Abstract: An evaluation system that may include an imager; and a processing circuit. The imager may be configured to obtain an electron image of a hole that is formed by an etch process, the hole exposes at least one layer of a one or more sets of layers, each set of layers comprises layers that differ from each other by their electron yield and belong to an intermediate product. The processing circuit may be configured to evaluate, based on the electron image, whether the hole ended at a target layer of the intermediate product. The intermediate product is manufactured by one or more manufacturing stages of a manufacturing process of a three dimensional NAND memory unit. The hole may exhibit a high aspect ratio, and has a width of a nanometric scale.

Abstract: Systems and methods for analyzing image data to automatically ensure image capture consistency are described. According to certain aspects, a server may access aerial and other image data and identify a set of parameters associated with the capture of the image data. The server may access a corresponding set of acceptable image capture parameters, and may compare the set of parameters to the set of acceptable parameters to determine whether image data is consistent with the set of acceptable parameters. In some embodiments, if the image data is not consistent, the server may generate a notification or instruction to cause an image capture component to recapture additional image data.

Abstract: The present disclosure relates to a target object detection method, a readable storage medium and an electronic device. The method is periodically executed and the method includes: identifying a target object based on first N frames of image data obtained within a current execution period, wherein N is a positive integer, and N is less than the total number of frames of image data that can be obtained within the current execution period; and if the target object is identified, tracking the target object based on the obtained image data within the remaining time period of the current execution period. In the above technical solution, when the target object is identified, the identified target object is tracked within the remaining time period of the current execution period, so that the data calculation amount can be effectively reduced.

Abstract: A depth detection device includes a stereo image recording device, a scaler and a depth determiner is provided. The stereo image recording device is configured to record a stereo image of a scene, and includes a first optical path for recording a first image of the stereo image, and a second optical path for recording a second image of the stereo image. The first optical path and the second optical path have different lengths. The scaler determines a scaling of the first image or of the second image, by minimizing differing lateral magnification of objects in the scene, and for performing the scaling. The depth determiner determines a depth map of the scene based upon the scaled stereo image.

Abstract: An image-processing method for filtering light pollution appearing in a video image stream acquired by a video camera. The method includes, for a current image of the video image stream, the steps of subtracting the background represented in the current image in order to obtain the foreground of the current image, determining a brightening matrix, determining a compensating matrix by restricting the values of the pixels of the determined brightening matrix, segmenting the determined brightening matrix, determining a mask from the segmented brightening matrix, applying the mask to the determined compensating matrix in order to obtain a filtering matrix, and filtering the foreground of the current image by applying the filtering matrix in order to decrease the zones of light pollution in the images of the image stream.

Abstract: An object tracking method includes the following operations: detecting a first area of an object in a first video frame based on a deep learning model, in order to forecast a forecast area of the object in a forecast video frame according to the first video frame and the first area; detecting a second area of the object in a second video frame based on the deep learning model; and determining a correlation between the forecast area and the second area, in order to track the object.

Abstract: Systems and methods for analyzing visible chemical reactions are provided. In one implementation, the method may include receiving from an image sensor associated with a mobile communications device an image of a reagent pad in proximity to a colorized surface having at least one pair of colored reference elements. The method includes identifying in the image the reagent pad, a first colored reference element, and a second colored reference element. Thereafter, the method includes using the first colored reference element and the second colored reference element to determine an extent of a chemical reaction on the reagent pad irrespective of local illumination conditions. Then the method includes causing the mobile communications device to provide data based on the extent of the chemical reaction.

Abstract: A method for analyzing validity of responses to a questionnaire by students identifies each student in classroom images, and analyzes the defined classroom behaviors of each student over a period of time. The classroom behaviors can include learning behaviors and non-learning behaviors. The method determines whether the non-learning behaviors of each student meet (ie. are less than) or exceed (ie. are more than) one or more preset conditions and accordingly screens the questionnaire responses as being valid or not valid. An electronic device and a storage medium for performing the above-described method are also disclosed.

Abstract: An axle-load measuring apparatus measures an axle load of a vehicle by using a captured image where a road and the vehicle on the road are imaged, and the axle-load measuring apparatus includes a displacement calculator, a correction information obtaining unit, and an axle-load calculator. The displacement calculator detects a displacement of the road by using the captured image. The displacement is caused by receiving the axle load. The correction information obtaining unit obtains correction information. An axle-load calculator calculates the axle load by using the displacement and the correction information.

Abstract: A device receives user interface information associated with a user interface to be provided for a particular platform, and receives design information for a design of the user interface to be provided for the particular platform. The device receives a request to visually compare the user interface information and the design information, and utilizes, based on the request, a trained machine learning model to visually compare the user interface information and the design information. The device generates information, indicating defects in the user interface information, based on utilizing the trained machine learning model to visually compare the user interface information and the design information, where the defects include user interface information that does not visually match the design information. The device provides the information indicating the defects in the user interface information.

