28 results about "Body images" patented technology

The application relates to a kind of body image matching network assisted internal three-dimensional deformation field measurement method, belong to deformation measurement field.The application implementation method is: in reference body image, define calculation region and calculation voxel point;Using three-dimensional body image feature extraction network, key three-dimensional features are extracted simultaneously.Using three-dimensional feature matching network, key three-dimensional features are matched.From matched three-dimensional feature point pair, the feature point pair in the preset three-dimensional neighborhood range around each calculation voxel point is extracted, and the three-dimensional deformation initial value of the calculation voxel point is calculated when more than four groups of feature point pairs are judged;Less than 4 groups are directly assigned using the deformation initial value of other successfully calculated points with the spatial position of the calculation voxel point nearest to the interpolation or, as the three-dimensional deformation initial value of the point.Using three-dimensional reverse combination Gauss-Newton nonlinear iterative optimization method, high-precision internal three-dimensional deformation field measurement value is calculated, that is, internal three-dimensional deformation field measurement is realized.

The application discloses a kind of based on distributed vision's coating block piece closed loop inspection control system and method, it is related to automobile intelligent manufacturing and automatic quality detection technical field.The method includes the following steps: detecting vehicle information, and according to vehicle information and vehicle real-time position acquisition multi-angle vehicle body image, specifically, in the process of uniform speed of vehicle to be detected through detection area, according to the change situation of real-time position trigger distributed image acquisition;Multi-angle vehicle body image is analyzed using visual inspection model to block piece misloading, missing loading;According to the analysis result, man-machine collaborative closed loop inspection is carried out, wherein the vehicle that is unqualified to analysis result is manually reviewed, and manual review result is added to the training sample of visual inspection model to carry out model iteration optimization.The application uses distributed imaging to automatically detect misloading and missing loading of rough sealing block piece on automobile coating production line, and is deeply integrated with production execution system to realize closed loop quality control.

The present invention relates to a portable system and method for providing holistic wellbeing assessment to be performed. The system comprises a modular air-tight chamber built upon a plurality of vehicular chassis, watercraft and inflatable spaces with its internal air separated from external environmental air. An acoustic tactile and infra-red radiating floor and wall surface module (200). A mirror is embedded leaderboard and video consultation instructional display module (300). A health and nutrition assessment module (400); singular or plurality of retractable treatment and therapy couch module(s) (450) is contained inside a high air pressure and oxygen rich airtight chamber (800,). A health sensor data aggregator (740) and artificial intelligence (AI) based main controller (705) is connected cloud (790) to display the content. A health assessment module is integrated with plurality of interactive sensory and 3D stereoscopic vision camera arrays (420) and breathe analysis module (415). The health assessment module is integrated with facial scanning and 3D body imaging camera array module (8A) and the health assessment module with integrated breath analyser (415).

The application discloses a kind of extrusion filling sand-prevention filling form monitoring test device and test method, device includes simulation formation sand box, the simulation formation sand box is connected with sand liquid mixing system, waste liquid recovery system, sand liquid mixing system, simulation formation sand box and data acquisition processing system are electrically connected, the simulation formation sand box includes heating jacket, the data acquisition processing system includes three-dimensional imaging system;The heating jacket is used to make simulation formation sand box in filling sand body consolidation, the three-dimensional imaging system is used to scan filling sand body, generates filling sand body three-dimensional image data.Utilize the device and method provided by the present application patent, can obtain intuitive three-dimensional filling sand body image, in combination reservoir physical property, can establish the influence law of extrusion construction parameter and reservoir condition to filling form, to evaluate extrusion filling sand-prevention effect further.

