Abstract: This disclosure relates to various implementations that dynamically adjust one or more shallow depth of field (SDOF) parameters based on a designated, artificial aperture value. The implementations obtain a designated, artificial aperture value that modifies an initial aperture value for an image frame. The designated, artificial aperture value generates a determined amount of synthetically-produced blur within the image frame. The implementations determine an aperture adjustment factor based on the designated, artificial aperture value in relation to a default so-called “tuning aperture value” (for which the camera's operations may have been optimized). The implementations may then modify, based on the aperture adjustment factor, one or more SDOF parameters for an SDOF operation, which may, e.g., be configured to render a determined amount of synthetic bokeh within the image frame.

Abstract: This disclosure pertains to systems, methods, and computer readable medium for mapping particular user interactions, e.g., gestures, to the input parameters of various image processing routines, e.g., image filters, in a way that provides a seamless, dynamic, and intuitive experience for both the user and the software developer. Such techniques may handle the processing of both “relative” gestures, i.e., those gestures having values dependent on how much an input to the device has changed relative to a previous value of the input, and “absolute” gestures, i.e., those gestures having values dependent only on the instant value of the input to the device. Additionally, inputs to the device beyond user-input gestures may be utilized as input parameters to one or more image processing routines. For example, the device's orientation, acceleration, and/or position in three-dimensional space may be used as inputs to particular image processing routines.

Abstract: This disclosure relates to techniques for the robust usage of semantic segmentation information in image processing techniques, e.g., shallow depth of field (SDOF) renderings. Semantic segmentation may be defined as a process of creating a mask over an image, wherein pixels are segmented into a predefined set of semantic classes. Segmentations may be binary (e.g., a ‘person pixel’ or a ‘non-person pixel’) or multi-class (e.g., a pixel may be labelled as: ‘person,’ ‘dog,’ ‘cat,’ etc.). As semantic segmentation techniques grow in accuracy and adoption, it is becoming increasingly important to develop methods of utilizing such segmentations and developing flexible techniques for integrating segmentation information into existing computer vision applications, such as synthetic SDOF renderings, to yield improved results in a wide range of image capture scenarios.

Abstract: This disclosure relates to various implementations that dynamically adjust one or more shallow depth of field (SDOF) parameters based on a designated, artificial aperture value. The implementations obtain a designated, artificial aperture value that modifies an initial aperture value for an image frame. The designated, artificial aperture value generates a determined amount of synthetically-produced blur within the image frame. The implementations determine an aperture adjustment factor based on the designated, artificial aperture value in relation to a default so-called “tuning aperture value” (for which the camera's operations may have been optimized). The implementations may then modify, based on the aperture adjustment factor, one or more SDOF parameters for an SDOF operation, which may, e.g., be configured to render a determined amount of synthetic bokeh within the image frame.

Abstract: This disclosure relates to various implementations that dynamically adjust one or more shallow depth of field (SDOF) parameters based on a designated, artificial aperture value. The implementations obtain a designated, artificial aperture value that modifies an initial aperture value for an image frame. The designated, artificial aperture value generates a determined amount of synthetically-produced blur within the image frame. The implementations determine an aperture adjustment factor based on the designated, artificial aperture value in relation to a default so-called “tuning aperture value” (for which the camera's operations may have been optimized). The implementations may then modify, based on the aperture adjustment factor, one or more SDOF parameters for an SDOF operation, which may, e.g., be configured to render a determined amount of synthetic bokeh within the image frame.

Abstract: This disclosure relates to techniques for synthesizing out of focus blurring effects in digital images. Cameras having wide aperture lenses typically capture images with a shallow depth of field (SDOF). SDOF cameras are often used in portrait photography, since they emphasize subjects, while deemphasizing the background via blurring. Simulating this kind of blurring using a large depth of field (LDOF) camera may require a large amount of computational resources, i.e., to simulate the physical effects of using a wide aperture lens, while constructing a synthetic SDOF image. Moreover, cameras having smaller lens apertures, such as those in mobile phones, may not have the ability to accurately estimate or recreate the true color of clipped background light sources. Described herein are techniques to synthesize out of focus background blurring effects that attempt to reproduce accurate light intensity and color values for clipped background light sources in images captured by LDOF cameras.

