Kodak Alaris Inc.

Eastman-kodak Company

David K Rhoda

Alexander C Loui

Raymond William Ptucha

Joseph Anthony Manico

Mark D Wood

David Kloosterman

Edward B Gindele

John D Buhr

John A Weldy

Thomas Joseph Murray

Andrew T Cooper

Michael S Axman

Michael W Dupin

A method for determining an impact score for a digital image includes providing the digital image wherein the digital image includes faces; using a processor to determine an image feature for the faces; using the processor to compute an object impact score for the faces, wherein the object impact score is based at least upon one of the determined image features; weighting the object impact score for the faces based on one of the determined image features for a face; using the processor to compute an impact score for the digital image by combining the weighted object impact scores for the faces in the image; and storing the computed impact score in a processor accessible memory.

Imaging workflow using facial and non-facial features

The present application is directed to new methods for automatically and effectively combining multiple metrics to select interesting, or key, frames from a video sequence. In one aspect, the methods include selecting a reference frame from a video sequence and iteratively computing the improvement or gain in the combined metrics for candidate frames with respect to that reference frame. In another aspect, a master list of reference key frames is determined, quality metrics are calculated for each of a plurality of candidate frames associated with each reference key frame, and each reference key frame in the master list is replaced with a candidate frame that yields the greatest aggregate quality improvement over that reference key frame.

Method for selecting frames from video sequences based on incremental improvement

Correction of color defects in a pupil represented in a digital image is disclosed. For example, a location in the pupil within the digital image is identified, and a target color to be corrected is computed based at least upon an analysis of pixels within a first region in which the location resides. Defect pixels in a second region in which the location resides are identified, the defect pixels being identified as having a pixel color similar to the target color. The defect pixels are color-corrected. For pupils that appear all white, appropriately configured pupil images are inserted therein.

Pet eye correction

A method of transforming the color appearance of a plurality of digital images includes receiving a plurality of digital images from a capture medium wherein each digital image includes a plurality of pixel values relating to at least three basic colors; calculating a color correction transform by using a non-linear adjustment that is independent of the digital images and which corrects an under-exposure condition as a function of the capture medium; and a linear functional relationship dependent upon the pixels of the basic colors, the linear functional relationship defining an exposure-level-dependent estimate of gray corresponding to the photographic response of the capture medium; and using the color correction transform to modify the pixels of the plurality of digital images wherein the pixels within a digital image are transformed with varying degrees of color modification as a function of exposure.

Color transformation for processing digital images

Growing community of inventors

Rochester, NY, United States of America

David K Rhoda