Abstract: Systems and methods are disclosed herein for using one or more computing devices to automatically segment an object in an image by referencing a dataset of already-segmented images. The technique generally involves identifying a patch of an already-segmented image in the dataset based on the patch of the already-segmented image being similar to an area of the image including a patch of the image. The technique further involves identifying a mask of the patch of the already-segmented image, the mask representing a segmentation in the already-segmented image. The technique also involves segmenting the object in the image based on at least a portion of the mask of the patch of the already-segmented image.

Abstract: Systems and methods are disclosed herein for using one or more computing devices to automatically segment an object in an image by referencing a dataset of already-segmented images. The technique generally involves identifying a patch of an already-segmented image in the dataset based on the patch of the already-segmented image being similar to an area of the image including a patch of the image. The technique further involves identifying a mask of the patch of the already-segmented image, the mask representing a segmentation in the already-segmented image. The technique also involves segmenting the object in the image based on at least a portion of the mask of the patch of the already-segmented image.

Abstract: Systems and methods are disclosed for identifying depth refinement image capture instructions for capturing images that may be used to refine existing depth maps. The depth refinement image capture instructions are determined by evaluating, at each image patch in an existing image corresponding to the existing depth map, a range of possible depth values over a set of configuration settings. Each range of possible depth values corresponds to an existing depth estimate of the existing depth map. This evaluation enables selection of one or more configuration settings in a manner such that there will be additional depth information derivable from one or more additional images captured with the selected configuration settings. When a refined depth map is generated using the one or more additional images, this additional depth information is used to increase the depth precision for at least one depth estimate from the existing depth map.

Abstract: Depth maps are generated from two or more of images captured with a conventional digital camera from the same viewpoint using different configuration settings, which may be arbitrarily selected for each image. The configuration settings may include aperture and focus settings and/or other configuration settings capable of introducing blur into an image. The depth of a selected image patch is evaluated over a set of discrete depth hypotheses using a depth likelihood function modeled to analyze corresponding images patches convolved with blur kernels using a flat prior in the frequency domain. In this way, the depth likelihood function may be evaluated without first reconstructing an all-in-focus image. Blur kernels used in the depth likelihood function and are identified from a mapping of depths and configuration settings to the blur kernels. This mapping is determined from calibration data for the digital camera used to capture the two or more images.

Abstract: Systems and methods are disclosed for identifying depth refinement image capture instructions for capturing images that may be used to refine existing depth maps. The depth refinement image capture instructions are determined by evaluating, at each image patch in an existing image corresponding to the existing depth map, a range of possible depth values over a set of configuration settings. Each range of possible depth values corresponds to an existing depth estimate of the existing depth map. This evaluation enables selection of one or more configuration settings in a manner such that there will be additional depth information derivable from one or more additional images captured with the selected configuration settings. When a refined depth map is generated using the one or more additional images, this additional depth information is used to increase the depth precision for at least one depth estimate from the existing depth map.

Abstract: Depth maps are generated from two or more of images captured with a conventional digital camera from the same viewpoint using different configuration settings, which may be arbitrarily selected for each image. The configuration settings may include aperture and focus settings and/or other configuration settings capable of introducing blur into an image. The depth of a selected image patch is evaluated over a set of discrete depth hypotheses using a depth likelihood function modeled to analyze corresponding images patches convolved with blur kernels using a flat prior in the frequency domain. In this way, the depth likelihood function may be evaluated without first reconstructing an all-in-focus image. Blur kernels used in the depth likelihood function and are identified from a mapping of depths and configuration settings to the blur kernels. This mapping is determined from calibration data for the digital camera used to capture the two or more images.

Abstract: Systems and methods are disclosed for identifying depth refinement image capture instructions for capturing images that may be used to refine existing depth maps. The depth refinement image capture instructions are determined by evaluating, at each image patch in an existing image corresponding to the existing depth map, a range of possible depth values over a set of configuration settings. Each range of possible depth values corresponds to an existing depth estimate of the existing depth map. This evaluation enables selection of one or more configuration settings in a manner such that there will be additional depth information derivable from one or more additional images captured with the selected configuration settings. When a refined depth map is generated using the one or more additional images, this additional depth information is used to increase the depth precision for at least one depth estimate from the existing depth map.

Abstract: Systems and methods are disclosed for identifying image capture instructions for capturing images that may be used to generate quality depth maps. In some examples, the image capture instructions are generated by predictively determining in a scene-independent manner configuration settings to be used by a camera to capture a minimal quantity of images for generating the quality depth map. The image capture instructions may thus indicate a quantity of images to be captured and the aperture and focus settings to be used when capturing the images. The image capture instructions may be determined based in part on a distance estimate, camera calibration information and a predetermined range of optimal blur radii. The range of optimal blur radii ensures that there will be sufficient depth information for generating a depth map of a particular quality from the yet-to-be-captured images.

Abstract: Depth maps are generated from two or more of images captured with a conventional digital camera from the same viewpoint using different configuration settings, which may be arbitrarily selected for each image. The configuration settings may include aperture and focus settings and/or other configuration settings capable of introducing blur into an image. The depth of a selected image patch is evaluated over a set of discrete depth hypotheses using a depth likelihood function modeled to analyze corresponding images patches convolved with blur kernels using a flat prior in the frequency domain. In this way, the depth likelihood function may be evaluated without first reconstructing an all-in-focus image. Blur kernels used in the depth likelihood function and are identified from a mapping of depths and configuration settings to the blur kernels. This mapping is determined from calibration data for the digital camera used to capture the two or more images.

Abstract: Techniques are disclosed for segmenting an image frame of a live camera feed. A biasing scheme can be used to initially localize pixels within the image that are likely to contain the object being segmented. An optimization algorithm for an energy optimization function, such as a graph cut algorithm, can be used with a non-localized neighborhood graph structure and the initial location bias for localizing pixels in the image frame representing the object. Subsequently, a matting algorithm can be used to define a pixel mask surrounding at least a portion of the object boundary. The bias and the pixel mask can be continuously updated and refined as the image frame changes with the live camera feed.

Abstract: This document provides methods and devices involved in medical treatment of morbid obesity. For example, this document provides methods and devices for reducing the digestive efficiency of the intestines by decreasing the arterial blood supply to the intestines.

Abstract: An automatic or semi-automatic magazine reloading firearm is described that includes a magazine track that stores one or more reserve magazines. After ejecting an active magazine from the active magazine well, a reload arrangement can be activated to move a reserve magazine from the magazine track into the active magazine well and lock it into place to allow for continued firing, increased safety, and positive user control.

Abstract: An automatic or semi-automatic magazine reloading firearm is described that includes a magazine track that stores one or more reserve magazines. After ejecting an active magazine from the active magazine well, a reload arrangement can be activated to move a reserve magazine from the magazine track into the active magazine well and lock it into place to allow for continued firing, increased safety, and positive user control.

Abstract: An automatic or semi-automatic magazine reloading firearm is described that includes a magazine track that stores one or more reserve magazines. After ejecting an active magazine from the active magazine well, a reload arrangement can be activated to move a reserve magazine from the magazine track into the active magazine well and lock it into place to allow for continued firing, increased safety, and positive user control.