Abstract: This disclosure pertains to systems, methods, and computer readable medium for mapping particular user interactions, e.g., gestures, to the input parameters of various image processing routines, e.g., image filters, in a way that provides a seamless, dynamic, and intuitive experience for both the user and the software developer. Such techniques may handle the processing of both “relative” gestures, i.e., those gestures having values dependent on how much an input to the device has changed relative to a previous value of the input, and “absolute” gestures, i.e., those gestures having values dependent only on the instant value of the input to the device. Additionally, inputs to the device beyond user-input gestures may be utilized as input parameters to one or more image processing routines. For example, the device's orientation, acceleration, and/or position in three-dimensional space may be used as inputs to particular image processing routines.

Abstract: This disclosure pertains to systems, methods, and computer readable medium for mapping particular user interactions, e.g., gestures, to the input parameters of various image processing routines, e.g., image filters, in a way that provides a seamless, dynamic, and intuitive experience for both the user and the software developer. Such techniques may handle the processing of both “relative” gestures, i.e., those gestures having values dependent on how much an input to the device has changed relative to a previous value of the input, and “absolute” gestures, i.e., those gestures having values dependent only on the instant value of the input to the device. Additionally, inputs to the device beyond user-input gestures may be utilized as input parameters to one or more image processing routines. For example, the device's orientation, acceleration, and/or position in three-dimensional space may be used as inputs to particular image processing routines.

Abstract: Embodiments are directed toward systems and methods segment an input image for performance of a warp kernel that executes by a graphics processing unit (GPU) the warp kernel on an array of dummy data, wherein cells of the array are populated with data representing the cells' respective locations within the array, determine, from an output array obtained from execution of the warp kernel on the dummy data, a segmentation size, and build by the GPU an output image from the input image by executing the warp kernel on the input image according to the segmentation size.

Abstract: This disclosure relates to techniques for the robust usage of semantic segmentation information in image processing techniques, e.g., shallow depth of field (SDOF) renderings. Semantic segmentation may be defined as a process of creating a mask over an image, wherein pixels are segmented into a predefined set of semantic classes. Segmentations may be binary (e.g., a ‘person pixel’ or a ‘non-person pixel’) or multi-class (e.g., a pixel may be labelled as: ‘person,’ ‘dog,’ ‘cat,’ etc.). As semantic segmentation techniques grow in accuracy and adoption, it is becoming increasingly important to develop methods of utilizing such segmentations and developing flexible techniques for integrating segmentation information into existing computer vision applications, such as synthetic SDOF renderings, to yield improved results in a wide range of image capture scenarios.

Abstract: This disclosure pertains to systems, methods, and computer readable medium for mapping particular user interactions, e.g., gestures, to the input parameters of various image processing routines, e.g., image filters, in a way that provides a seamless, dynamic, and intuitive experience for both the user and the software developer. Such techniques may handle the processing of both “relative” gestures, i.e., those gestures having values dependent on how much an input to the device has changed relative to a previous value of the input, and “absolute” gestures, i.e., those gestures having values dependent only on the instant value of the input to the device. Additionally, inputs to the device beyond user-input gestures may be utilized as input parameters to one or more image processing routines. For example, the device's orientation, acceleration, and/or position in three-dimensional space may be used as inputs to particular image processing routines.

Abstract: This disclosure pertains to systems, methods, and computer readable medium for mapping particular user interactions, e.g., gestures, to the input parameters of various image processing routines, e.g., image filters, in a way that provides a seamless, dynamic, and intuitive experience for both the user and the software developer. Such techniques may handle the processing of both “relative” gestures, i.e., those gestures having values dependent on how much an input to the device has changed relative to a previous value of the input, and “absolute” gestures, i.e., those gestures having values dependent only on the instant value of the input to the device. Additionally, inputs to the device beyond user-input gestures may be utilized as input parameters to one or more image processing routines. For example, the device's orientation, acceleration, and/or position in three-dimensional space may be used as inputs to particular image processing routines.

