Abstract: Example embodiments may allow for the efficient, edge-preserving filtering, upsampling, or other processing of image data with respect to a reference image. A cost-minimization problem to generate an output image from the input array is mapped onto regularly-spaced vertices in a multidimensional vertex space. This mapping is based on an association between pixels of the reference image and the vertices, and between elements of the input array and the pixels of the reference image. The problem is them solved to determine vertex disparity values for each of the vertices. Pixels of the output image can be determined based on determined vertex disparity values for respective one or more vertices associated with each of the pixels. This fast, efficient image processing method can be used to enable edge-preserving image upsampling, image colorization, semantic segmentation of image contents, image filtering or de-noising, or other applications.

Abstract: Example embodiments may allow for the efficient determination of disparity information for a stereo image pair by embedding pixels of the image pair in a multidimensional dimensional vertex space. Regularly-spaced vertices in the vertex space are associated with pixels of the stereo image pair and disparity loss functions are determined for each of the vertices based on disparity loss functions of the associated pixels. The determined vertex-disparity loss functions can be used to determine vertex disparity values for each of the vertices. Disparity values for pixels of the stereo image pair can be determined based on determined vertex disparity values for respective one or more vertices associated with each of the pixels. The determined pixel disparity values can be used to enable depth-selective image processing, determination of pixel depth maps, mapping and/or navigation of an environment, human-computer interfacing, biometrics, augmented reality, or other applications.

Abstract: Embodiments described herein may help a computing device, such as a head-mountable device (HMD), to capture and process images in response to a user placing their hands in, and then withdrawing their hands from, a frame formation. For example, an HMD may analyze image data from a point-of-view camera on the HMD, and detect when a wearer holds their hands in front of their face to frame a subject in the wearer's field of view. Further, the HMD may detect when the wearer withdraws their hands from such a frame formation and responsively capture an image. Further, the HMD may determine a selection area that is being framed, within the wearer's field of view, by the frame formation. The HMD may then process the captured image based on the frame formation, such as by cropping, white-balancing, and/or adjusting exposure.