The application provides a bag mouth recognition method during beverage bag filling, comprising: obtaining a bag body image of a beverage bag, and recognizing a bag body region and a suction nozzle candidate region on the bag body; obtaining three-dimensional structure information of the suction nozzle candidate region, and recognizing a real position of the suction nozzle to obtain initial positioning coordinates of the suction nozzle; extracting shoulder feature points of the bag body region, constructing a dynamic constraint model based on a spatial topological relationship between the shoulder feature points and the initial positioning coordinates of the suction nozzle, and verifying and correcting the initial positioning coordinates of the suction nozzle by using the dynamic constraint model to obtain corrected positioning coordinates of the suction nozzle; collecting corrected positioning coordinates of the same suction nozzle in a continuous predetermined number of frames of images to form a motion trajectory of the suction nozzle, and performing position prediction on the motion trajectory of the suction nozzle based on a time sequence filtering algorithm to output final positioning coordinates of the suction nozzle. The recognition method provided by the application realizes stable and accurate positioning of a flexible bag suction nozzle by fusing image analysis, three-dimensional structure recognition and shoulder topological constraints.

The application discloses an intelligent fish protection and fish returning method and system based on deep learning, which obtains fish body images and total weight information, adopts an object recognition model to analyze fish body contour feature vectors in real time, and obtains detection quantity and species results by fusing total weight data; then, a fusion model is used to process multi-modal fusion of contour size and total weight data to determine single fish weight distribution, and if the deviation exceeds a threshold value, the feature weight is iteratively adjusted to obtain an accurate single fish weight value; subsequently, data transmission generates a visual interface, a fish returning instruction sequence is triggered according to user selection, a local processing unit executes an opening operation and records the number, weight and timestamp of the fish returned, and an encrypted log file is formed; finally, log analysis judges environmental compliance parameters and updates behavior records. The application realizes accurate and automatic management of fish catching, improves the efficiency of fishing activities and environmental sustainability, and ensures the integrity of compliance behavior feedback.

This invention relates to a visual calibration method, a paint defect detection method, a medium, and equipment. It involves acquiring several sets of vehicle body images and calibration images from different vehicle body positions; determining initial extrinsic parameters between each acquisition device based on the calibration images; identifying a master acquisition device and slave acquisition devices based on vehicle body feature points in the vehicle body images; determining the master precise extrinsic parameters between the master acquisition device coordinate system and the vehicle body coordinate system based on the 3D and 2D coordinates of the feature points; constructing an extrinsic parameter objective optimization function with the objective of minimizing the sum of squared pose deviations between the initial extrinsic parameters and the global precise extrinsic parameters, and solving for the global precise extrinsic parameters between each acquisition device; and obtaining the precise extrinsic parameters between the coordinate systems of each acquisition device and the vehicle body coordinate system based on the master precise extrinsic parameters and the global precise extrinsic parameters. This invention solves the problems of low efficiency, poor accuracy, insufficient compatibility, and limited precision in existing technologies for detecting automotive paint defects.

The present disclosure provides a pose estimation method, which relates to the field of artificial intelligence. The method comprises: collecting N body images of a user at the same time from N perspectives, the N perspectives comprising an upper perspective, the collection position of the upper perspective being higher than the height of the user, and N being greater than or equal to 3; inputting the N body images into N two-dimensional pose estimation models respectively to obtain N two-dimensional pose data, wherein the N two-dimensional pose estimation models have a one-to-one mapping relationship with the N perspectives; and inputting the N two-dimensional pose data into a three-dimensional pose estimation model to obtain three-dimensional pose data of the user. The present disclosure also provides a pose estimation device, equipment, storage medium and program product.

Disclosed herein is a method and system for predicting a crack in a glazing while mounting the glazing on a vehicle is disclosed The method comprises receiving a vehicle body image and a glazing image from a portable device. Further, the method comprises obtaining vehicle measurement data and glazing measurement data from the received vehicle body image and the glazing image, respectively. Furthermore, the method comprises comparing the vehicle measurement data and the glazing measurement data with a corresponding predefined vehicle threshold and a predefined glazing threshold, respectively. Further, the method comprises determining stress in the glazing based on the comparing when the vehicle measurement data and the glazing measurement data are within the corresponding predefined vehicle threshold and predefined glazing threshold, respectively. Therefore, the method predicting the crack in the glazing, based on the determined stress in the glazing.