Abstract: Embodiments disclosed herein include, but are not limited to, methods for capturing video sampling data comprising a plurality of video images of a moving object, for example using one or more cameras positioned on a stationary frame of reference adjacent to the mechanical component under investigation, in which an external trigger is used for image acquisition (in some embodiments, one image per revolution or cycle) similar in result to an internal clock or timed pulse can act as a command over a camera to obtain visual data from which the movement of the component or object can be acquired and studied, and further producing video of motion from a series of images to detect, amplify, or enlarge an appearance of motion present as a result of components not coming back to their original locations.

Abstract: An image evaluation apparatus which is capable of evaluating an image that can be given positive evaluations in a networking service. Images included in post data posted on a networking service and evaluating values for the post data are obtained at predetermined time intervals. When the post data has been obtained, first parameters are generated by applying an image analysis process to the images. The first parameters and the evaluation values are stored in association with each other. Upon input of image to be evaluated, a second parameter is generated by applying the image analysis process to the image to be evaluated. A first parameter corresponding to the second parameter is extracted from the plurality of stored first parameters. Parameter evaluation values representing variations in the evaluation values associated with the extracted first parameter in notifying order are calculated. Notification of the calculated parameter evaluation values is provided.

Abstract: A wearable apparatus is provided for identifying a person in an environment of a user of the wearable apparatus based on non-facial information. The wearable apparatus includes a wearable image sensor configured to capture a plurality of images from the environment of the user, and a processing device programmed to analyze a first image of the plurality of images to determine that a face appears in the first image. The processing device also analyzes a second image of the plurality of images to identify an item of non-facial information appearing in the second image that was captured within a time period including a time when the first image is captured. The processing device also determines identification information of a person associated with the face based on the item of non-facial information.

Abstract: Methods, systems, and media for summarizing a video with video thumbnails are provided.

Abstract: Methods, systems, and computer readable media are provided for processing medical images. One or more prior medical images are aligned with a current medical image. Image subtraction between the current medical image and the one or more prior medical images is performed to produce one or more difference images. The one or more difference images are applied to a machine learning model to determine a presence or an absence of a medical condition.

Abstract: An electronic device includes a graphic processor and a memory device. The graphic processor includes an artificial neural network engine that makes an object recognition model learn by using learning data and weights to provide a learned object recognition model. The memory device, divides a feature vector into a first sub feature vector and a second feature vector, and performs a first calculation to apply the second sub feature vector and the weights to the learned object recognition model to provide a second object recognition result. The artificial neural network engine performs a second calculation to apply the first sub feature vector and the weights to the learned object recognition model to provide a first object recognition result and provides the first object recognition result to the memory device. The second calculation is performed in parallel with the first calculation.

Abstract: An electronic device estimates a pose of one or more subjects in an environment based on estimating a correspondence between a data volume containing a data mesh based on a current frame captured by a depth camera and a reference volume containing a plurality of fused prior data frames based on spectral embedding and performing bidirectional non-rigid matching between the reference volume and the current data frame to refine the correspondence so as to support location-based functionality. The electronic device predicts correspondences between the data volume and the reference volume based on spectral embedding. The correspondences provide constraints that accelerate the convergence between the data volume and the reference volume. By tracking changes between the current data mesh frame and the reference volume, the electronic device avoids tracking failures that can occur when relying solely on a previous data mesh frame.

Abstract: The image evaluation device includes a design data image generation unit that images design data; a machine learning unit that creates a model for generating a design data image from an inspection target image, using the design data image as a teacher and using the inspection target image corresponding to the design data image; a design data prediction image generation unit that predicts the design data image from the inspection target image, using the model created by the machine learning unit; a design data image generation unit that images the design data corresponding to the inspection target image; and a comparison unit that compares a design data prediction image generated by the design data prediction image generation unit and the design data image. As a result, it is possible to detect a systematic defect without using a defect image and generating misinformation frequently.

Abstract: An industrial vehicle is provided comprising a drive mechanism, a steering mechanism, a vehicle controller, a camera, and a navigation module. The camera is communicatively coupled to the navigation module, the vehicle controller is responsive to commands from the navigation module, and the drive mechanism and the steering mechanism are responsive to commands from the vehicle controller. The camera is configured to capture an input image of a warehouse ceiling comprising elongated skylights characterized by different rates of image intensity change along longitudinal and transverse axial directions, and ceiling lights characterized by a circularly symmetric rate of image intensity change. The navigation module is configured to distinguish between the ceiling lights and the skylights and send commands to the vehicle controller for localization, or to navigate the industrial vehicle through the warehouse based upon valid ceiling light identification, valid skylight identification, or both.

Abstract: A specimen of an artificial marker is arranged on an object, the position of which is to be determined. The artificial marker defines a nominal marker pattern with nominal characteristics. The specimen embodies the nominal marker pattern with individual characteristics. One or more images of the specimen are captured while the specimen is arranged on the object. An image representation of the specimen is analyzed using a data set. The data set comprises measured data values representing the individual characteristics as individually measured on the first specimen. Position values representing a 3D position of the specimen relative to a coordinate system are determined. A 3D position of the object is determined based on the position values of the specimen.