rotated integral image has a different in-plane rotation. A set of secondary rotated integral images are derived from each of the primary rotated integral images. The secondary rotated integral images have further in-plane rotations relative to the respective primary rotated integral image. A window is defined within the digital image and corresponding windows of the rotated integral images. The values of convolution sums of a predetermined set of feature boxes within the window, in each of the rotated integral images are extracted. The dimensionality of the convolution sums is reduced to provide a set of reduced sums. A probability model is applied to the reduced sums to provide a best estimated derotated image of the window.

Abstract: In a photographic processing method, an image unit having a plurality of printable image frames is received. The image frames include a data frame having subchannel information. An indicator of the presence of the subchannel information is detected. A bitstream of all of the image frames is generated. Responsive to the indicator, the data frame is extracted from the bitstream and subchannel information is read from the data frame. Fulfillment of image frames of the bitstream is provided in accordance with the subchannel information. If no indicator is detected, then the image frames are fulfilled in accordance with a different, default subchannel.

Abstract: A method for detecting an object in a digital image including the steps of performing color quantization on a model image including the object and on a search image that potentially includes the object, generating a plurality of search windows, computing spatial-color joint probability functions of each model and search image, where the color co-occurrence edge histogram is chosen to be the spatial-color joint probability function, assessing the similarity of each search image to the model, and designating search windows as containing the target object.

Abstract: A digital image processing method detects facial features in a digital image. This method includes the steps of detecting iris pixels in the image, clustering the iris pixels, and selecting at least one of the following schemes to identify eye positions: applying geometric reasoning to detect eye positions using the iris pixel clusters; applying a summation of squared difference method using the iris pixel clusters to detect eye positions; and applying a summation of squared difference method to detect eye positions from the pixels in the image. The method applied to identify eye positions is selected on the basis of the number of iris pixel clusters, and the facial features are located using the identified eye positions.

Abstract: A method for synthesizing noise in a digital image comprises the steps of (a) estimating noise statistics based on a set of spatial autocorrelation coefficients and a set of spectral correlation coefficients that correspond to the color channels of the image, where at least one of the spatial autocorrelation coefficients is weighted by at least one of the spectral correlation coefficients, thereby providing a weighted set of autocorrelation coefficients, and where a set of filter coefficients is obtained from the weighted set of autocorrelation coefficients; and (b) synthesizing a synthetic texture by using the filter coefficients to filter a random noise field in each of the color channels, thereby producing output noise fields in each of the color channels that replicate a synthetic texture, e.g., a desired grain appearance, when combined into the digital image.

Abstract: A batch processing method for enhancing an appearance of a face located in a digital image, where the image is one of a large number of images that are being processed through a batch process, comprises the steps of: (a) providing a script file that identifies one or more original digital images that have been selected for enhancement, wherein the script file includes an instruction for the location of each original digital image; (b) using the instructions in the script file, acquiring an original digital image containing one or more faces; (c) detecting a location of facial feature points in the one or more faces, said facial feature points including points identifying salient features including one or more of skin, eyes, eyebrows, nose, mouth, and hair; (d) using the location of the facial feature points to segment the face into different regions, said different regions including one or more of skin, eyes, eyebrows, nose, mouth, neck and hair regions; (e) determining one or more facially relevant character