Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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? and EV+) may be used to adaptively change the bracket settings for the current image's EV? and EV+ images. In another implementation, the most recently obtained EV? and EV+ images may be used to adaptively change the bracket settings for the current image—no prior capture being necessary.

Abstract: 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? and EV+) may be used to adaptively change the bracket settings for the current image's EV? and EV+ images. In another implementation, the most recently obtained EV? and EV+ images may be used to adaptively change the bracket settings for the current image—no prior capture being necessary.

Abstract: 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? and EV+) may be used to adaptively change the bracket settings for the current image's EV? and EV+ images. In another implementation, the most recently obtained EV? and EV+ images may be used to adaptively change the bracket settings for the current image—no prior capture being necessary.

Abstract: 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.

Abstract: 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.

Abstract: 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? and EV+) may be used to adaptively change the bracket settings for the current image's EV? and EV+ images. In another implementation, the most recently obtained EV? and EV+ images may be used to adaptively change the bracket settings for the current image—no prior capture being necessary.

Abstract: A method of and an apparatus for determining a depth map utilizing a movable lens and an image sensor are described herein. The depth information is acquired by moving the lens a short distance and acquiring multiple images with different blur quantities. An improved method of simulating gaussian blur and an approximation equation that relates a known gaussian blur quantity to a known pillbox quantity are used in conjunction with a non-linear blur difference equation. The blur quantity difference is able to be calculated and then used to determine the depth map. Many applications are possible using the method and system described herein, such as autofocusing, surveillance, robot/computer vision, autonomous vehicle navigation, multi-dimensional imaging and data compression.

Abstract: A method of and an apparatus for determining a depth map utilizing a movable lens and an image sensor are described herein. The depth information is acquired by moving the lens a short distance and acquiring multiple images with different blur quantities. An improved method of simulating gaussian blur and an approximation equation that relates a known gaussian blur quantity to a known pillbox quantity are used in conjunction with a non-linear blur difference equation. The blur quantity difference is able to be calculated and then used to determine the depth map. Many applications are possible using the method and system described herein, such as autofocusing, surveillance, robot/computer vision, autonomous vehicle navigation, multi-dimensional imaging and data compression.