Abstract: In general, the subject matter described in this disclosure can be embodied in methods, systems, and program products for indicating a reference song. A computing device stores reference song characterization data that identifies a plurality of audio characteristics for each reference song in a plurality of reference songs. The computing device receives digital audio data that represents audio recorded by a microphone, converts the digital audio data from time-domain format into frequency-domain format, and uses the digital audio data in the frequency-domain format in a music-characterization process. In response to determining that characterization values for the digital audio data are most relevant to characterization values for a particular reference song, the computing device outputs an indication of the particular reference song.

Abstract: In general, the subject matter described in this disclosure can be embodied in methods, systems, and program products. A computing device stores reference song characterization data and receives digital audio data. The computing device determines whether the digital audio data represents music and then performs a different process to recognize that the digital audio data represents a particular reference song. The computing device then outputs an indication of the particular reference song.

Abstract: The present disclosure provides an image capture, curation, and editing system that includes a resource-efficient mobile image capture device that continuously captures images. In particular, the present disclosure provides low power frameworks for controlling image sensor mode in a mobile image capture device. On example low power frame work includes a scene analyzer that analyzes a scene depicted by a first image and, based at least in part on such analysis, causes an image sensor control signal to be provided to an image sensor to adjust at least one of the frame rate and the resolution of the image sensor.

Abstract: An apparatus is described. The apparatus includes a smart image sensor having a memory and a processor that are locally integrated with an image sensor. The memory is to store first program code to be executed by the processor. The memory is coupled to the image sensor and the processor. The memory is to store second program code to be executed by the processor. The first program code is to cause the smart image sensor to perform an analysis on one or more images captured by the image sensor. The analysis identifies a region of interest within the one or more images with machine learning from previously captured images. The second program code is to cause the smart image sensor to change an image sensing and/or optical parameter in response to the analysis of the one or more images performed by the execution of the first program code.

Abstract: The present disclosure provides an image capture, curation, and editing system that includes a resource-efficient mobile image capture device that continuously captures images. In particular, the present disclosure provides low power frameworks for controlling image sensor mode in a mobile image capture device. On example low power frame work includes a scene analyzer that analyzes a scene depicted by a first image and, based at least in part on such analysis, causes an image sensor control signal to be provided to an image sensor to adjust at least one of the frame rate and the resolution of the image sensor.

Abstract: Computer-implemented methods and systems of updating user identifiers in an image-sharing environment include features for facilitating blocking, permitting, sharing and/or modifying content such as images and videos. User identification vectors providing data representative of a user and information about one or more facial characteristics of the user are broadcasted by a modular computing device. Information about one or more additional characteristics of the user (e.g., body characteristics and/or contextual characteristics) as determined from images of the user obtained by one or more image capture devices are received. An updated user identification vector including the information about one or more additional characteristics of the user is stored at and subsequently broadcasted by the modular computing device.

Abstract: An integrated stacked and/or abutted sensor, memory and processing hardware camera solution is described. The sensor is to receive light from an image and generate electronic pixels from the light. The processing hardware is to process the electronic pixels to: a) recognize a scene from the image in a lower quality image mode; b) trigger actions by the camera solution in response to the recognition of the scene, the actions including: i) transitioning the camera solution from the lower quality image mode to a higher quality image mode to capture a higher quality version of the image; and, ii) forwarding from the camera solution important imagery and not forwarding from the camera solution unimportant imagery.

Abstract: An apparatus is described. The apparatus includes a smart image sensor having a memory and a processor that are locally integrated with an image sensor. The memory is to store first program code to be executed by the processor. The memory is coupled to the image sensor and the processor. The memory is to store second program code to be executed by the processor. The first program code is to cause the smart image sensor to perform an analysis on one or more images captured by the image sensor. The analysis identifies a region of interest within the one or more images with machine learning from previously captured images. The second program code is to cause the smart image sensor to change an image sensing and/or optical parameter in response to the analysis of the one or more images performed by the execution of the first program code. Alternatively or in combination, the memory is to store third program code to be executed by the processor and fourth program code to be executed by the processor.

Abstract: The present disclosure provides an image capture, curation, and editing system that includes a resource-efficient mobile image capture device that continuously captures images. The mobile image capture device is operable to input an image into at least one neural network and to receive at least one descriptor of the desirability of a scene depicted by the image as an output of the at least one neural network. The mobile image capture device is operable to determine, based at least in part on the at least one descriptor of the desirability of the scene of the image, whether to store a second copy of such image and/or one or more contemporaneously captured images in a non-volatile memory of the mobile image capture device or to discard a first copy of such image from a temporary image buffer without storing the second copy of such image in the non-volatile memory.

Abstract: Systems and methods prevent or restrict the mining of content on a mobile device. For example, a method may include identifying a mining-restriction mark in low order bits or high order bits in a frame buffer of a mobile device and determining whether the mining-restriction mark prevents mining of content. Mining includes non-transient storage of a copy or derivations of data in the frame buffer. The method may also include preventing the mining of data in the frame buffer when the mining-restriction mark prevents mining.

