Abstract: This application discloses a video data processing method and apparatus, a computer device, and a storage medium, and belongs to the field of video processing technologies. According to this application, when a second video is embedded into a target sub-image of a first video, a position movement of the target sub-image in the first video is obtained based on an artificial intelligence technology. According to the position movement, motion blur processing is performed on the picture of the second video correspondingly, so that the second video, after being embedded into the first video, will have the same motion blur effect as the target sub-image in the first video. Therefore, the first video and the second video can be better fused, and a better video transition effect can be obtained.

Abstract: A mobile cleaning robot includes a cleaning head configured to clean a floor surface in an environment, and at least one camera having a field of view that extends above the floor surface. The at least one camera is configured to capture images that include portions of the environment above the floor surface. The robot includes a recognition module is configured to recognize objects in the environment based on the images captured by the at least one camera, in which the recognition module is trained at least in part using the images captured by the at least one camera. The robot includes a storage device is configured to store a map of the environment. The robot includes a control module configured to control the mobile cleaning robot to navigate in the environment using the map and operate the cleaning head to perform cleaning tasks taking into account of the objects recognized by the recognition module.

Abstract: An automatic vehicle window cleaning method and apparatus includes obtaining a vehicle window image; determining a dark channel image corresponding to a single frame of vehicle window image; further determining, based on a quantity of pixels whose gray values exceed a preset gray threshold in the dark channel image and/or definition of the dark channel image, that a first-type foreign object exists on a vehicle window, and/or determining, based on RGB values of pixels in i consecutive frames of vehicle window images, that a second-type foreign object exists on the vehicle window, and controlling a cleaning tool to remove a foreign object. The foreign object includes the first-type foreign object and/or the second-type foreign object.

Abstract: A system for intelligent image dehazing based on haze type classification. The system receives a hazy image and classifies the haze type using either a Single Selective Classifier (SSC) or a Hybrid Conditional Classifier (HCC), based on user preference. If SSC is selected, a specific single model is used for classification. If HCC is selected, all pre-trained and pre-defined models are used. The system then selects a suitable specialized dehazer based on the predicted haze type and applies it to the input image to mitigate the effects of the haze. The system is flexible, allowing for adjustments to various parameters as per the users' requirements and target application domains.

Abstract: A learning device performs learning a facial recognition model so as to further reduce a triplet loss that uses a first facial image, a second facial image that is a candidate for an adversarial example of a same person as the first facial image, and a third facial image that is a candidate for an adversarial example of a different person than the first facial image.

Abstract: A neural network is trained for use in a substrate residue classification system by obtaining ground truth residue level measurements of a top layer of a calibration substrate at a plurality of locations, each location at a defined position for a die being fabricated on the substrate. A plurality of color images of the calibration substrate are obtained, each color image corresponding to a region for a die being fabricated on the substrate. A neural network is trained to convert color images of die regions from an in-line substrate imager to residue level measurements for the top layer in the die region.

Abstract: The invention refers to an apparatus for monitoring a subject (121) during an imaging procedure, e.g. CT-imaging. The apparatus (110) comprises a monitoring image providing unit (111) providing a first monitoring image and a second monitoring image acquired at different support positions, a monitoring position providing unit (112) providing a first monitoring position of a region of interest in the first monitoring image, a support position providing unit (113) providing support position data of the support positions, a position map providing unit (114) providing a position map mapping calibration support positions to calibration monitoring positions, and a region of interest position determination unit (115) determining a position of the region of interest in the second monitoring image based on the first monitoring position, the support position data, and the position map. This allows to determine the position of the region of interest accurately and with low computational effort.

Abstract: A processor detects a structure of interest from a plurality of tomographic images indicating a plurality of tomographic planes of an object. The processor selects a tomographic image from the plurality of tomographic images according to a type of the structure of interest in a region in which the structure of interest has been detected and generates a composite two-dimensional image using the selected tomographic image in the region in which the structure of interest has been detected and using a predetermined tomographic image in a region in which the structure of interest has not been detected.

Abstract: A server is provided. The server provides virtual indoor space content corresponding to a real indoor space. The server includes a memory; a communication module; and at least one processor operatively connected to the memory and the communication module. The at least one processor may receive image data to generate the virtual indoor space content from a mobile terminal through the communication module, the image data including, for each of a plurality of indoor points, an omnidirectional image acquired from each indoor point and relative location information on the indoor point, and generate the virtual indoor space content by mapping a plurality of virtual points corresponding to the plurality of indoor points in a virtual indoor space based on the relative location information.

Abstract: A vehicle occupant detection system includes a processor that extracts, using an occupant detection area position information stored in a memory, an occupant detection area within a captured image of an in-vehicle camera, and detects any occupant in that area. The processor executes a correction mode using a correction-purpose image obtained by capturing, using the camera, a linearly-extending metal frame exposed when a vehicle door is open. In the correction mode, the processor detects an actual position of the metal frame in the correction-purpose image, calculates, as a shift amount, a difference between the actual metal frame position and an expected metal frame position in the correction-purpose image, and updates the occupant detection area position information in the memory such that an area reached by moving the occupant detection area of the current captured image by the shift amount serves as the occupant detection area of a new captured image.

