Apple Inc.

Other

Claus Molgaard

Paul M Hubel

Marius Tico

Rolf Toft

Marco Zuliani

Farhan A Baqai

Todd Stephen Sachs

Fabio Riccardi

Frank Doepke

George E Williams

Brett M Keating

Russell A Pflughaupt

Vincent Y Wong

Ethan J Tira-Thompson

Gennadiy A Agranov

Jianping Zhou

Xiangli Li

Chiajen Lee

Kevin L Hunter

Gijesh Varghese

Thomas E Bishop

Ralph T Brunner

Rudolph Van Der Merwe

Xuemei Zhang

Justin S Titi

Jason Klivington

Elliott B Harris

Sebastien X Beysserie

Alex Tremain Nelson

Charles A Mezak

Iain A McAllister

Ashirwad Bahukhandi

Douglas P Mitchell

Arwen V Bradley

Alexander Lindskog

Anita Nariani Schulze

Ziv Attar

Karl Hsu

Michael Rousson

Benjamin M Olson

Zahra Sadeghipoor Kermani

Sebastien Marineau-Mes

Ilana Volfin

Jeff Brasket

Iain A Mcallister

Some embodiments include methods and/or systems for using multiple cameras to provide optical zoom to a user. Some embodiments include a first camera unit of a multifunction device capturing a first image of a first visual field. A second camera unit of the multifunction device simultaneously captures a second image of a second visual field. In some embodiments, the first camera unit includes a first optical package with a first focal length. In some embodiments, the second camera unit includes a second optical package with a second focal length. In some embodiments, the first focal length is different from the second focal length, and the first visual field is a subset of the second visual field. In some embodiments, the first image and the second image are preserved to a storage medium as separate data structures.

Mobile zoom using multiple prime cameras

Mobile camera system

The invention relates to systems, methods, and computer readable media for responding to a user snapshot request by capturing anticipatory pre-snapshot image data as well as post-snapshot image data. The captured information may be used, depending upon the embodiment, to create archival image information and image presentation information that is both useful and pleasing to a user. The captured information may automatically be trimmed or edited to facilitate creating an enhanced image, such as a moving still image. Varying embodiments of the invention offer techniques for trimming and editing based upon the following: exposure, brightness, focus, white balance, detected motion of the camera, substantive image analysis, detected sound, image metadata, and/or any combination of the foregoing.

Method and apparatus for finding and using video portions that are relevant to adjacent still images

Techniques are described for automated analysis and filtering of image data. Image data is analyzed to identify regions of interest (ROIs) within the image content. The image data also may have depth estimates applied to content therein. One or more of the ROIs may be designated to possess a base depth, representing a depth of image content against which depths of other content may be compared. Moreover, the depth of the image content within a spatial area of an ROI may be set to be a consistent value, regardless of depth estimates that may have been assigned from other sources. Thereafter, other elements of image content may be assigned content adjustment values in gradients based on their relative depth in image content as compared to the base depth and, optionally, based on their spatial distance from the designated ROI. Image content may be adjusted based on the content adjustment values.

Image adjustments based on depth of field estimations

Pixel binning is performed by summing charge from some pixels positioned diagonally in a pixel array. Pixel signals output from pixels positioned diagonally in the pixel array may be combined on the output lines. A signal representing summed charge produces a binned 2&#xd7;1 cluster. A signal representing combined voltage signals produces a binned 2&#xd7;1 cluster. A signal representing summed charge and a signal representing combined pixel signals can be combined digitally to produce a binned 2&#xd7;2 pixel. Orthogonal binning may be performed on other pixels in the pixel array by summing charge on respective common sense regions and then combining the voltage signals that represent the summed charge on respective output lines.

