Systems, methods, and computer-readable media for providing a localization processing service for enabling localization of a navigation network-restricted subsystem are provided.

A method for dynamically determining a separation distance in which thermal images of a space are received that indicate a count and location of users within the space, temperature of the users within the space are received along with acoustic data from the users within the space, which is filtered to include specific symptom-related sounds and discard other sounds. The one or more processors generate a probability of a contagious infection of users within the space at a location determined by the thermal images, based on correlating the temperature and the acoustic data associated with the users within the space. A separation distance from the users within the space is calculated, based on the locations and the probabilities of infection of the users within the space, and a notification corresponding to the calculated separation distance is delivered to a protected user.

A first parcel digital image associated with a first interaction point is received. The first parcel digital image may be associated with a first parcel being transported to or from the first interaction point. At least a second parcel digital image associated with at least a second interaction point is further be received. The second parcel digital image may be associated with the first parcel being transported to or from the second interaction point. A first parcel damage analysis is automatically generated based at least in part on analyzing the first parcel digital image and the at least second parcel image. The damage analysis can include determining whether the first parcel is damaged above or below a threshold.

The present disclosure provides a computer-implemented method for translating an image and a computer-implemented method for training an image translation model. In the computer-implemented method for translating an image, an image translation request carrying an original image is obtained. The original image is processed to generate a pre-translated image, a mask image and a deformation parameter. The original image is deformed based on the deformation parameter to obtain a deformed image. The deformed image, the pre-translated image and the mask image are merged to generate a target translated image.

In brain analysis, anatomical standardization is performed when analyzing a region of interest (ROI). There are individual differences in the shape and size of the brain and by converting the brain into a standard brain, these differences can be compared with each other and subjected to statistical analysis. When generating a standard brain analysis, a large number of pieces of image data are classified into a plurality of groups based on their anatomical features. An intermediate template that is an intermediate conversion image and a conversion map is calculated for each group, and the calculation of the intermediate template and the generation of the intermediate conversion image are repeated while gradually reducing the number of classifications, so that a final standard image is generated. Using the standard image and the intermediate template calculated during the generation of the standard image, spatial standardization of the measured image is performed.

Each of a plurality of co-located inspection camera modules captures raw images of objects passing in front of the co-located inspection camera modules which form part of a quality assurance inspection system. The inspection camera modules have either a different image sensor or lens focal properties and generate different feeds of raw images. The co-located inspection camera modules can reside within a single standalone module and be selectively switched amongst to activate the corresponding feed of raw images. The activated feed of raw images is provided to a consuming application or process for quality assurance analysis.

Provided herein are systems and methods for estimating contact pressure of a human lying on a surface including one or more imaging devices having imaging sensors oriented toward the surface, a processor and memory, including a trained model for estimating human contact pressure trained with a dataset including a plurality of human lying poses including images generated from at least one of a plurality of imaging modalities including at least one of a red-green-blue modality, a long wavelength infrared modality, a depth modality, or a pressure map modality, wherein the processor can receive one or more images from the imaging devices of the human lying on the surface and a source of one or more physical parameters of the human to determine a pressure map of the human based on the one or more images and the one or more physical parameters.

Embodiments of the disclosure provide methods and systems for representation learning from a biomedical image with a sparse convolution. The exemplary system may include a communication interface configured to receive the biomedical image acquired by an image acquisition device. The system may further include at least one processor, configured to extract a structure of interest from the biomedical image. The at least one processor is also configured to generate sparse data representing the structure of interest and input features corresponding to the sparse data. The at least one processor is further configured to apply a sparse-convolution-based model to the biomedical image, the sparse data, and the input features to generate a biomedical processing result for the biomedical image. The sparse-convolution-based model performs one or more neural network operations including the sparse convolution on the sparse data and the input features.

Data is received that includes a feed of images of a plurality of objects passing in front of an inspection camera module forming part of a quality assurance inspection system. Thereafter, it is detected whether there is an object within each image. Based on this detection, images in which each object is detected that meet predefined object representation parameters are identified (on an object-by-object basis, etc.). The identified images are provided to a consuming application or process for quality assurance analysis. Related apparatus, systems, techniques and articles are also described.

Hybrid deep learning systems and methods allow for detecting anomalies in objects, such as electrical printed circuit board (PCB) components, based on image data. In one or more embodiments, a hybrid deep learning model comprises a Graph Attention Network (GAT) that uses spatial properties of the PCB components to extract latent semantic information and generate an output set of hidden representations. The GAT treats each of the electrical components as a node and each connection between them as edges in a graph. The hybrid system further comprises a Convolutional Neural Network (CNN) that uses pixel data to obtain its own output set of hidden representations. The hybrid deep learning model concatenates both sets to detect anomalies that may be present on the PCB.