A system for physiological motion measurement is provided. The system may acquire a reference image corresponding to a reference motion phase of an ROI and a target image of the ROI corresponding to a target motion phase, wherein the reference motion phase may be different from the target motion phase. The system may identify one or more feature points relating to the ROI from the reference image, and determine a motion field of the feature points from the reference motion phase to the target motion phase using a motion prediction model. An input of the motion prediction model may include at least the reference image and the target image. The system may further determine a physiological condition of the ROI based on the motion field.

Described herein are systems, methods, and non-transitory computer readable media for validating or rejecting automated detections of an entity being tracked within an environment in order to generate a track representative of a travel path of the entity within the environment. The automated detections of the entity may be generated by an artificial intelligence (AI) algorithm. The track may represent a travel path of the tracked entity across a set of image frames. The track may contain one or more tracklets, where each tracklet includes a set of validated detections of the entity across a subset of the set of image frames and excludes any rejected detections of the entity. Each tracklet may also contain one or more user-provided detections in scenarios in which the tracked entity is observed or otherwise known to be present in an image frame but automated detection of the entity did not occur.

Described herein are systems, methods, and non-transitory computer readable media for validating or rejecting automated detections of an entity being tracked within an environment in order to generate a track representative of a travel path of the entity within the environment. The automated detections of the entity may be generated by an artificial intelligence (AI) algorithm. The track may represent a travel path of the tracked entity across a set of image frames. The track may contain one or more tracklets, where each tracklet includes a set of validated detections of the entity across a subset of the set of image frames and excludes any rejected detections of the entity. Each tracklet may also contain one or more user-provided detections in scenarios in which the tracked entity is observed or otherwise known to be present in an image frame but automated detection of the entity did not occur.

An occlusion detection model training method is provided. The training method includes the following steps: constructing a plurality of pieces of training sample data, where the training sample data includes a first face image added with an occlusion object, coordinate values of a first key point in the first face image, and occlusion information of the first key point; and using the first face image as input data, and using the coordinate values of the first key point and the occlusion information of the first key point as output data, to train an occlusion detection model, so that the occlusion detection model outputs, based on any input second face image, coordinate values of a second key point included in the second face image and an occlusion probability of the second key point.

An occlusion detection model training method is provided. The training method includes the following steps: constructing a plurality of pieces of training sample data, where the training sample data includes a first face image added with an occlusion object, coordinate values of a first key point in the first face image, and occlusion information of the first key point; and using the first face image as input data, and using the coordinate values of the first key point and the occlusion information of the first key point as output data, to train an occlusion detection model, so that the occlusion detection model outputs, based on any input second face image, coordinate values of a second key point included in the second face image and an occlusion probability of the second key point.

A computer-implemented method for annotating driving scenario sensor data, including the steps of receiving raw sensor data, the raw sensor data comprising a plurality of successive LIDAR point clouds and/or a plurality of successive camera images, recognizing objects in each image of the camera data and/or each point cloud using one or more neural networks, correlating objects within successive images and/or point clouds, removing false positive results on the basis of plausibility criteria, and exporting the annotated sensor data of the driving scenario.

A method for binocular ranging for children is provided in this disclosure, which includes following steps: acquiring an image of a target area and world coordinates of a human body in left and right eyes; correcting the image to obtain a corrected image of the target area; performing face detection on the corrected image of the target area to obtain a face image and an eye image; recognizing the face image and determining whether the face image is a child's face; if yes, performing image correction based on the obtained face image; inputting the eye image into a preset eye recognition model for recognition whether the human eye is staring at a screen; and performing ranging on the eye image based on the world coordinates of the human body to obtain a distance between the screen and the human eye, if the human eye is staring at the screen.

A method for binocular ranging for children is provided in this disclosure, which includes following steps: acquiring an image of a target area and world coordinates of a human body in left and right eyes; correcting the image to obtain a corrected image of the target area; performing face detection on the corrected image of the target area to obtain a face image and an eye image; recognizing the face image and determining whether the face image is a child's face; if yes, performing image correction based on the obtained face image; inputting the eye image into a preset eye recognition model for recognition whether the human eye is staring at a screen; and performing ranging on the eye image based on the world coordinates of the human body to obtain a distance between the screen and the human eye, if the human eye is staring at the screen.

Systems and methods are provided for classifying microbial cells according to morphological features of microcolonies. A dark-field objective is employed to acquire a dark-field image of a microcolony during a microcolony growth phase that is characterized by phenotypic expression of microcolony morphological features which evolve with time and are differentiated among classes of microbial cell types. The dark-field image is processed to classify the microcolony according to two or more microbial cell types, such as Gram status and/or speciation. The dark-field objective may have a numerical aperture selected to facilitate the imaging of microcolony morphological features, residing, for example, between 0.15 and 0.35. A set of dark-field images of a microcolony may be collected during the microcolony growth phase and processed to classify the microcolony. Classification may be performed according to a temporal ordering of the dark-field images, for example, using a recurrent neural network.

Systems and methods are provided for classifying microbial cells according to morphological features of microcolonies. A dark-field objective is employed to acquire a dark-field image of a microcolony during a microcolony growth phase that is characterized by phenotypic expression of microcolony morphological features which evolve with time and are differentiated among classes of microbial cell types. The dark-field image is processed to classify the microcolony according to two or more microbial cell types, such as Gram status and/or speciation. The dark-field objective may have a numerical aperture selected to facilitate the imaging of microcolony morphological features, residing, for example, between 0.15 and 0.35. A set of dark-field images of a microcolony may be collected during the microcolony growth phase and processed to classify the microcolony. Classification may be performed according to a temporal ordering of the dark-field images, for example, using a recurrent neural network.