A medical device system is provided for guiding implantation of a medical electrical lead. The system includes processing circuitry configured to receive first patient body image data and second patient body image data and register the first patient body image data and the second patient body image data. The processing circuitry may be further configured to receive a signal from at least one location sensor disposed at a known distance from a feature of a removable outer sheath of a delivery tool configured to deliver an extravascular lead to an implant site of a patient via a non-transvenous pathway. The processing circuitry may determine from the location sensor signal a location of a feature of the removable outer sheath in a coordinate system space corresponding to the second patient body image data.

The present application relates to an optical imaging device and method. According to an embodiment, the optical imaging device can include: a treatment head, an accessory cavity formed inside; at least two imaging units arranged in the accessory cavity, used for collecting target body images; an image data analysis unit connected with the at least two imaging units, used for analyzing the target body images; wherein the optical lens of the at least two imaging units is directed to the central axis of the treatment head, and the central axis of the optical lens converges to the central axis of the treatment head. The optical imaging device and method can be applied to a radiotherapy device, through accurate positioning of the irradiated area and establishing the spatial position relationship of the irradiated area relative to the radiotherapy device, so as to realize accurate dose delivery.

The application discloses a fish body detection method and system based on a decoupling cross-domain fish detection model, relates to the technical field of fish body detection, and comprises the following steps: based on a multiscale feature enhancement detector in the decoupling cross-domain fish detection model, image with source domain region suggestion or image with target domain region suggestion in multiscale fish body data is extracted; the extracted image is input into a category adaptive module, the foreground region prediction suggestion of the image with the target domain region suggestion in the fish body image and the category pseudo label are determined through category adaptive adversarial learning; the foreground region prediction suggestion is input into a regression adaptive module, and the bounding box regression pseudo label of the image with the target domain region suggestion in the fish body image is determined through regression adaptive adversarial learning. The application combines the field adaptive technology and the target detection in aquaculture, so that the training of the detection model does not depend on a large amount of data labeling, the workload of data labeling is reduced, and the fish body detection efficiency is improved.

Disclosed is a video door bell, aiming at providing a video door bell which is capable of shooting full-body images, convenient to install and use, and high in security. The video door bell includes a door bell body, a front end face of which has a first inclined surface, wherein a mounting frame is disposed inside the door bell body and has a second inclined surface, an infrared sensor and a lens are disposed on the second inclined surface, the lens is disposed on the first inclined surface, and the first inclined surface, the second inclined surface, the lens, and the infrared sensor are all inclined downward. The present disclosure is applied in the technical field of door bells.

This invention relates to an online defect detection system and method for refractory material sintering. It obtains a standard sequence of body temperature and an ambient temperature standard sequence based on the heating curve. The time-series thermal imaging image of the refractory material sintering process is segmented into a body image based on the refractory material stacking area and an ambient image based on the surrounding environment. An ambient temperature sequence is extracted from the ambient image. Based on the ambient temperature sequence and the ambient temperature standard sequence, the range of suspected defects is determined based on the obtained defect features. The range of suspected defects is extracted from the body image, and a temperature feature sequence of the suspected defect range is obtained. The degree of deviation between the temperature feature sequence and the temperature standard sequence is obtained. Based on the degree of deviation, it is determined whether a defect exists in the suspected defect range. Furthermore, time-series synchronization analysis is used to distinguish between physical defects and thermal interference. This invention improves detection efficiency and reliability through environment-oriented and localized focusing strategies, making it suitable for online detection needs.

Even when remosaic processing is performed outside of a solid-state imaging device, a high-quality captured image can be produced. An electronic device comprises a solid-state imaging element and a signal processing device that performs remosaic processing on an output signal of the solid-state imaging element. The solid-state imaging element comprises a plurality of pixels arranged in a two-dimensional direction, a plurality of on-chip lenses, each provided for a corresponding group of pixels comprising two or more pixels, and a phase-difference-sensitivity-difference characteristic removal section that removes a radio-frequency component caused by a phase and sensitivity difference between two or more pixel signals of the same color output for each of the pixel groups.

