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.

Embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for detecting objects located in an area of interest. In accordance with one embodiment, a method is provided comprising: receiving, via an interface provided through a general instance on a cloud environment, imaging data comprising raw images collected on the area of interest; upon receiving the images: activating a central processing unit (CPU) focused instance on the cloud environment and processing, via the image, the raw images to generate an image map of the area of interest; and after generating the image map: activating a graphical processing unit (GPU) focused instance on the cloud environment and performing object detection, via the image, on a region within the image map by applying one or more object detection algorithms to the region to identify locations of the objects in the region.

Embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for detecting objects located in an area of interest. In accordance with one embodiment, a method is provided comprising: receiving, via an interface provided through a general instance on a cloud environment, imaging data comprising raw images collected on the area of interest; upon receiving the images: activating a central processing unit (CPU) focused instance on the cloud environment and processing, via the image, the raw images to generate an image map of the area of interest; and after generating the image map: activating a graphical processing unit (GPU) focused instance on the cloud environment and performing object detection, via the image, on a region within the image map by applying one or more object detection algorithms to the region to identify locations of the objects in the region.

A system and method of identifying objects is provided. In one aspect, the system and method includes a hand-held device with a display, camera and processor. As the camera captures images and displays them on the display, the processor compares the information retrieved in connection with one image with information retrieved in connection with subsequent images. The processor uses the result of such comparison to determine the object that is likely to be of greatest interest to the user. The display simultaneously displays the images the images as they are captured, the location of the object in an image, and information retrieved for the object.

A system and method of identifying objects is provided. In one aspect, the system and method includes a hand-held device with a display, camera and processor. As the camera captures images and displays them on the display, the processor compares the information retrieved in connection with one image with information retrieved in connection with subsequent images. The processor uses the result of such comparison to determine the object that is likely to be of greatest interest to the user. The display simultaneously displays the images the images as they are captured, the location of the object in an image, and information retrieved for the object.

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.

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.

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.

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.