Abstract: A method for determining an optimized weighting of an encoder and decoder network; the method comprising: for each of a plurality of test weightings, performing the following steps with the encoder and decoder operating using the test weighting: (a) encoding, using the encoder, a reference image and a distorted image into a latent space to form an encoding; (b) decoding the encoding, using the decoder, to form a distortion map indicative of a difference between the reference image and a distorted image; (c) spatially transforming the distorted image by the distortion map to obtain an aligned image; (d) comparing the aligned image to the reference image to obtain a similarity metric; and (e) determining a loss function which is at least partially defined by the similarity metric; wherein the optimized weighting is determined to be the test weighting which has an optimized loss function.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a model to detect objects. One of the methods includes maintaining first training data that represents at least a plurality of first images of invertebrate fossils; determining, using at least one or more geographic properties of a geographic region, one or more transformations from a group comprising: a pixel group translation, a pixel group two-dimensional rotation, a changed background, or an object obfuscation; creating, using the one or more transformations and the first training data, second training data that represents at least a plurality of second images of invertebrate fossils; training, using the second training data, a machine learning model to detect invertebrate fossils in images of the geographic region; and providing, to a system, the trained machine learning model to enable the system to detect invertebrate fossils in images of the geographic region.

Abstract: According to an embodiment of the present invention, a multi-resolution image processing device capable of recognition of a plurality of dynamic objects comprises a dynamic object identifier extracting a motion vector from an input image and identifying a dynamic object; a dynamic object ID imparter assigning an ID to the dynamic object identified by the dynamic object identifier; a dynamic object artificial intelligence determiner determining an authenticity of the dynamic object identified by the dynamic object identifier based on a standard shape for each type of dynamic object learned and stored, classifying at least one object of interest and object of no interest designated by a user, and removing the object of no interest; a region-of-interest detector extracting a feature point from the at least one object of interest and setting at least a portion of the object of interest as a region of interest; and a variable compressor compressing the image with different compression rates for the region of inter

Abstract: A system for detecting a defect of an electrode tab, is capable of easily detecting whether an electrode tab is folded during a manufacturing process of a secondary battery, and a method of detecting a defect of an electrode tab using the same is disclosed. The system includes a gripper configured to grasp a unit cell including an electrode and to transfer the unit cell to a magazine and a visual inspection system to detect the electrode tab.

Abstract: A sensor and method for sleep position detection including: a transmitter configured to transmit electromagnetic waves between 30 GHz and 300 GHz; a receiver configured to receive the electromagnetic waves from the transmitter, wherein the transmitter and receiver are positioned in relation to person sleeping such that the receiver receives reflected electromagnetic waves; and a control station configured to analyze the transmitted and received electromagnetic waves to determine a position of the person sleeping. In some cases, the method may include: forming a radar cube of results; performing a fast fourier transform (FFT) on the radar cube; applying a constant false alarm rate (CFAR) processor to the FFT data; determining a capon gradient; forming a 5-dimensional feature space based on the capon gradient; and conducting an optimization of SVM.

Abstract: A system and method for determining a mood for a crowd is disclosed. In example embodiments, a method includes identifying an event that includes two or more attendees, receiving at least one indicator representing emotions of attendees, determining a numerical value for each of the indicators, and aggregating the numerical values to determine an aggregate mood of the attendees of the event.

Abstract: A trajectory analysis device that displays relevant information on a trajectory of a moving object, comprising: a display interface to display the relevant information; a storage to store trajectory data indicating a plurality of trajectories and the relevant information; an input interface to receive input by a user operation; and a controller to control the display interface based on the relevant information and the user operation input on the input interface, wherein the controller causes the display interface to display one or more staying locations where trajectories stay, based on the trajectory data, receives an input result by a user operation selecting at least one of the staying locations, receives an input result by a user operation designating a designated location different from the selected staying location, and extracts the relevant information to be displayed on the display interface, based on the selected staying location and the designated location.

Abstract: A system and method for generating a training data set for training a machine learning model to detect defects in specimens is described herein. A computing system cause presentation of an image on a device of a user. The image includes at least one defect on an example specimen. The computing system receives an annotated image from the user. The user annotated the image using an input via the device. The input includes a first indication of a location of the defect and a second indication of a class corresponding to the defect. The computing system adjusts the annotated image to standardize the input based on an error profile of the user and the class corresponding to the defect. The computing system uploads the annotated image for training the machine learning model.

Abstract: The present invention is related to the field of quality control technology, specifically disclosing a classification method for product quality accidents and its system based on convolutional neural networks. The method consists of: collecting the sample data of product quality accidents, and grading the severity of accidents for each product with the quality accident; then extracting the image feature elements of various products with quality accidents and a valid feature element screening is performed; after that, a product quality accident classification model can be generating through training various valid feature elements and then the product quality accident is classified. The present invention can reduce the subjective errors due to human judgment, and thus the objectivity and consistency of the quality accident assessment results can be improved.

Abstract: Provided is a detection method for a spinning workshop, an electronic device and a storage medium; relating to the field of data processing. The method includes: performing image collection on a process control device of a spinning box in the spinning workshop to obtain an image to be processed; extracting a first image feature from the image to be processed; and processing the first image feature based on a decoder network to obtain a fault detection result. The decoder network comprises a plurality of decoder modules connected in series in sequence. Each decoder module includes a decoder layer and an adaptive classification head. The adaptive classification head is configured to perform classified prediction on an output feature of the decoder layer to obtain a first fault classification result. The fault detection result output by the decoder network includes a second fault classification result and a fault position.