Abstract: Computer-implemented methods and systems of updating user identifiers in an image-sharing environment include features for facilitating blocking, permitting, sharing and/or modifying content such as images and videos. User identification vectors providing data representative of a user and information about one or more facial characteristics of the user are broadcasted by a modular computing device. Information about one or more additional characteristics of the user (e.g., body characteristics and/or contextual characteristics) as determined from images of the user obtained by one or more image capture devices are received. An updated user identification vector including the information about one or more additional characteristics of the user is stored at and subsequently broadcasted by the modular computing device.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, to generate a scannable document. In one aspect, a method includes receiving a scan request, wherein the scan request includes a plurality of text images; for each text image of the plurality of text images: rectifying the text image to generate a text image with parallel image lines, generating a plurality of word bounding boxes that enclose one or more connected components in the text image, wherein each word bounding box is associated with a respective word, and generating, for each respective word in the text image, a plurality of points that represent the respective word; combining the plurality of text images to form a single text document; and providing the combined image as a scannable document.

Abstract: Systems and methods prevent or restrict the mining of content on a mobile device. For example, a method may include identifying a mining-restriction mark in low order bits or high order bits in a frame buffer of a mobile device and determining whether the mining-restriction mark prevents mining of content. Mining includes non-transient storage of a copy or derivations of data in the frame buffer. The method may also include preventing the mining of data in the frame buffer when the mining-restriction mark prevents mining.

Abstract: The present disclosure provides an image capture, curation, and editing system that includes a resource-efficient mobile image capture device that continuously captures images. The mobile image capture device is operable to input an image into at least one neural network and to receive at least one descriptor of the desirability of a scene depicted by the image as an output of the at least one neural network. The mobile image capture device is operable to determine, based at least in part on the at least one descriptor of the desirability of the scene of the image, whether to store a second copy of such image and/or one or more contemporaneously captured images in a non-volatile memory of the mobile image capture device or to discard a first copy of such image from a temporary image buffer without storing the second copy of such image in the non-volatile memory.

Abstract: The present disclosure provides an image capture, curation, and editing system that includes a resource-efficient mobile image capture device that continuously captures images. In particular, the present disclosure provides low power frameworks for processing, compressing, and transmitting images at a mobile image capture device. One example low power framework includes a scene analyzer that analyzes a scene depicted by a first image and determines whether to store the first image in a non-volatile memory or to discard the first image from a temporary image buffer without storing the first image in the non-volatile memory.

Abstract: The present disclosure provides an image capture, curation, and editing system that includes a resource-efficient mobile image capture device that continuously captures images. The mobile image capture device is operable to input an image into at least one neural network and to receive at least one descriptor of the desirability of a scene depicted by the image as an output of the at least one neural network. The mobile image capture device is operable to determine, based at least in part on the at least one descriptor of the desirability of the scene of the image, whether to store a second copy of such image in a non-volatile memory of the mobile image capture device or to discard a first copy of such image from a temporary image buffer without storing the second copy of such image in the non-volatile memory.

Abstract: Systems and methods prevent or restrict the mining of content on a mobile device. For example, a method may include identifying a mining-restriction mark in low order bits or high order bits in a frame buffer of a mobile device and determining whether the mining-restriction mark prevents mining of content. Mining includes non-transient storage of a copy or derivations of data in the frame buffer. The method may also include preventing the mining of data in the frame buffer when the mining-restriction mark prevents mining.

Abstract: An integrated stacked and/or abutted sensor, memory and processing hardware camera solution is described. The sensor is to receive light from an image and generate electronic pixels from the light. The processing hardware is to process the electronic pixels to: a) recognize a scene from the image in a lower quality image mode; b) trigger actions by the camera solution in response to the recognition of the scene, the actions including: i) transitioning the camera solution from the lower quality image mode to a higher quality image mode to capture a higher quality version of the image; and, ii) forwarding from the camera solution important imagery and not forwarding from the camera solution unimportant imagery.

Abstract: Systems and methods prevent or restrict the mining of content on a mobile device. For example, a method may include identifying a mining-restriction mark in low order bits or high order bits in a frame buffer of a mobile device and determining whether the mining-restriction mark prevents mining of content. Mining includes non-transient storage of a copy or derivations of data in the frame buffer. The method may also include preventing the mining of data in the frame buffer when the mining-restriction mark prevents mining.

Abstract: An apparatus is described. The apparatus includes a smart image sensor having a memory and a processor that are locally integrated with an image sensor. The memory is to store first program code to be executed by the processor. The memory is coupled to the image sensor and the processor. The memory is to store second program code to be executed by the processor. The first program code is to cause the smart image sensor to perform an analysis on one or more images captured by the image sensor. The analysis identifies a region of interest within the one or more images with machine learning from previously captured images. The second program code is to cause the smart image sensor to change an image sensing and/or optical parameter in response to the analysis of the one or more images performed by the execution of the first program code. Alternatively or in combination, the memory is to store third program code to be executed by the processor and fourth program code to be executed by the processor.