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 provided for location tracking and filtering or hiding electronic communications. In one implementation, a method is provided for location tracking for electronic communications. The method includes receiving a location ID for a location from a user who desires to follow the location within a social networking environment, and associating the location ID with the user. The method also includes providing instructions to display a representation of the location in a list of the user's contacts.

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 for efficiently absorbing, archiving, and distributing any size data sets are provided. Some embodiments provide flexible, policy-based distribution of high volume data through real time streaming as well as past data replay. In addition, some embodiments provide for a foundation of solid and unambiguous consistency across any vendor system through advanced version features. This consistency is particularly valuable to the financial industry, but also extremely useful to any company that manages multiple data distribution points for improved and reliable data availability.

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: Disclosed are various embodiments labeling objects using multi-scale partitioning, rare class expansion, and/or spatial context techniques. An input image may be partitioned using different scale values to produce a different set of superpixels for each of the different scale values. Potential object labels for superpixels in each different set of superpixels of the input image may be assessed by comparing descriptors of the superpixels in each different set of superpixels of the input image with descriptors of reference superpixels in labeled reference images. An object label may then be assigned for a pixel of the input image based at least in part on the assessing of the potential object labels.

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 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: 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 for efficiently absorbing, archiving, and distributing any size data sets are provided. Some embodiments provide flexible, policy-based distribution of high volume data through real time streaming as well as past data replay. In addition, some embodiments provide for a foundation of solid and unambiguous consistency across any vendor system through advanced version features. This consistency is particularly valuable to the financial industry, but also extremely useful to any company that manages multiple data distribution points for improved and reliable data availability.

Abstract: Methods for providing an interface to refine a search query are provided. In one aspect, a method includes receiving a submission of a first search query in an input field, and providing, for display, the first search query in the input field with first search results for the search query, the first search results including at least one subset category of search results. The method also includes receiving a selection of the at least one subset category of the first search results, and providing, for display, an indicator of the selected at least one subset category in the input field with second search results for the selected at least one subset category. Systems and machine-readable media are also provided.

Abstract: Techniques are disclosed herein that enable digital images to be segmented based on a user's semantic input. In other words, given an input image of a person walking a dog adjacent to a tree, a user can simply provide the semantic input “dog” and the system will segment the dog from the other elements in the image. If the user provides other semantic input, such as “person” or “tree”, the system will segment the person or the tree, respectively, from the same image. Using semantic input advantageously eliminates any need for a user to directly interact with the input image through a tedious process of painting brush strokes, tracing boundaries, clicking target points, and/or drawing bounding boxes. Thus semantic input represents an easier and more intuitive way for users to interact with an image segmentation interface, thereby enabling novice users to take advantage of advanced image segmentation techniques.

Abstract: Techniques are disclosed herein that enable digital images to be segmented based on a user's semantic input. In other words, given an input image of a person walking a dog adjacent to a tree, a user can simply provide the semantic input “dog” and the system will segment the dog from the other elements in the image. If the user provides other semantic input, such as “person” or “tree”, the system will segment the person or the tree, respectively, from the same image. Using semantic input advantageously eliminates any need for a user to directly interact with the input image through a tedious process of painting brush strokes, tracing boundaries, clicking target points, and/or drawing bounding boxes. Thus semantic input represents an easier and more intuitive way for users to interact with an image segmentation interface, thereby enabling novice users to take advantage of advanced image segmentation techniques.

Abstract: A personal inhalation device includes an outer shell having an orifice formed therein and containing a medium having one or more deliverables and an atomizing unit disposed within the shell. The atomizing unit atomizes the medium when a user puffs on the orifice such that vapor containing the deliverables is discharged through the orifice. The personal inhalation device is further capable of metering the deliverables discharged with the vapor.

Abstract: Systems and methods for obscuring the existence of a communication system by presenting covert or intentionally deceptive information to a user interface to prevent unintended observers from determining the nature of the communication system. Communications initiated by users of the system are erased less than twenty-five hours after receipt. Notifications, provided to users of the existence of new messages, are likewise erased within a certain period of time after being viewed. Outbound email may be sent as an image and may be configured to be self-erasing upon being read. The systems and methods also provide a safety measure for erasing messages that can be employed by the user at any time. The user can enter a code that is supplied to a server that manages the communications, causing the server to erase all communications and indication of communications, such as logs, for that user.

Abstract: Disclosed are various embodiments labeling objects using multi-scale partitioning, rare class expansion, and/or spatial context techniques. An input image may be partitioned using different scale values to produce a different set of superpixels for each of the different scale values. Potential object labels for superpixels in each different set of superpixels of the input image may be assessed by comparing descriptors of the superpixels in each different set of superpixels of the input image with descriptors of reference superpixels in labeled reference images. An object label may then be assigned for a pixel of the input image based at least in part on the assessing of the potential object labels.

Abstract: Techniques are disclosed herein that enable digital images to be segmented based on a user's semantic input. In other words, given an input image of a person walking a dog adjacent to a tree, a user can simply provide the semantic input “dog” and the system will segment the dog from the other elements in the image. If the user provides other semantic input, such as “person” or “tree”, the system will segment the person or the tree, respectively, from the same image. Using semantic input advantageously eliminates any need for a user to directly interact with the input image through a tedious process of painting brush strokes, tracing boundaries, clicking target points, and/or drawing bounding boxes. Thus semantic input represents an easier and more intuitive way for users to interact with an image segmentation interface, thereby enabling novice users to take advantage of advanced image segmentation techniques.

Abstract: Techniques are disclosed herein that enable digital images to be segmented based on a user's semantic input. In other words, given an input image of a person walking a dog adjacent to a tree, a user can simply provide the semantic input “dog” and the system will segment the dog from the other elements in the image. If the user provides other semantic input, such as “person” or “tree”, the system will segment the person or the tree, respectively, from the same image. Using semantic input advantageously eliminates any need for a user to directly interact with the input image through a tedious process of painting brush strokes, tracing boundaries, clicking target points, and/or drawing bounding boxes. Thus semantic input represents an easier and more intuitive way for users to interact with an image segmentation interface, thereby enabling novice users to take advantage of advanced image segmentation techniques.