Abstract: Disabling wireless access point notifications is described. In some implementations, wireless access point notifications on a mobile device can be disabled when movement of the mobile device is detected. In some implementations, wireless access point notifications on a mobile device can be re-enabled when the mobile device detects that the mobile device has been stationary for a specified period of time following movement of the mobile device. In some implementations, wireless access point notifications can be disabled on a per access point basis.

Abstract: This disclosure pertains to systems, methods, and computer readable medium for mapping particular user interactions, e.g., gestures, to the input parameters of various image processing routines, e.g., image filters, in a way that provides a seamless, dynamic, and intuitive experience for both the user and the software developer. Such techniques may handle the processing of both “relative” gestures, i.e., those gestures having values dependent on how much an input to the device has changed relative to a previous value of the input, and “absolute” gestures, i.e., those gestures having values dependent only on the instant value of the input to the device. Additionally, inputs to the device beyond user-input gestures may be utilized as input parameters to one or more image processing routines. For example, the device's orientation, acceleration, and/or position in three-dimensional space may be used as inputs to particular image processing routines.

Abstract: This disclosure pertains to systems, methods, and computer readable medium for mapping particular user interactions, e.g., gestures, to the input parameters of various image processing routines, e.g., image filters, in a way that provides a seamless, dynamic, and intuitive experience for both the user and the software developer. Such techniques may handle the processing of both “relative” gestures, i.e., those gestures having values dependent on how much an input to the device has changed relative to a previous value of the input, and “absolute” gestures, i.e., those gestures having values dependent only on the instant value of the input to the device. Additionally, inputs to the device beyond user-input gestures may be utilized as input parameters to one or more image processing routines. For example, the device's orientation, acceleration, and/or position in three-dimensional space may be used as inputs to particular image processing routines.

Abstract: Embodiments are directed toward systems and methods segment an input image for performance of a warp kernel that executes by a graphics processing unit (GPU) the warp kernel on an array of dummy data, wherein cells of the array are populated with data representing the cells' respective locations within the array, determine, from an output array obtained from execution of the warp kernel on the dummy data, a segmentation size, and build by the GPU an output image from the input image by executing the warp kernel on the input image according to the segmentation size.

Abstract: Disabling wireless access point notifications is described. In some implementations, wireless access point notifications on a mobile device can be disabled when movement of the mobile device is detected. In some implementations, wireless access point notifications on a mobile device can be re-enabled when the mobile device detects that the mobile device has been stationary for a specified period of time following movement of the mobile device. In some implementations, wireless access point notifications can be disabled on a per access point basis.

Abstract: Disabling wireless access point notifications is described. In some implementations, wireless access point notifications on a mobile device can be disabled when movement of the mobile device is detected. In some implementations, wireless access point notifications on a mobile device can be re-enabled when the mobile device detects that the mobile device has been stationary for a specified period of time following movement of the mobile device. In some implementations, wireless access point notifications can be disabled on a per access point basis.

Abstract: A technique for optimizing the rendering of such complex render-graphs caches intermediate buffers of nodes that are expected to be re-used after they've been rendered. The render-graph is examined to determine the number of re-uses of each node's output buffer, and the buffer is cached in memory until all the re-uses of the buffer have occurred. Once all the re-uses of the buffer have occurred, the buffer is removed from the cache. This technique guarantees that for a given render-graph, no nodes will be re-rendered, resulting in improved render performance.

Abstract: This disclosure pertains to systems, methods, and computer readable medium for mapping particular user interactions, e.g., gestures, to the input parameters of various image processing routines, e.g., image filters, in a way that provides a seamless, dynamic, and intuitive experience for both the user and the software developer. Such techniques may handle the processing of both “relative” gestures, i.e., those gestures having values dependent on how much an input to the device has changed relative to a previous value of the input, and “absolute” gestures, i.e., those gestures having values dependent only on the instant value of the input to the device. Additionally, inputs to the device beyond user-input gestures may be utilized as input parameters to one or more image processing routines. For example, the device's orientation, acceleration, and/or position in three-dimensional space may be used as inputs to particular image processing routines.