Sony Corporation

Sony Electronics Inc

Apple Inc.

Earl Quong Wong

Makibi Nakamura

Hidenori Kushida

Pingshan Li

Soroj Triteyaprasert

Yoshihiro Murakami

Mamoru Sugiura

Akira Matsui

Alexander Berestov

Eric D Edwards

Garrett M Johnson

Kensuke Miyagi

Steven David Hordley

Maikibi Nakamura

Wrist-worn devices and methods for measuring blood oxygen saturation using a wrist-worn device compute blood oxygen saturation by processing an output signal from one or more photodetectors indicative of absorption of light by a finger interfaced with the one or more photodetectors. A method includes transmitting a first wavelength light into a finger from a first light emitter mounted to a wrist band of the wrist-worn device. A second wavelength light is transmitted into the finger from a second light emitter mounted to the wrist band. An output signal indicative of absorption by the finger of the first wavelength light and the second wavelength light is generated by one or more photodetectors interfaced with the finger and disposed on a housing of the wrist-worn device. The output signal is processed with a processor disposed in the housing to compute blood oxygen saturation.

Wrist-worn device and method for accurate blood oxygen saturation measurement

Techniques and devices for post-processing time-lapse videos are described. The techniques include obtaining an input time-lapse sequence of frames and determining a visual metric value, e.g., average luminance, for each frame. A curve of best fit may then be determined for the visual metric values of the frames. The visual metric values, e.g., the average luminance values, of the plurality of frames may then be adjusted, e.g., by adjusting the visual metric values of each frame to be equal to the corresponding value determined by the curve of best fit. Some embodiments include further adjusting the visual metric values to be equal to a weighted average of the adjusted visual metric values for adjacent frames in the time-lapse sequence. Finally, a visual characteristic of the frames, e.g., an image histogram, may be adjusted based on the frame's determined adjusted visual metric value, and an output time-lapse sequence may be generated.

Systems and methods for post processing time-lapse videos

Techniques to adaptively select bracket settings during auto-exposure bracket (AEB) operations are described. In general, AEB settings for a current image may be based on the evaluation of prior bracketed images. For example, the current exposure setting established by an auto-exposure mechanism (EV0) may be compared with a prior EV0 image. If the two are consistent, the prior image's lower and upper f-stop setting images (EV&#x2212; and EV+) may be used to adaptively change the bracket settings for the current image's EV&#x2212; and EV+ images. In another implementation, the most recently obtained EV&#x2212; and EV+ images may be used to adaptively change the bracket settings for the current image&#x2014;no prior capture being necessary.

Intelligent auto-exposure bracketing

Techniques to improve image fusing operations using intensity mapping functions (IMFs) are described. In one approach, when a reference image's pixel values are within its' IMF's useful range, they may be used to generate predicted secondary image pixel values. When the reference image's pixel values are not within the IMF's useful range, actual values from a captured secondary image may be used directly or processed further to generate predicted secondary image pixel values. The predicted and actual pixel values may be used to construct predicted secondary images that may be fused. In another approach, the consistency between pixel pairs may be used to generate consistency-based weighting factors that may be used during image fusion operations.

Image fusion using intensity mapping functions

A fast, accurate and efficient Gaussian filter implements a box filter implementation, applies the central limit theorem and uses an overflow implementation. By combining the box filter, central limit theorem and overflow, the filter is fast, accurate and efficient so that it is able to be implemented in hardware and/or software easily.

Fast, accurate and efficient gaussian filter

A super-resolution image is generated from a sequence of low resolution images. In one embodiment, the image shift information is measured for each of the low resolution images using an image stabilization component of an imaging device. The shift information is used to generate the super-resolution image. In another embodiment, the blurs are calculated for each of the low resolution images and are used to generate the super-resolution image.

Method and apparatus for super-resolution imaging using digital imaging devices

Tunable Gaussian filters enable imaging effects to be applied to images and videos in orientations other than standard symmetric, 0 degree orientations and 90 degree orientations. The tunable Gaussian filters are able to be applied in any orientation such as 45 degrees, slightly less than 45 degrees and slightly more than 0 degrees.

Tunable gaussian filters

An imaging system generates a picture depth map from a pair of reduced resolution images. The system captures two full resolution images, receives image reduction image information and creates two reduced resolution images. The system computes a blur difference between the two reduced resolution images at different image locations. The system calculates the depth map based on the blur difference between the two reduced resolution images at different image locations.

Reduced hardware implementation for a two-picture depth map algorithm

A two picture matching curve information is able to be used to determine precise object distance or relative object distance in a scene. Acquiring two images with different blur information in addition to the curve information enables a device to determine distance information of objects in a scene. The distance information is able to be used in image processing including generating a depth map which is then able to be used in many imaging applications.

Algorithms for estimating precise and relative object distances in a scene

Apparatus and method for electronically estimating focusing distance between a camera (still and/or video camera) and a subject. Images at different focal positions of a calibration target are collected with distances between subject positions. In one aspect, histogram matching is performed to reduce noise error. A focus matching model is then generated in response to detected blur differences between successive images of the calibration target. The focus matching model is preferably converted to a polynomial equation of a desired order to smooth out image collection noise. The focus matching model is stored for access during operation. In use, the distance to subject is estimated in response to capturing images, detecting blur differences between the images and entering the blur difference information into the matching model.

Two-dimensional polynomial model for depth estimation based on two-picture matching

A camera auto-focuses using computed blur differences between images of a three-dimensional scene. The camera computes the blur difference between two images of the scene acquired at two different picture numbers. The camera uses the computed blur difference to predict a third picture number, where the camera uses the third picture number to auto-focus a camera lens on the scene.

Fast camera auto-focus

Camera depth estimation is performed in response to picture matching based on blur difference computed between images captured at different focal positions. A blur difference model is stored in the camera based on characterization of the camera with a series of matching curves in which blur difference varies depending on the focal length, aperture, subject distance, and lens focus position. A four-dimensional polynomial model is created to fit the matching curves for use in estimating subject distance. During operation, images are captured for use in estimating subject distance. Motion compensation is applied and blur difference is determined. Blur difference is utilized in the polynomial model to estimate subject distance. Subject distance estimates can be output or utilized within an auto focus process to provide accurate focus adjustments.

Four-dimensional polynomial model for depth estimation based on two-picture matching

An imaging acquisition system that generates a picture depth from an auto focus curve generated from picture of a three dimensional spatial scene is described. The auto focus curve comprises a step edge. The system generates the depth based on the step edge and a reference auto focus normalization curve.

Simple method for calculating camera defocus from an image scene

An imaging acquisition system that generates a depth map from two pictures of a three dimensional spatial scene is described. According to one aspect of the invention, the system generates the depth map based on the relative blur between the two pictures and the absolute blur contributed by the system. According to another aspect of the invention, the system calculates the depth map directly from the relative blur between the two pictures.

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Vallejo, CA, United States of America

Earl Quong Wong