The purpose of this application is to provide a method, device, medium, and program product for waking up a robot. The method includes: obtaining the upper body bounding box coordinates of at least one human body in each video frame of a video stream obtained by the robot; performing an affine transformation on each video frame based on the upper body bounding box coordinates to obtain an upper body image frame of each video frame with respect to the at least one human body; performing dynamic gesture recognition on the upper body image frame to obtain a corresponding dynamic gesture recognition result; if the accumulated number of upper body image frames reaches a first preset frame number, and the dynamic gesture recognition result corresponding to the current upper body image frame is wake-up, performing static gesture detection on the currently accumulated multiple upper body image frames; if the number of upper body image frames in the multiple upper body image frames that have detected a preset gesture meets a preset condition, performing a wake-up operation on the robot.

The application provides a computer vision-based intelligent clothing personalized customization recommendation method and system, relates to the technical field of computer vision, and comprises the following steps: acquiring a user body image and clothing demand, constructing a body feature representation, fusing clothing demand semantics to construct a fusion vector, matching a candidate clothing style based on the fusion vector, performing clothing physical simulation on a human body model through three-dimensional virtual fitting, analyzing fit and visual coordination characteristics, scoring and sorting in combination with user preferences, presenting a recommendation list, and optimizing based on user feedback. The application can accurately capture user body feature, and improve the fitting degree and personalization level of clothing recommendation.

The application provides a building appearance deformation detection method and device based on binocular vision and a storage medium, and belongs to the technical field of computer vision. The method is as follows: a circular target and a scale are arranged on the surface of a building body, a binocular industrial camera is used to shoot a building body image containing the circular target and the scale, and a camera pose is acquired; the circular target and the scale in the image are identified at a sub-pixel level; the circular target is used as an unknown point, a free network without control points is constructed in cooperation with the scale, and translation, rotation and scale constraint conditions of the free network are determined; under the constraint conditions, the three-dimensional coordinates of each circular target are solved by using the least square principle in combination with a camera imaging equation; in the fixed free network, the three-dimensional coordinates of the circular targets at different times are solved, and the deformation variable is calculated by subtracting the positioning results of adjacent two times. The method avoids the problem that the control points are unstable with the deformation of the building body in the building appearance deformation detection process, and realizes high-precision deformation detection of the building appearance.

A system and method for determining a sleep disorder in a patient is disclosed. A storage device stores a digital image including the patient's face and neck. A database stores previously identified phenotypes and dimensions of facial and neck features. A sleep disorder analysis engine is coupled to the storage device and the database. The sleep disorder analysis engine is operable to identify facial and neck features on the image by determining landmarks on the image. The sleep disorder analysis engine classifies at least one phenotype on the image based on a comparison with the database. The sleep disorder analysis engine correlates the at least one phenotype and at least one feature with a sleep disorder. The sleep disorder analysis engine determines a risk score for the sleep disorder based on the correlation between the phenotype and the feature.

The application discloses a composite material reinforcing body length calculation method and device based on image processing, and the method comprises the following steps: obtaining a composite material organization image, processing the composite material organization image to obtain a reinforcing body image with a single composite material reinforcing body, processing the reinforcing body outer contour of the reinforcing body image by adopting a skeleton thinning algorithm to obtain a first reinforcing body skeleton, performing point-by-point detection based on the first reinforcing body skeleton, and removing redundant points to obtain a more accurate second reinforcing body skeleton. The front moving method is adopted to extract the reinforcing body axis in the second reinforcing body skeleton, and the longest one in the reinforcing body axis is found as a reinforcing body main axis for calculating the reinforcing body length. The method is simple, the calculation amount is small, the reinforcing body main axis obtained can better reflect the main part of the reinforcing body length, and the shortest distance between the reinforcing body main axis two end points and the reinforcing body outer contour makes the calculation of the reinforcing body length more accurate.

A first neural network is trained to generate a ground truth using a small set of example images that illustrate the goal ground truth output images, which can be full-body images of people in an AR style. The first neural network is used to generate ground truth output images from random input images. Example methods of the first neural network include determining poses in input images, changing values of pixels within areas of the input images, inputting the poses, the areas of the changed input images, and a text prompt describing the input images, into a neural network, to generate output images. The methods further include determining losses between the output images and the input images and updating weights of the neural network based on the losses. A second neural network is then trained using the generated ground truth. And, an application is generated that uses the second neural network.