Pixel binning in an image sensor

Systems and methods for improving automatic selection of keeper images from a commonly captured set of images are described. A combination of image type identification and image quality metrics may be used to identify one or more images in the set as keeper images. Image type identification may be used to categorize the captured images into, for example, three or more categories. The categories may include portrait, action, or 'other.' Depending on the category identified, the images may be analyzed differently to identify keeper images. For portrait images, an operation may be used to identify the best set of faces. For action images, the set may be divided into sections such that keeper images selected from each section tell the story of the action. For the 'other' category, the images may be analyzed such that those having higher quality metrics for an identified region of interest are selected.

Automated selection of keeper images from a burst photo captured set

Techniques to capture and fuse short- and long-exposure images of a scene from a stabilized image capture device are disclosed. More particularly, the disclosed techniques use not only individual pixel differences between co-captured short- and long-exposure images, but also the spatial structure of occluded regions in the long-exposure images (e.g., areas of the long-exposure image(s) exhibiting blur due to scene object motion). A novel device used to represent this feature of the long-exposure image is a 'spatial difference map.' Spatial difference maps may be used to identify pixels in the short- and long-exposure images for fusion and, in one embodiment, may be used to identify pixels from the short-exposure image(s) to filter post-fusion so as to reduce visual discontinuities in the output image.

Scene motion correction in fused image systems

Generating an image with a selected level of background blur includes capturing, by a first image capture device, a plurality of frames of a scene, wherein each of the plurality of frames has a different focus depth, obtaining a depth map of the scene, determining a target object and a background in the scene based on the depth map, determining a goal blur for the background, and selecting, for each pixel in an output image, a corresponding pixel from the focus stack.

Photo-realistic shallow depth-of-field rendering from focal stacks

Techniques to capture and fuse short- and long-exposure images of a scene from a stabilized image capture device are disclosed. More particularly, the disclosed techniques use not only individual pixel differences between co-captured short- and long-exposure images, but also the spatial structure of occluded regions in the long-exposure images (e.g., areas of the long-exposure image(s) exhibiting blur due to scene object motion). A novel device used to represent this feature of the long-exposure image is a 'spatial difference map.' Spatial difference maps may be used to identify pixels in the short-and long-exposure images for fusion and, in one embodiment, may be used to identify pixels from the short-exposure image(s) to filter post-fusion so as to reduce visual discontinuities in the output image.

A method for generating a depth map is described. The method includes obtaining a first image of a scene from a first image capture unit, the first image having a first depth-of-field (DOF), obtaining a second image of the scene from a second image capture unit, the second image having a second DOF that is different than the first DOF. Each pixel in the second image has a corresponding pixel in the first image. The method also includes generating a plurality of third images, each corresponding to a blurred version of the second image at each of a plurality of specified depths, generating a plurality of fourth images, each representing a difference between the first image and one or the plurality of third images, and generating a depth map where each pixel in the depth map is based on the pixels in one of the plurality of fourth images.

Depth map calculation in a stereo camera system

Systems, methods, and computer readable media for calibrating two cameras (image capture units) using a non-standard, and initially unknown, calibration object are described. More particularly, an iterative approach to determine the structure and pose of an target object in an unconstrained environment are disclosed. The target object may be any of a number of predetermined objects such as a specific three dimensional (3D) shape, a specific type of animal (e.g., dogs), or the face of an arbitrary human. Virtually any object whose structure may be expressed in terms of a relatively low dimensional parametrized model may be used as a target object. The identified object (i.e., its pose and shape) may be used as input to a bundle adjustment operation resulting in camera calibration.

Camera pair calibration using non-standard calibration objects

Pixel binning is performed by summing charge from some pixels positioned diagonally in a pixel array. Pixel signals output from pixels positioned diagonally in the pixel array may be combined on the output lines. A signal representing summed charge produces a binned 2&#xd7;1 cluster. A signal representing combined voltage signals produces a binned 2&#xd7;1 cluster. A signal representing summed charge and a signal representing combined pixel signals can be combined digitally to produce a binned 2&#xd7;2 pixel. Orthogonal binning may be performed on other pixels in the pixel array by summing charge on respective common sense regions and then then combining the voltage signals that represent the summed charge on respective output lines.

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Los Gatos, CA, United States of America

Claus Molgaard