Abstract: The present invention relates to a device, system and method for less obtrusively determining a tissue characteristic of a subject, the device comprises a first control unit (11) configured to control an electromechanical transducer (31) by a first control signal (21) to transfer mechanical waves varying in a frequency range or with varying frequency content to an exposed tissue area of the subject; a second control unit (12) configured to control an electromagnetic radiation emitter (32) by a second control signal (22) to emit electromagnetic radiation towards the exposed tissue area of the subject; a radiation signal input (13) configured to obtain a radiation signal (23) indicative of electromagnetic radiation reflected from the exposed tissue area of the subject; and a processor (14) configured to determine a tissue characteristic signal (24) indicative of a tissue characteristic of the exposed tissue area of the subject derived from a frequency response or a frequency transfer function obtained from the

Abstract: Various implementations disclosed herein include devices, systems, and methods for normal estimation using a directional measurement, such as a gravity vector. In various implementations, a device includes a non-transitory memory and one or more processors coupled with the non-transitory memory. In some implementations, a method includes identifying planar surfaces in an environment represented by an image. Each planar surface is associated with a respective orientation. A directional vector associated with the environment is determined. A subset of the planar surfaces that have a threshold orientation relative to the directional vector is identified. For each planar surface in the subset of the planar surfaces, a normal vector for the planar surface is determined based on the orientation of the planar surface and the directional vector.

Abstract: An ML root model format having a root model definition, is converted into an ML target model format not having the root model definition. A learning system is assigned to the root model definition that is convertible to the machine learning target model format. A new version of the root model definition is ported from the ML root model to the format of the ML target model to generate a new version of the machine learning target model after a learning iteration of the learning system. Quality limits are determined using an X control chart method based on a cross-validation of fold results generated during a validation of the new version of the machine learning root model. A quality metric value of the new version of the ML target model is evaluated against the quality threshold values of the new version of the ML root model.

Abstract: Systems and methods herein describe using a neural network to identify a first set of joint location coordinates and a second set of joint location coordinates and identifying a three-dimensional hand pose based on both the first and second sets of joint location coordinates.

Abstract: The present disclosure provides systems, methods, and computer program products for controlling an object. An example method can comprise (a) obtaining video data of the object and (b) performing motion analysis on the video data to generate modified video data. The method can further comprise (c) using artificial intelligence (AI) to identify a set of features in the modified video data. The set of features may be indicative of a predicted state of the object. The AI may be been trained offline on historical training data. The method can further comprise (d) using the predicted state to determine a control signal and (e) transmitting, in real-time, the control signal to the object to adjust or maintain a state of the object in relation to the predicted state. Operations (a) to (d) can be performed without contacting the object.

Abstract: Methods and systems implemented in a vehicle involve obtaining a single camera image from a camera arranged on the vehicle. The image indicates a heading angle ?0 between a vehicle heading x and a tangent line that is tangential to road curvature of a road on which the vehicle is traveling and also indicates a perpendicular distance y0 from a center of the vehicle to the tangent line. An exemplary method includes obtaining two or more inputs from two or more vehicle sensors, and estimating kinematic states of the vehicle based on applying a Kalman filter to the single camera image and the two or more inputs to solve kinematic equations. The kinematic states include roll angle and pitch angle of the vehicle.

Abstract: Present embodiments pertain to systems, apparatuses, and methods for analyzing and reporting the movements in mechanical structures, inanimate physical structures, machinery, and machine components, including automatically detecting aliased frequencies of a component on the structure which exhibits frequencies higher than the maximum frequency of the FFT spectrum calculated from the acquired data. To automatically detect the presence of aliased frequencies, a second virtually identical recording is acquired using a slightly different sampling rate and this provides the basis for detecting frequencies which are greater than the Nyquist sampling rate of the video recording and calculating the true frequency value of the aliased peaks in the frequency spectrum.

Abstract: An image processing method and apparatus, a device, and a storage medium are provided. The image processing method includes: obtaining key points of a reference region of an object in an image; determining the orientation of the reference region according to the key points of the reference region; and performing deformation processing on a region to be adjusted of the object based on the orientation of the reference region, where the region to be adjusted is the same as or different from the reference region.

Abstract: A dental device tracking method including acquiring, using an imager of a dental device, at least a first image which includes an image of at least one user body portion outside of a user's oral cavity; identifying the at least one user body portion in the first image; and determining, using the at least the first image, a position of the dental device with respect to the at least one user body portion.

Abstract: Embodiments of the present invention provide systems and methods to detect a moving anatomic feature during a treatment sequence based on a computed and/or a measured shortest distance between the anatomic feature and at least a portion of an imaging system.

Abstract: The present disclosure provides a method and apparatus for detecting a region of interest in a video, a device and a storage medium. The method may include: acquiring a current to-be-processed frame from a picture frame sequence of a video; detecting a region of interest (ROI) in the current to-be-processed frame, in response to determining that the current to-be-processed frame is a detection picture frame, to determine at least one ROI in the current to-be-processed frame; and updating a to-be-tracked ROI, based on the ROI in the current to-be-processed frame and a tracking result determined by a pre-order tracking picture frame; and tracking the current to-be-processed frame based on the existing to-be-tracked ROI, in response to determining that the current to-be-processed frame is a tracking picture frame, to determine at least one tracking result as the ROI of the current to-be-processed frame.

