4515 results about "Imaging equipment" patented technology

The invention relates to a fabric flaw detection and traceability system based on edge calculation and computing power scheduling, which is suitable for intelligent quality control in a textile production process. The system comprises an acquisition unit, a modeling unit and the like. The acquisition unit acquires fabric images and environmental data through a multispectral imaging device and a process parameter sensor, and constructs time-aligned multi-modal feature tensors. The modeling unit extracts texture features by using unsupervised comparative learning in combination with fabric material characteristics, and generates potential texture fingerprint vectors. And the detection unit adopts a target detection network of a channel attention mechanism to identify fabric flaws and output positions, types and severity. The traceability unit analyzes correlation between defects and process parameters through time sequence causal reasoning, and constructs a causal atlas. And the optimization unit generates a process optimization vector according to the causal atlas and the risk score, and realizes visual display and edge control feedback, thereby constructing a real-time defect control and explainable traceability-oriented closed-loop quality management system.

The invention discloses an AI-based neurosurgery auxiliary robot visual positioning system, and relates to the field of visual positioning, which comprises the steps of deploying and initializing structured light scanning equipment and near-infrared imaging equipment, carrying out multi-modal image acquisition on a surgical area, and carrying out standardization processing, space-time alignment and fusion on the acquired image. A multi-modal visual acquisition and preprocessing module, a three-dimensional tissue model construction and registration module, a visual-anatomical feature recognition and extraction module, an AI auxiliary positioning and path optimization module, an intraoperative dynamic perception and feedback control module and a target position confirmation and instruction output module are constructed. According to the method, high-precision identification and dynamic modeling of a brain tissue structure are realized, real-time identification, path planning and position correction can be carried out on a key anatomical structure in an operation process, the precision, the intelligent level and the intra-operation response capability of neurosurgery operation are remarkably improved, the operation risk is effectively reduced, and the positioning reliability and the automatic control efficiency are improved.

The invention provides a processing method and system for reconstructing an LDI sub-pixel image through sparse compression and application, and the method comprises the steps: calculating a low-resolution to-be-exposed image corresponding to each phase structure in advance through a computer image compression algorithm; the low-resolution to-be-exposed images obtained through calculation are loaded to the digital micro-reflector according to the time sequence, and the display moment of each pattern is matched with the rotation position of the corresponding phase structure; coupling the pattern of the digital micro-mirror and the phase encoding mask in a frequency domain through a 4-f optical system, and reconstructing a high-resolution exposure pattern at a sub-pixel level by utilizing a diffraction effect; projecting a high-resolution light field of the reconstructed high-resolution exposure pattern to the surface of the photoresist, and forming a target circuit pattern by accumulating exposure dose; and the micro-nano structure with sub-pixel precision is obtained after development. The system comprises an image compression module, a pattern matching module and a pattern forming module. According to the invention, the manufacturing cost of the laser direct imaging equipment is reduced under the condition of the same precision.

The invention discloses a defoaming agent foam distribution analysis method based on image feature recognition, and particularly relates to the field of industrial foam behavior perception and analysis for recognizing an image object with a random mode as a feature, and the method comprises the following steps: obtaining a foam image sequence in a target area, and collecting the foam image sequence through imaging equipment, the image frames of the foam image sequence have time continuity; and performing disturbance feature extraction operation on the foam image sequence to obtain local disturbance speed information, membrane surface tension change trend information and form boundary fluctuation information of the foam edge within a preset time. According to the method, foam structure disturbance characteristics are extracted from an image time sequence, a structure evolution graph memory bank is constructed, and irregular sudden change image behaviors are identified in combination with a trend matching mechanism, so that dynamic perception and abnormal response of a sudden foam state without prior support are realized, and the problem that a random mode foam state cannot be identified is solved.