This invention relates to the field of oil and gas worker safety monitoring technology, specifically to a method and system for monitoring oil and gas worker fatigue based on multimodal information fusion. The method includes: acquiring facial images, full-body images, and heart rate data of oil and gas miners; performing multiple enhancements on the facial and limb images: adaptive illumination equalization, dust defogging, and motion deblurring processing to obtain three enhanced images, which are then weighted and fused to obtain enhanced facial and full-body images; extracting fatigue expression feature values ​​and facial dynamic feature values ​​from the enhanced facial image, and extracting limb feature values ​​from the enhanced full-body image, and fusing the three to obtain a fatigue quantification value; and correcting the fatigue quantification value based on heart rate data to determine the fatigue level, thereby achieving monitoring and graded early warning of miner fatigue. This invention improves the accuracy of miner safety monitoring.

The present application relates to the technical field of computer vision, and in particular to a fish body key point detection method based on double attention enhancement HRNet, which comprises the following steps: obtaining an underwater fish body image, labeling key points, and constructing a fish body key point dataset; then constructing an initial feature extraction module of global-local feature fusion; then constructing a fish body key point detection model based on an HRNet network and embedding a double attention mechanism module; then using a distribution perception method to relieve coordinate regression quantization bias; and finally restoring the detected key points to the original image space and outputting the fish body key point detection result. The present application can specifically deal with the problems of low precision and poor robustness in underwater fish body key point detection, and effectively solves the problem of lack of fish body key point dataset in training the key point detection network.

The application discloses a kind of based on commodity on body digital person generation method, equipment and storage medium, comprising: obtaining text instruction and commodity image input to the semantic association feature and visual detail feature output by multi-modal model;Fusion semantic association feature and visual detail feature obtain commodity on body instruction;In response to commodity on body instruction, adjust commodity image and preset model image, use diffusion model to generate initial commodity on body image;Positioning the modification area of initial commodity on body image, after verifying the consistency of image information in modification area with corresponding area in the commodity image and the preset model image, call text rendering strategy to render the initial commodity on body image to obtain commodity on body image.The application extracts commodity semantics and visual features through multi-modal model, adjusts image scale and spatial perspective using diffusion model, and realizes the technical effect of natural and efficient fusion of commodity and digital person through consistency verification and text rendering.

The application discloses a kind of full load state vehicle-mounted scrap steel automatic identification method, system, equipment and medium, comprising: obtaining the image of vehicle-mounted scrap steel to be identified;Using the image segmentation depth network constructed in advance to the image of vehicle-mounted scrap steel to be identified Segmentation, obtain preliminary target area image;Using the breadth-first search algorithm of single connected domain to the scrap steel main body independent non-evaluation main body in preliminary target area image is excluded, obtain intermediate target area image;Using secondary minimum rectangular contour line method to the scrap steel main body in intermediate target area image is polygonal correction, obtain final target area image;Using minimum cost translation target optimization method extracts the scrap steel main body in final target area image, obtain accurate scrap steel main body image.The application greatly improves the identification precision of scrap steel main body.

This application discloses a method, device, and medium for generating clothing patterns. The method includes: acquiring full-body images of a user from multiple perspectives; analyzing the full-body images to extract the user's body shape features; determining the corresponding clothing pattern topology based on the body shape features; generating a basic clothing pattern corresponding to the user based on the clothing pattern topology and clothing customization requirements; constructing a virtual 3D model corresponding to the user based on the body shape features; loading the basic clothing pattern into the virtual 3D model; simulating and rendering virtual clothing images of the user in different movement postures by adjusting the posture parameters corresponding to preset key parts in the virtual 3D model; evaluating the fit of the basic clothing pattern based on the clothing surface deformation parameters in the virtual clothing image; if the fit is less than a preset fit threshold, adjusting the basic clothing pattern based on the clothing surface deformation parameters until the adjusted basic clothing pattern fits the user.

