A system and method for providing and managing a remote patient monitoring (RPM) system. The method is implemented by a central server, an RPM client, and a networked monitoring device. The RPM client is a software program that is executed by a computing device that is connected to the server via a network. The networked monitoring device is implemented as a locator or a smart mobile cart. More specifically, the RPM system can provide a tele-monitor with the ability to remotely monitor multiple patients, control remote cameras, and address abnormal patient situations. The RPM system can enhance tele-monitor effectiveness by detecting patient motion and tracking tele-monitor alertness.

Embodiments disclosed herein generally relate to identifying necrotic tissue in a multiplex immunofluorescence image of a slice of specimen. Particularly, aspects of the present disclosure are directed to accessing a multiplex immunofluorescence image of a slice of specimen comprising a first channel for a nuclei marker and a second channel for an epithelial tumor marker, wherein the slice of specimen comprises one or more necrotic tissue regions; providing the multiplex immunofluorescence image to a machine-learning model; receiving an output of the machine-learning model corresponding to a prediction that the multiplex immunofluorescence image includes one or more necrotic tissue regions at one or more particular portions of the multiplex immunofluorescence image; generating a mask for subsequent image processing of the multiplex immunofluorescence image based on the output of the machine-learning model; and outputting the mask for the subsequent image processing.

Methods, system, and media for comparing a set of images to determine the existence and location of any differences between the image set. The differences may be located using image comparison techniques such as SURF and Blob Detection, as well as through techniques used to identify areas of data sliding and match probabilities. A logical match probability, as well as a physical match probability, may be included in an output report with a result image highlighting the differences between the comparison images in the image set.

The present invention discloses a credible fruit traceability method and device based on fruit texture atlas and blockchain. According to the present invention, images of fruit pedicel part and fruit navel part of a single fruit are obtained, and the images are grayed and normalized to be converted into rectangular images; features are extracted from the rectangular images, respectively, and are encoded to obtain a fruit pedicle feature code table and a fruit navel feature code table; the two feature code tables are subjected to a merging operation to obtain a combined bidirectional feature code table, thus forming a unique fruit texture atlas. By processing the fruit texture atlas, the fruit texture atlas information and related information are stored on the blockchain. The user uses the same algorithm to obtain a fruit texture atlas of a fruit to be inspected through a smart terminal, the fruit texture atlas is processed and then is compared with the information on the blockchain for verification, so as to achieve the purpose of credible traceability. The present invention realizes the uniqueness and convenience of fruit identification, solves the problem of information tampering by evidence storage on the blockchain, and achieves the purpose of credible traceability.

A method is provided for improving image classification accuracy in few-shot learning scenarios, where only a limited number of training examples are available. The method combines the use of Gabor filters and convolutional neural networks (CNNs) to extract detailed texture and orientation features from images. These features are then enhanced through global average pooling, aggregated into comprehensive feature vectors, and refined using an attention mechanism that identifies and emphasizes the most relevant features for classification. Masks generated from this attention process selectively enhance critical features, which, after optional re-encoding, are used to train a classifier via a metric learning approach. This method aims to increase feature separability and classification performance, facilitating more accurate classification of new images with minimal training data.

Techniques related to poly-scale kernel-wise convolutional neural network layers are discussed. A poly-scale kernel-wise convolutional neural network layer is applied to an input volume to generate an output volume and include filters each having a number of filter kernels with the same sample rate and differing dilation rates optionally in a repeating pattern of dilation rate groups within each of filters with the pattern of dilation rate groups offset between the filters the poly-scale kernel-wise convolutional neural network layer.

Disclosed is a body-contact safety warning system based on a potential risk of automatic driving. The system includes a data receiving module, a data processing module, a safety warning module and a terminal interaction module; where the data receiving module is used for receiving data to obtain an image data set, a vehicle motion data set and communication transmission data; the data processing module is used for calculating to obtain a visual risk early warning degree, a predicted vehicle body stability degree and a communication data packet distortion degree; the safety warning module is used for performing classification judgment to obtain an early warning result and sending a vibration early warning; and the terminal interaction module is used for providing the user with a visual interaction page, receiving instructions and storing data.

An image processing apparatus comprises: a model obtaining unit configured to obtain a learned model that has learned, based on a position of a predetermined feature point, a contour of a target in an image obtained by capturing the target; an image obtaining unit configured to obtain an input image; a position obtaining unit configured to obtain a position of an input point input on the input image by a user; a normalization unit configured to obtain a normalized image generated by coordinate-transforming the input image such that the position of the input point matches the position of the predetermined feature point in the learned model; and an estimation unit configured to estimate the contour of the target in the input image using the normalized image and the learned model.

Methods, system, and media for comparing a set of images to determine the existence and location of any differences between the image set. The differences may be located using image comparison techniques such as SURF and Blob Detection, as well as through techniques used to identify areas of data sliding and match probabilities. A logical match probability, as well as a physical match probability, may be included in an output report with a result image highlighting the differences between the comparison images in the image set.

A method and apparatus for assisting a vehicle to stop at crosswalks and intersections by using a surround view monitor (SVM). In particular, the method includes: obtaining a front top-view image of the vehicle by using the SVM, performing a stop line detection in the front top-view image, and performing a crosswalk detection in the front top-view image in response to no detection of a stop line. The method further includes: generating a virtual stop line in response to a detection of a crosswalk, and controlling the vehicle to stop based on the detected stop line or the generated virtual stop line.