Intel Corporation

Koninklijke Philips Corporation N.v.

Walid Ali

Jorge E Caviedes

Tiehan Lu

Raju Hormis

Tsung-Hsin Lu

Christopher Stephen Hall

Larry James Eshelman

Robert Randall Entrekin

Yasser Alsafadi

Claire Levrier

James David Schaffer

Claude Cohen-Bacrie

Jean-Michel LaGrange

Nicholas Villain

A method of sharpening video data may include, for at least some pixels in the video data, determining a motion value for a pixel. The sharpness of the pixel may be changed inversely in relation to the motion value of the pixel. Pixels that have higher velocities may be sharpened less than slower moving or stationary pixels in an image of video data.

Varying sharpness based on motion in video sequences

A method and system are disclosed that detect signal artifacts in one or more event signals. The system and method may be used with a patient monitoring apparatus that adapts to a patient's condition and distinguishes between clinically significant changes in the patient's state verse clinically insignificant changes.

System and method for detecting signal artifacts

A method and system are disclosed that detect signal artifacts in one or more event signals. The system and method may be used to estimate whether a monitored signal includes artifacts based upon a statistical analysis using a transform function.

System and method to estimate signal artifacts

An apparatus, system, method, and article for enhancing video sharpness are described. The apparatus may include a media processing node to receive an input image having a first level of contrast, and create an output image having a second level of contrast using a scaled luminance transfer function. Other embodiments are described and claimed.

Techniques to improve contrast enhancement

A method for determining the presence or absence of malignant features in medical images, wherein a plurality of base comparison or training images of various types of lesions taken of actual patient is examined by one or more image reading experts to create a first database array. Low-level features of each of the lesions in the same plurality of base comparisons or training images are determined using one or more image processing algorithms to obtain a second database array set. The first and second database array set are combined to create a training database array set which is input to a learning system that discovers/learns a classifier that maps from a subset of the low-level features to the expert's evaluation in the first database array set. The classifier is used to determine the presence of a particular mid-level feature in an image of lesion in a patient based solely on the image.

Method and apparatus for automatically developing a high performance classifier for producing medically meaningful descriptors in medical diagnosis imaging

A method for monitoring a patient () includes determining () convex hulls for pairs of monitored signals from the patient, and determining whether a perturbation has occurred () in one or more of the convex hulls. This exemplary embodiment () can also include alerting an operator that a clinically significant change may have occurred () in the patient if each of the convex hulls has been perturbed. If only a subset of the convex hulls is perturbed, an artifact has probably occurred ().

Decision support system to detect the presence of artifacts in patients monitoring signals using morphograms

An apparatus, system, method, and article for enhancing video sharpness are described. The apparatus may include a media processing node to enhance sharpness of an input image. The media processing node may include a wavelet decomposition module to perform wavelet decomposition of the input image to detect and modify edge information. Other embodiments are described and claimed.

Enhancing video sharpness and contrast by luminance and chrominance transient improvement

A method includes determining a lowest-score interpolation direction among a plurality of interpolation directions. The method further includes calculating a candidate pixel value by interpolating along the lowest-score interpolation direction. The method further includes applying a median function to a set of pixel values. The set of pixel values includes (a) the candidate pixel value, (b) at least one pixel value from a line of pixels that is immediately above a pixel location that is currently being interpolated, and (c) at least one pixel value from a line of pixel values that is immediately below the pixel location that is currently being interpolated.

Video de-interlacing with motion estimation

A method for monitoring a patient employs hypothesis testing against each of several monitored signals to determine whether an artifact is present in the monitored signals. In the hypothesis testing, a null hypothesis includes an assumption that pairs of samples of highly correlated monitored signals of the several monitored signals have a predetermined distribution. The method determines that an artifact may exist in one of the monitored signals when the likelihood that the null hypothesis is true falls below a predetermined confidence value. This method can be embodied in an intelligent module for processing multiple data from one or more patients to filter out clinically significant changes in the patient from those changes caused by artifacts.

Method and system for detecting artifacts in ICU patient records by data fusion and hypothesis testing

A method of selectively sharpening video data may include, for at least some pixels in the video data, generating a sharpened value for a pixel value in an image. The sharpened value may be disregarded if a combination of the pixel value and the sharpened value is in a coring region. The sharpened value also may be disregarded if a combination of the pixel value and the sharpened value is a clipping region. The combination of the pixel value and the sharpened value may be output if the combination is not in the coring region or in the clipping region.

Enhancing sharpness in video images

A method of enhancing the contrast of image or video data may include applying a contrast increasing transfer function to a reference image to generate an enhanced image. A transformation may be applied to the reference image to generate reference high frequency components and may also be applied to the enhanced image to generate enhanced high frequency components. For a pixel in the reference image, whether a corresponding enhanced high frequency component has a higher energy than a corresponding reference high frequency component may be determined. The method may also include replacing the pixel in the reference image with a corresponding pixel in the reference image if the corresponding enhanced high frequency component has a higher energy than the corresponding reference high frequency component.

Enhancing contrast of video data while preserving sharpness

A method of selectively sharpening video data may include, for at least some pixels in the video data, generating a sharpening value for a pixel value in an image. The sharpening value may be amplified in a non-linear manner to produce an amplified value. The pixel value and the amplified value may then be combined.

Enhancing video sequence sharpness by adaptive peaking

Gradient adaptive video de-interlacing

One implementation of a method for edge directed video de-interlacing in accordance with the disclosed invention includes obtaining at least a portion of a field of input video data including at least portions of four consecutive rows of field pixels including first, second, third, and fourth rows of field pixels. The method further includes selecting an orientation over which to de-interlace the input video data based, at least in part, on a measure of the deviation in pixel values among the four consecutive rows of field pixels and a fifth row of pixels located between the second and third rows of field pixels, the fifth row of pixels including previously interpolated pixel values and pixel values obtained by line averaging between pixel values in the second and third rows of field pixels. The method further includes interpolating along the selected orientation to determine a value for a pixel to be interpolated.

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Chandler, AZ, United States of America

Walid Ali