An image recognition accelerator, a terminal device, and an image recognition method are provided. The image recognition accelerator includes a dimensionality-reduction processing module, an NVM, and an image matching module. The dimensionality-reduction processing module first reduces a dimensionality of first image data. The NVM writes, into a first storage area of the NVM according to a specified first current I, ω low-order bits of each numeric value of the first image data on which dimensionality reduction has been performed, and writes, into a second storage area of the NVM according to a specified second current, (N−ω) high-order bits of each numeric value of the first image data on which dimensionality reduction has been performed. The image matching module determines whether an image library stored in the NVM includes image data matching the first image data on which dimensionality reduction has been performed.

Systems and methods are described for generating a plurality of three-dimensional (3D) proxy geometries of an object, generating, based on the plurality of 3D proxy geometries, a plurality of neural textures of the object, the neural textures defining a plurality of different shapes and appearances representing the object, providing the plurality of neural textures to a neural renderer, receiving, from the neural renderer and based on the plurality of neural textures, a color image and an alpha mask representing an opacity of at least a portion of the object, and generating a composite image based on the pose, the color image, and the alpha mask.

A user device provides a user interface for video manipulation with face replacement. A method of implementations includes accessing a video comprising a plurality of frames that comprise one or more faces, providing a plurality of stickers comprising alternate face graphics for the one or more faces, receiving, via a user interface of a user device, user selection of one of the stickers and a selected face of the one or more faces, accessing a plurality of face frame sequences of the video, wherein each face frame sequence is a sequence of frames of the video comprising the selected face of the one or more faces, and replacing the selected face with the selected sticker in a first face frame sequence of the plurality of face frame sequences and in a second face frame sequence of the plurality of face frame sequences.

Described herein are systems, methods, and non-transitory computer readable media for validating or rejecting automated detections of an entity being tracked within an environment in order to generate a track representative of a travel path of the entity within the environment. The automated detections of the entity may be generated by an artificial intelligence (AI) algorithm. The track may represent a travel path of the tracked entity across a set of image frames. The track may contain one or more tracklets, where each tracklet includes a set of validated detections of the entity across a subset of the set of image frames and excludes any rejected detections of the entity. Each tracklet may also contain one or more user-provided detections in scenarios in which the tracked entity is observed or otherwise known to be present in an image frame but automated detection of the entity did not occur.

A system, method and computer program product for use in managing collaboration on documents having digital ink on a network of computing devices is disclosed. Each computing device has a processor and at least one system application for processing handwriting input under control of the processor. The system application displays, on a display associated with one of the computing devices, a document having digital ink based on a journal of the document, defines the journal to have journal entries associated with at least handwriting input to the document represented by the digital ink, and communicates the journal entries of the journal with one or more of the other networked computing devices displaying the document. The handwriting input associated with the journal entries is handwriting input to the document via the input interface of any of the computing devices displaying the document based on the communicated journal entries.

Methods, apparatus, systems and articles of manufacture are disclosed for scalable architectures for reference signature matching and updating. An example method for scalable architectures for reference signature matching and updating includes accessing site signatures to be compared to reference signatures from a first group of media sources. Determining if a first reference node is an owner of a first one of the site signatures. Comparing a neighborhood of site signatures including the first site signature to reference signatures in a first subset of reference signatures when the first reference node is the owner of the first site signature, the first subset of references signatures stored in a first memory partition associated with the first reference node. Not comparing site signature to reference signatures when the first reference node is not the owner of the first one of the site signatures.

In one embodiment, an apparatus comprises a memory and a processor. The memory is to store visual data associated with a visual representation captured by one or more sensors. The processor is to: obtain the visual data associated with the visual representation captured by the one or more sensors, wherein the visual data comprises uncompressed visual data or compressed visual data; process the visual data using a convolutional neural network (CNN), wherein the CNN comprises a plurality of layers, wherein the plurality of layers comprises a plurality of filters, and wherein the plurality of filters comprises one or more pixel-domain filters to perform processing associated with uncompressed data and one or more compressed-domain filters to perform processing associated with compressed data; and classify the visual data based on an output of the CNN.

Fabrication processing is executed in a chip of an image sensor. An image capturing device includes an image capturing unit (11) mounted on a vehicle and configured to generate image data by performing image capturing of a peripheral region of the vehicle, a scene recognition unit (214) configured to recognize a scene of the peripheral region based on the image data, and a drive control unit (12) configured to control drive of the image capturing unit based on the scene recognized by the scene recognition unit.

A data processing device and a computer-implemented method are configured to execute in parallel a data hub process (6) comprising at least a segmentation sub-process (61) which segments input data into data segments and at least one keying sub-process (62) which provides keys to the data segments creating keyed data segments, wherein the data hub process (6) stores the keyed data segments in a shared memory device (4) as shared keyed data segments and a plurality of processes in the form of computation modules (7) wherein each computation module (7) is configured to access the at least one shared memory device (4) to look for modulo-specific data segments which are shared keyed data segments that are keyed with at least one key which is specific for at least one of the computation modules (7) and to execute a machine learning method on the module-specific data segments, said machine learning method comprising data interpretation and classification methods using at least one pre-trained neuronal network (71) and to output the result of the executed machine learning method to the shared memory device (4) or another computation module.

A vehicular vision system includes at least one color camera disposed at a vehicle and having an image sensor operable to capture image data. A first system on chip (SoC) includes an image signal processor that receives captured image data and converts the received image data to converted data that is in a format suitable for machine vision processing. A second system on chip (SoC) receives captured image data and communicates display data to a display disposed in the vehicle, with the display data being in a format suitable for display of video images at the display. At startup of the vehicle, video images derived from the display data are displayed by the display within a time period following startup of the vehicle and machine vision data processing of converted data does not commence until after the display time period has elapsed following startup of the vehicle.