An image processing method including: capturing changes in a monitored scene; and performing a sparse feature calculation on the changes in the monitored scene to obtain a sparse feature map.

Systems, and method and computer readable media that store instructions for calculating signatures, utilizing signatures and the like.

Systems, and method and computer readable media that store instructions for configuring spanning elements of a signature generator.

Systems, and method and computer readable media that store instructions for calculating signatures, utilizing signatures and the like.

An image recognition method includes: determining a first feature map of the current frame image by using a convolutional neural network based on a type of a current frame image; determining a second feature map of a key frame image before the current frame image; performing feature alignment on the first feature map and the second feature map to obtain a first aligned feature map; fusing the first feature map and the first aligned feature map to obtain a first fused feature map; and recognizing content in the current frame image based on the first fused feature map.

An apparatus and method of denoising a dynamic image is provided. The dynamic image can represent a time-series of snapshot images. The dynamic image is transformed, using a sparsifying transformation, into an aggregate image and a series of transform-domain images. The transform-domain images represent kinetic information of the dynamic images (i.e., differences between the snapshots), and the aggregate image represents static information (i.e., features and structure common among the snapshots). The transform-domain images, which can be approximated using a sparse approximation method, are denoised. The denoised transform-domain images are recombined with the aggregate image using an inverse sparsifying transformation to generate a denoised dynamic image. The transform-domain images can be denoised using at least one of a principal component analysis method and a K-SVD method.

A method and system for gathering vehicle video data, processing the vehicle video data, and providing the processed data to a cloud layer that reconstructs the scene encountered by the vehicle. By reconstructing the encountered scene at the cloud layer, a variety of commands can be generated for that vehicle or other vehicles in the vicinity, where the commands address the conditions being experienced by the vehicles. This may be particularly useful for autonomous or semi-autonomous vehicles. If the reconstructed scene is not sufficiently accurate or detailed, one or more data extraction parameter(s) can be adjusted so that additional data is provided to the cloud layer; if the reconstructed scene is sufficiently accurate, then the data extraction parameter(s) can be adjusted so that less data is provided to the cloud layer, thus, reducing unnecessary cellular data charges.

Image data is run through a neural network, and the neural network produces a vector representation of the image data. Random sparse sampling masks are created. The vector representation of the image data is masked with each of the random sparse sampling masks, the masking generating corresponding sparsely sampled vectors. The sparsely sampled vectors are transmitted to nodes of a consensus network, wherein a sparsely sampled vector of the sparsely sampled vectors is transmitted to a node of the consensus network. Votes from the nodes of the consensus network are received. Whether a consensus is achieved in the votes is determined. Responsive to determining that the consensus is achieved, at least one of identification and verification of the image data may be provided.

Image data is run through a neural network, and the neural network produces a vector representation of the image data. Random sparse sampling masks are created. The vector representation of the image data is masked with each of the random sparse sampling masks, the masking generating corresponding sparsely sampled vectors. The sparsely sampled vectors are transmitted to nodes of a consensus network, wherein a sparsely sampled vector of the sparsely sampled vectors is transmitted to a node of the consensus network. Votes from the nodes of the consensus network are received. Whether a consensus is achieved in the votes is determined. Responsive to determining that the consensus is achieved, at least one of identification and verification of the image data may be provided.

A method and system for gathering vehicle video data, processing the vehicle video data, and providing the processed data to a cloud layer that reconstructs the scene encountered by the vehicle. By reconstructing the encountered scene at the cloud layer, a variety of commands can be generated for that vehicle or other vehicles in the vicinity, where the commands address the conditions being experienced by the vehicles. This may be particularly useful for autonomous or semi-autonomous vehicles. If the reconstructed scene is not sufficiently accurate or detailed, one or more data extraction parameter(s) can be adjusted so that additional data is provided to the cloud layer; if the reconstructed scene is sufficiently accurate, then the data extraction parameter(s) can be adjusted so that less data is provided to the cloud layer, thus, reducing unnecessary cellular data charges.