Abstract: An image analysis device 10 includes: a generation unit 11 which generates a similar set, which is a set of similar pieces of learning data selected from among a plurality of pieces of learning data, each including an image and information that represents an object to be recognized that is displayed in the image; and a learning unit 12 which uses the generated similar set to learn parameters for a predetermined recognition model that allow the predetermined recognition model to recognize the object to be recognized that is displayed in each image included in the generated similar set.

Abstract: The disclosure provides an objective identification device, comprising: a classifier training circuit configured to extract objective characteristics based on training samples and perform offline training based on the objective characteristics to obtain a classifier; and a calculation circuit is configured to identify an objective in an image based on a particle swarm optimization algorithm, wherein each of particles is defined as an object having a predefined size in the image; and a fitness value of each of particles is calculated based on the classifier and the objective characteristics of the particle in the particle swarm optimization algorithm, the fitness value representing a probability that the particle belongs to the objective. The disclosure also provides an objective identification method and driving assistance device. According to the disclosure, not only the identification rate can be increased but also application scenarios having different identification rate requirements can be satisfied.

Abstract: Disclosed herein are apparatuses and methods for auto-configuring a region of interest (ROI) associated with a camera. In one implementation, a method comprises receiving image frames from a camera installed in the environment, wherein the ROI is located within the view of the image frames. The method includes tracking a plurality of persons in the image frames and determining a respective trajectory of movement for each person of the plurality of persons. The method further includes comparing each of the respective trajectories to one another and identifying, based on the comparing, a common trajectory shared by more than one person of the plurality of persons, wherein the common trajectory is not fully encompassed in the ROI. The method additionally includes updating the ROI to encompass the common trajectory, and includes configuring the updated ROI to be associated with new image frames from the camera.

Abstract: In an approach to tracking mass-produced items via a digital twin, one or more computer processors receive one or more images of a product. One or more computer processors retrieve a digital twin template corresponding to the product. One or more computer processors detect an imperfection in the one or more images of the product. One or more computer processors map data associated with the imperfection to the digital twin template. One or more computer processors map data associated with the imperfection to the digital twin template. One or more computer processors assign a unique identifier to the digital twin template that includes the data associated with the imperfection. One or more computer processors store the data associated with the imperfection in association with the unique identifier.

Abstract: An estimation device includes a recognition unit configured to recognize a surrounding environment of a moving object in recognition regions, and an estimation unit configured to estimate a risk for the moving object on the basis of a recognition result from the recognition unit, in which the recognition unit sets a priority region on which a recognition process is preferentially performed among the recognition regions, according to a state of the surrounding environment of the moving object, and sets, as the priority region, a region overlapping a region including at least a part of at least one crosswalk that is present in a vicinity of an intersection region in which a first road on which the moving object is located intersects a second road present in an advancing direction of the moving object.

Abstract: A mine safety apparatus, and related method, that records movement data of a scene, visual images of the scene and correlates the movement data with the visual images. The apparatus comprises: a slope monitoring device that records location data in the scene and tracks the location data over time to identify movement and produce visual movement overlays; an image capture device that records a plurality of visual images of the scene from a location; and a processor that stitches the visual images into a panoramic image of the entire scene and for selected Points of Interest in the scene accurately determines a coordinate so that the visual movement overlays are correctly correlated with the Points of Interest.

Abstract: There is provided a method for detecting a vehicle including receiving continuously captured front images, setting a search area of the vehicle in a target image based on a location of the vehicle or a vehicle area detected from a previous image among the front images, detecting the vehicle in the search area according to a machine learning model, and tracking the vehicle in the target image by using feature points of the vehicle extracted from the previous image according to a vehicle detection result based on the machine learning model. Since the entire image is not used as a vehicle detection area, a processing speed may be increased, and a forward vehicle tracked in an augmented reality navigation may be continuously displayed without interruption, thereby providing a stable service to the user.

Abstract: A method of generating a correction line indicating a relationship between an amount of deviation of an edge of a wafer pattern from an edge of a reference pattern and a width of a space adjacent to the edge of the reference pattern, includes: creating an appearance-frequency graph of widths of spaces adjacent to reference patterns located within a designated area; obtaining images of wafer patterns corresponding to a plurality of space widths shown in the appearance-frequency graph; calculating amounts of deviation between edges of the wafer patterns on the images and edges of corresponding reference patterns; plotting a plurality of data points on a coordinate system, the plurality of data points being specified by the plurality of space widths and the amounts of deviation; and generating a correction line from the plurality of data points on the coordinate system.

Abstract: In an object detection device to be installed to a vehicle and detect an object outside the vehicle, a position calculator sets multiple candidate points representing a candidate position of the object, based on positions of feature points extracted from a first image captured at a first time. The multiple candidate points are set to be denser within a detection range set based on a distance to the object detected by the ultrasonic sensor than outside the detection range. The position calculator estimates positions of the multiple candidate points at a second time which is after the first time, based on the positions of the multiple candidate points and movement information of the vehicle, and calculates the position of the object by comparing the estimated positions of the multiple candidate points at the second time and the positions of the feature points extracted from a second image captured at the second time.