A system includes a processor circuit configured for communication with an external imaging device. The processor circuit receives, from the external imaging device, an image comprising a blood vessel within a patient. The processor circuit determines, using the image, a first location of the blood vessel with a restriction in blood flow caused by compression of the blood vessel by an anatomical structure within the patient and different than the blood vessel. The processor circuit generates a first graphical representation associated with the restriction. The processor circuit outputs, to a display in communication with the processor circuit, a screen display. The screen display includes the image and the first graphical representation at the first location of the blood vessel in the image.

A remote sensing image geometric normalization method and apparatus. The method comprises: constructing a pyramid tile structure for a reference image, and releasing reference tile data, wherein the reference tile data is data in the pyramid tile structure (S11); according to the resolution and geographic coordinates of an image to be subjected to geometric normalization, calculating the level of a tile to be downloaded and the name of the tile to be downloaded, and according to the level of the tile to be downloaded and the name of the tile to be downloaded, downloading corresponding data from the reference tile data to obtain a standard tile set (S12); performing first geometric correction on the image to be subjected to geometric normalization and tiles in the standard tile set to obtain a first image processing result (S13); matching the first image processing result with the tiles in the standard tile set to obtain a plurality of control points, and using the plurality of control points to calculate a result evaluation precision (S14); and according to the result evaluation precision, determining whether to perform second geometric correction on the first image processing result (S15). The method improves the efficiency of processing a remote sensing image.

Systems and methods for measuring vitals in accordance with embodiments of the invention are illustrated. One embodiment includes a method for measuring vital signs. The method includes steps for identifying regions of interest (ROIs) from video data of an individual, generating temporal waveforms from the ROIs, analyzing the generated temporal waveforms to extract vital sign measurements, and generating outputs based on the analyzed temporal waveforms.

Systems and methods are provided for medical image classification of images from varying sources. A set of microscopic medical images are acquired, and a first neural network module configured to reduce each of the set of microscopic medical images to a feature representation is generated. The first neural network module, a second neural network module, and a third neural network module are trained on at least a subset of the set of microscopic medical images. The second neural network module is trained to receive feature representation associated with an image of the microscopic images and classify the image into one of a first plurality of output classes. The third neural network module is trained to receive the feature representation, classify the image into one of a second plurality of output classes based on the feature representation, and provide feedback to the first neural network module.

Disclosed are an image display device and a method of controlling an image display device. The image display device includes a motion detection unit for detecting a motion of a user and provides a welcome feedback to the user in the form of a visual signal, an audio signal, or both according to whether the motion of the user is detected or not, thereby drawing attention of the user or switching a display state to reduce power consumption of the image display device. The image display device determines a motion exception event that is not regarded a user motion on the basis of an overall change or a local change in pixel value between image frames, thereby preventing the display panel from being activated by a non-human-related factor such as a sudden change in illuminance or a movement of a periodically operating object.

An image processing method and apparatus that includes: performing feature extraction processing on N image regions of a to-be-processed image respectively to obtain feature data corresponding to the N image regions respectively, N being an integer greater than or equal to 1, performing defect detection on the N image regions respectively according to the feature data corresponding to the N image regions respectively to obtain a prediction probability of a defect in each of the N image regions, obtaining attention for each image region, adjusting the prediction probability of the defect in each image region according to the attention for each image region, and generating a prediction result of the to-be-processed image according to the adjusted prediction probability of each image region.

A lane extraction method uses projection transformation of a 3D point cloud map, by which the amount of operations required to extract the coordinates of a lane is reduced by performing deep learning and lane extraction in a two-dimensional (2D) domain, and therefore, lane information is obtained in real time. In addition, black-and-white brightness, which is most important information for lane extraction on an image, is substituted by the reflection intensity of a light detection and ranging (LiDAR) sensor so that a deep learning model capable of accurately extracting a lane is provided. Therefore, reliability and competitiveness is enhanced in the field of autonomous driving, the field of road recognition, the field of lane recognition, and the field of HD road maps for autonomous driving, and the fields similar or related thereto, and more particularly, in the fields of road recognition and autonomous driving using LiDAR.

One or more images of a portion of a wafer with fabricated devices are acquired using an imaging tool. A pattern of repeating features in an input image of a wafer is identified using various methods, such as correlation and clustering of neighboring vectors. A template is generated based on the found pattern of repeating features. The template is aligned with the acquired image to identify target locations. The target locations are then isolated from the original image for performing detailed metrology.

To improve calculation efficiency by properly reducing the number of regression regions based on a size of an object to be detected, a processing apparatus has at least one processor or circuit configured to function as: a size obtain unit configured to obtain a size of a predetermined object in the image; a set unit configured to divide the image into a plurality of regions based on the size of the predetermined object obtained by the size obtain unit, and to set regression regions for estimating a number of the predetermined object, wherein the set unit is configured to inhibit to set the regression regions smaller than a predetermined minimum size corresponding to the predetermined object, and an estimate unit configured to estimate the number of the predetermined object by performing a regression process on the regression regions set by the set unit.

An analyzing apparatus according to an embodiment includes processing circuitry. The processing circuitry is configured to calculate a tissue characteristic parameter value with respect to each of a plurality of positions within a region of interest, by analyzing a result of a scan performed on a patient. The processing circuitry is configured to determine a measurement region in the region of interest by performing an analysis while using the tissue characteristic parameter values. The processing circuitry is configured to calculate a statistic value of the tissue characteristic parameter values in the measurement region.