The present invention relates to an artificial intelligence-based virtual clothing fitting system comprising: a body information generating unit that generates body information including a user's height, chest circumference, waist circumference, and hip circumference based on a body image of a user captured by a 3D camera; an avatar generating unit that generates a 3D avatar based on the body information; a clothing generating unit that generates a 3D clothing based on a previously stored clothing drawing; and a simulation unit that fits the 3D clothing to the 3D avatar to generate simulation information indicating the elongation rate, curvature, and contact pressure of the 3D clothing. The system includes a rendering unit that visually outputs the 3D avatar fitted with the 3D clothing based on the simulation information, and the body information generation unit includes a preprocessing module that generates 3D point cloud data from the body image and preprocesses the body image by performing background removal, noise filtering, surface refinement, and alignment processing on the point cloud data; an extraction module that inputs the preprocessed body image into an artificial intelligence model based on a PointNet architecture to extract feature points from the point cloud data, wherein if the prediction reliability of the feature points falls below a preset threshold, the feature points are discarded or classified as targets for re-estimation; and a computation module that generates the body information by performing a consistency check by comparing the distance between the feature points with standard human body ratios, determining feature points exceeding a preset tolerance as errors, and calculating the user's height, chest circumference, waist circumference, and hip circumference based on the distance between feature points that passed the consistency check. The simulation unit calculates the elongation rate, the curvature, and the contact pressure by placing the 3D clothing on the surface of the 3D avatar, wherein the elongation rate is the 3D It is calculated based on the degree of surface deformation of the garment, and the curvature is calculated based on the change in curved slope and folding distribution of the 3D garment.The above contact pressure is calculated based on changes in the contact position and contact distance between the 3D garment and the surface of the 3D avatar, and the simulation unit generates predictive simulation information reflecting future body shape changes of the 3D avatar through an artificial intelligence prediction module comprising a recurrent neural network (RNN) with a Long Short-Term Memory (LSTM) or Gated Recurrent Unit (GRU) structure that performs time-series-based body shape change modeling based on user information including health status information, age information, weight change trend information, pregnancy status information, gestational week information, hormone change history information, and seasonal environmental information; if the user is pregnant, the artificial intelligence prediction module generates a 3D avatar at a future point in time by reflecting changes in the body shape of the user's abdomen, hips, and thighs according to the gestational week information, and generates the predictive simulation information by calculating the elongation rate, curvature, and contact pressure of the 3D garment for the 3D avatar at the future point in time; and based on the seasonal environmental information, reflects changes in the body shape of the abdomen and upper body due to reduced outdoor activity and weight gain in winter, and sweat wicking and body heat dissipation efficiency in summer A 3D avatar at the future point in time is generated by reflecting a decrease in body shape due to increase, and the elongation rate, curvature, and contact pressure of the 3D clothing for the 3D avatar at the future point in time are calculated to generate the prediction simulation information. The rendering unit generates a rendering frame at the present point in time based on the simulation information and generates a rendering frame at the future point in time based on the prediction simulation information. The rendering frame at the present point in time and the rendering frame at the future point in time are output simultaneously, and enlarged display or color contrast is applied to mesh areas where the difference in elongation rate, curvature, and contact pressure between points in time exceeds a preset reference value. According to the present invention, a digital clothing is virtually fitted to a 3D avatar generated based on the user's body information.By providing results visually and simultaneously quantitatively analyzing fitting responses—such as garment deformation patterns and fit characteristics—it is possible to offer a fitting experience close to actual wear in an online environment. This allows consumers to more accurately select clothing suitable for their body types, thereby reducing return rates and increasing purchase satisfaction. Furthermore, based on the quantitative analysis of wearing responses, various functions such as size recommendations, fit predictions, and wear simulations can be automated. This enables applications in diverse fields, including clothing design, virtual fitting shopping malls, and personalized recommendation systems, contributing to improved user experience, distribution efficiency, and the digital transformation of the entire industry.