Eastman-kodak Company

Omnivision Technologies, Inc.

Apple Inc.

Kodak Alaris Inc.

Intellectual Ventures Fund 83 LLC

Harris Corporation

Bruce Harold Pillman

Mrityunjay Kumar

James E Adams, Jr

John F Hamilton, Jr

Russell J Palum

Amy D Enge

John N Border

Frank Razavi

Alexander C Loui

John Thomas Compton

Aaron Thomas Deever

Michele O'Brien

James A Hamilton

Kenneth Alan Parulski

Keith Stoll Karn

John Robert McCoy

Robert Gretzinger

Andrew Charles Gallagher

Jiebo Luo

Edward B Gindele

Robert Michael Guidash

Craig M Smith

Richard B Wheeler

James Michael Enge

Richard M Vogel

James E McGarvey

Wei Hao

David Wayne Jasinski

Sean C Kelly

Thomas E DeWeese

Erin S Wanek

John Randall Fredlund

Robert P Cloutier

Michael J Telek

John D Griffith

Scott C Cahall

Kevin Michael Gobeyn

Andrew C Blose

Efrain Orlando Morales

Dan Harel

Geoffrey John Woolfe

Lynn Schilling-Benz

Keith A Jacoby

Michael James Barry

Lynn V Larsen

Wayne Prentice

Michael E Napoli

Ann M Taccardi

John Norvold Border

John Franklin Hamilton

Amy Dawn Enge

Michelle O'BRIEN

John R Mccoy

Ann Marie Taccardi

James Andrew Hamilton

A digital camera having a plurality of photography modes, comprising: an image sensor; an optical system for imaging a scene onto the image sensor; an image capture control for initiating an image capture operation; a photography mode user interface for selecting between a plurality of photography modes, the photography modes having associated image capture and image processing settings; a power control for turning the digital camera on or off, wherein when the camera is in an off state and the power control is activated with a first activation pattern the digital camera is turned on and set to operate in a default photography mode and when the power control is activated with a second activation pattern the digital camera is turned on and set to operate in a previously selected photography mode.

Automatic digital camera photography mode selection

A method for identifying a set of key video frames from a video sequence comprising extracting feature vectors for each video frame and applying a group sparsity algorithm to represent the feature vector for a particular video frame as a group sparse combination of the feature vectors for the other video frames. Weighting coefficients associated with the group sparse combination are analyzed to determine video frame clusters of temporally-contiguous, similar video frames. The video sequence is segmented into scenes by identifying scene boundaries based on the determined video frame clusters.

Identifying scene boundaries using group sparsity analysis

A statistically lossless transform (SLT) can be used with data (e.g., image data) to be compressed. Prior to compression, an SLT stabilizes the variance for a read noise+Poisson process. The SLT redistributes (image) data that has been quantized with a linear uniform quantizer and essentially assures there are no gaps in the output values (i.e., all output digital values are used). The SLT re-quantizes the data with a quantization interval width that is proportional to the standard deviation of the process.

Statistically lossless compression system and method

System and method for contextual digital photography mode selection

A method for identifying a set of key video frames from a video sequence comprising extracting feature vectors for each video frame and applying a group sparsity algorithm to represent the feature vector for a particular video frame as a group sparse combination of the feature vectors for the other video frames. Weighting coefficients associated with the group sparse combination are analyzed to determine video frame clusters of temporally-contiguous, similar video frames. A summary is formed based on the determined video frame clusters.

Video summarization using group sparsity analysis

A digital video camera system that provides a video summary using a method that includes: designating a reference image containing a particular person; capturing a video sequence of the scene using the image sensor, the video sequence including a time sequence of image frames; processing the captured video sequence using a video processing path to form a digital video file; during the capturing of the video sequence, analyzing the captured image frames using a person recognition algorithm to identify a subset of the image frames that contain the particular person; forming the video summary including fewer than all of the image frames in the captured video sequence, wherein the video summary includes at least part of the identified subset of image frames containing the particular person; storing the digital video file in the storage memory; and storing a representation of the video summary in the storage memory.

Video summary including a particular person

A method for selecting a digital image having controlled sharpness characteristics from a set of candidate digital images of a common scene, each digital image having different sharpness characteristics. An image segmentation process is used to segment each of the candidate digital images into a subject region and a background region. For each candidate digital image the subject and background regions are analyzed to determine an associated subject and background sharpness levels. An output digital image is selected by comparing the determined subject and background sharpness levels to respective aim subject and background sharpness levels. In some embodiments, the aim subject and background sharpness levels are defined in accordance with a scene type classification.

Controlling the sharpness of a digital image

A method for identifying a set of key video frames from a video sequence comprising extracting feature vectors for each video frame and applying a group sparsity algorithm to represent the feature vector for a particular video frame as a group sparse combination of the feature vectors for the other video frames. Weighting coefficients associated with the group sparse combination are analyzed to determine video frame clusters of temporally-contiguous, similar video frames. A set of key video frames are selected based on the determined video frame clusters.

Identifying key frames using group sparsity analysis

A method of processing a digital image having a predetermined color pattern, includes converting the predetermined color pattern of the digital image into a converted digital image having a different desired color pattern; and using algorithms adapted for use with the desired color pattern for processing the converted digital image.

Pattern conversion for interpolation

The sharpness of a digital image is adjusted according to defined aim subject and background sharpness levels. An image segmentation process is used to segment an input digital image into a subject region and a background region. The subject and background regions are analyzed to determine corresponding subject and background sharpness levels. An enhanced digital image is formed wherein the sharpness of the subject region is adjusted responsive to the subject sharpness level and the aim subject sharpness level, and the sharpness of the background region is adjusted responsive to the background sharpness level and the aim background sharpness level. In some embodiments, the input digital image is analyzed to determined a scene type classification and the aim subject and background sharpness levels are defined in accordance with the determined scene type classification.

Adjusting the sharpness of a digital image

A digital video camera system that provides a video summary using a method that includes: specifying reference data, wherein the reference data indicates a feature of interest; capturing a video sequence of the scene using the image sensor, the video sequence including a time sequence of image frames; processing the captured video sequence using a video processing path to form a digital video file; during the capturing of the video sequence, analyzing the captured image frames using a feature recognition algorithm to identify a subset of the image frames that contain the feature of interest; forming the video summary including fewer than all of the image frames in the captured video sequence, wherein the video summary includes at least part of the identified subset of image frames containing the feature of interest; storing the digital video file in the storage memory; and storing a representation of the video summary in the storage memory.

Video summary including a feature of interest

Growing community of inventors

Rochester, NY, United States of America

Bruce Harold Pillman