Abstract: An image is automatically assessed with respect to certain features, wherein the assessment is a determination of the degree of importance, interest or attractiveness of the image. First, a digital image is obtained corresponding to the image. Then one or more quantities are computed that are related to one or more features in the digital image, including one or more features pertaining to the content of the digital image. The quantities are processed with a reasoning algorithm that is trained on the opinions of one or more human observers, and an output is obtained from the reasoning algorithm that assesses the image. More specifically, the reasoning algorithm is a Bayesian network that provides a score which, when done for a group of images, selects one image as the emphasis image or the appeal image. The features pertaining to the content of the digital image include people-related features and/or subject-related features.

Abstract: A method to automatically vary the compression of images by ranking images within clusters based upon image emphasis. The ranking process computes one or more quantities related to one or more features in each image. The features can include the content of images. The invention processes the quantities with a reasoning algorithm that is trained based on opinions of one or more human observers. The invention applies the quantities to the images to produce the ranking and variably compresses the images depending upon the ranking. The images having a low ranking and are compressed more than images that have a high ranking.

Abstract: In a method for varying one or more print attributes of a print made from a digital image, a print attribute value is computed for the digital image based on a determination of the degree of importance, interest or attractiveness of the image and the resulting print attribute value is used to control a print attribute of the print made from the image. The print attribute value is derived from an emphasis or appeal value, wherein an appeal value is an assessment of each image taken by itself and the emphasis value is an assessment of each image in relation to other images in a group. In a typical embodiment, the print attribute is either the number of prints made of the image, the size of a print made from the image, or the magnification factor used for the image.

Abstract: A method for varying the image processing path for a digital image involves the steps of (a) computing an image processing attribute value for the digital image based on a determination of the degree of importance, interest or attractiveness of the image; and (b) using the image processing attribute value to control the image processing path for the image. In one embodiment, the image processing attribute value is based on an appeal value determined from the degree of importance, interest or attractiveness that is intrinsic to the image. In another embodiment, wherein the image is one of a group of digital images, the image processing attribute value is based on an emphasis value determined from the degree of importance, interest or attractiveness of the image relative to other images in the group of images.

Abstract: An image is automatically assessed with respect to certain features, wherein the assessment is a determination of the degree of importance, interest or attractiveness of the image. First, a digital image is obtained corresponding to the image. Then one or more quantities are computed that are related to one or more features in the digital image, including one or more features pertaining to the content of the digital image. The quantities are processed with a reasoning algorithm that is trained on the opinions of one or more human observers, and an output is obtained from the reasoning algorithm that assesses the image. More specifically, the reasoning algorithm is a Bayesian network that provides a score which, when done for a group of images, selects one image as the emphasis image. The features pertaining to the content of the digital image include people-related features and/or subject-related features.

Abstract: A method and system for segmenting textures in an image includes computing multiresolution simultaneous autoregressive (MSAR) features in the image, preparing an uncertainty map of the MSAR features (the uncertainty map including high confidence pixels and low confidence pixels), computing wavelet features in the image, preparing a classifier based on the high confidence pixels and the wavelet features, and reclassifying the low confidence pixels based on the classifier to obtain the final segmentation.

Abstract: A method to automatically vary the compression of images by ranking images within clusters based upon image emphasis. The ranking process computes one or more quantities related to one or more features in each image. The features can include the content of images. The invention processes the quantities with a reasoning algorithm that is trained based on opinions of one or more human observers. The invention applies the quantities to the images to produce the ranking and variably compresses the images depending upon the ranking. The images having a low ranking and are compressed more than images that have a high ranking.

Abstract: A method for automatically classifying a digital image as a dud, the method comprises the steps of receiving the digital image; determining individually or any combination of sharpness,;contrast, noise, and exposure of the digital image; determining a threshold individually for sharpness, contrast, noise, and exposure, or a threshold for any combination of sharpness, contrast, noise, and exposure which determined threshold or thresholds determines if the image is classified as a dud; and classifying the image as a dud based on the determination of the previous step.

Abstract: An image is automatically assessed with respect to certain features, wherein the assessment is a determination of the degree of importance, interest or attractiveness of the image. First, a digital image is obtained corresponding to the image. Then one or more quantities are computed that are related to one or more features in the digital image, including one or more features pertaining to the content of the digital image. The quantities are processed with a reasoning algorithm that is trained on the opinions of one or more human observers, and an output is obtained from the reasoning algorithm that assesses the image. More specifically, the reasoning algorithm is a Bayesian network that provides a score which, when done for a group of images, selects one image as the emphasis image or the appeal image. The features pertaining to the content of the digital image include people-related features and/or subject-related features.

Abstract: A method of generating an M-bit grayscale image from an N-bit grayscale image, where 1<M<N, includes the steps of: for each pixel in the N-bit image, determining a threshold, based on the values of neighboring pixels, for generating the most significant bit (MSB) of the M-bit image, and determining thresholds, based on the threshold determined in step i) and the values of surrounding pixels, for each successive less significant bit(s) (LSB) of the M-bit image. The thresholds are used to quantize the pixels in the N-bit image to produce the M-bit image. The resulting M-bit image can be displayed on a multilevel display device for good readability, and the most significant bit of the M-bit image can be archived and printed on a binary printer thereby minimizing long term storage requirements.

Abstract: Document image thresholding using foreground and background is disclosed. A document (16) is scanned and a region (30) and a sub-region (32) of the document is selected. The intensity value of a pixel in the region is compared to an average intensity value of pixels in a foreground cluster and to an average intensity value of pixels in a background cluster (50). The pixel is assigned to the foreground cluster (53) if the intensity value of the pixel is closer to the average intensity of the foreground cluster, or to the background cluster (54) if the pixel intensity is closer to the average intensity value of the background cluster. A new average intensity value is calculated for the foreground cluster (55) and the background cluster (56). A new pixel is compared to the new average intensity values for the foreground and background clusters and the process is repeated until all pixels in the region have been compared (57).

Abstract: A document (10) is scanned to provide a digital image. At least one non-dropout color is selected (32) and transformed to a Luminance-Chrominance space (34). Each pixel of the scanned image is converted to the Luminance-Chrominance space (36) and the distance of each of the image pixels from the non-dropout color is determined (38). Each of the image pixels is converted to black (44) if the distance from the non-dropout color is less than or equal to a threshold value, and converted to white (42) if the distance is greater than the threshold value. The converted black and white pixels are then stored.

Abstract: A laser velocimeter signal processor for measuring the signal frequency within a signal burst. The input signal is converted to digital by an ADC 16 and then shifted into shift registers 30 and 31. An automatic gain circuit 15 controls the gain of the input signal. A signal integration circuit 32 determines when a signal burst has been captured by the shift registers and and then transfers the contents of the registers to data latches 33 and 34. The data in data latches 33 and 34 is processed by digital bandpass filters 57-63, square law detectors 64-70, burst counters 71-77 and signal processor 78 to determine the frequency of the signal within the captured signal burst.