The invention discloses an intelligent welding quality evaluation method and system based on vision, and relates to the technical field of welding quality evaluation.The method comprises the steps that the welding process is monitored in real time through multi-mode imaging equipment, weld joint surface texture, molten pool heat distribution and three-dimensional shape information are synchronously obtained, and a three-dimensional voxel model is constructed; establishing a two-stage multi-channel architecture for executing dynamic resource scheduling, and extracting geometric features and deep semantic features in the three-dimensional voxel model; and on the basis of the defect confidence coefficient vector, in combination with the real-time process parameters and the molten pool state, a dynamic evaluation model of the welding quality index is constructed, and time-phased quality scores are calculated and output through stage weighting. According to the method, the welding seam surface texture, molten pool heat distribution and three-dimensional shape information are synchronously collected through multi-mode imaging equipment, a texture-temperature-space coordinate bound three-dimensional voxel model is constructed, and multi-source heterogeneous data fusion analysis of the welding quality is achieved.

The invention relates to the technical field of image data processing, and discloses a fine metal powder defect detection method and system based on image recognition, and the method comprises the steps: illuminating a metal powder region through a non-polarized light source, and collecting two images in an orthogonal polarization state through imaging equipment with a linear polarizer, according to the method, the defect morphological fingerprints are constructed through the polarization information entropy, the three-dimensional structure information is converted into light field order degree change for detection, the dimension limitation of traditional two-dimensional imaging is broken through, and the detection accuracy is improved. Hidden defects such as internal pores and aggregates can be accurately identified, and meanwhile, stable detection and material defect cross discrimination under a dynamic working condition are realized in combination with motion compensation and multiband entropy ratio verification.

The invention discloses a multi-source image data fusion-based fine decoration air crack seepage quality problem detection method and device, and solves the technical problem of how to carry out comprehensive, high-precision and intelligent detection on the fine decoration surface air crack seepage quality problem. Comprising the following steps: 1) receiving visible light image data, thermal infrared image data and three-dimensional laser point cloud data of a target refined decoration surface acquired from visible light acquisition equipment, thermal infrared imaging equipment and three-dimensional laser scanning equipment respectively; 2) carrying out feature extraction on the visible light image data, the thermal infrared image data and the three-dimensional laser point cloud data; 3) obtaining point cloud projection coordinates, and then performing association fusion on the first two-dimensional feature and the second two-dimensional feature with corresponding three-dimensional features to generate fusion point cloud data containing multi-source features; and 4) based on the fused point cloud data, carrying out defect classification identification and spatial positioning to obtain a detection result. And comprehensive and high-precision detection of the quality problem of air crack seepage of the finely-decorated surface is realized.

A method for identifying a log file for resolution of a fault of a medical imaging device, is provided. The method includes: obtaining log file browsing data describing one or more log files of the medical imaging device already viewed by a user to resolve the fault of the medical imaging device; obtaining problem data describing the fault of the medical imaging device; inputting the problem data and the log file browsing data to a machine learning algorithm, the machine learning algorithm being trained to predict, for each of a plurality of log files of the medical imaging device, and based on the browsing data, a resolution probability indicating a likelihood that the log file will assist in resolution of the fault of the medical imaging device; obtaining a prediction result from the machine learning algorithm in response to the inputting, the prediction result comprising a resolution probability for one or more of the plurality of log files of the medical imaging device; and identifying a log file for resolution of the fault of the medical imaging device based on the obtained prediction result.

The invention provides a heading machine positioning method and system, and relates to the technical field of heading machines, and the method comprises the steps: pre-arranging a target on a heading machine, and building a heading machine gravity center coordinate system; capturing a plurality of frames of images with targets by adopting imaging equipment; performing image recognition on the multi-frame image by adopting a target detection model, extracting a feature point set of an area where the target is located, and generating multi-frame data of each feature point in the feature point set; performing multi-angle fusion on each feature point based on the multi-frame data of each feature point, and converting each feature point from a two-dimensional coordinate in an image coordinate system to a three-dimensional coordinate in a world coordinate system; based on the centroid coordinate system of the heading machine, converting the three-dimensional coordinates of the feature points in the world coordinate system into the three-dimensional coordinates in the centroid coordinate system of the heading machine, so as to generate position information of the heading machine; the problem that an existing heading machine is poor in pose positioning result accuracy is solved.

Systems and methods are provided for enabling quick interactions with a user's frequent contacts from a wrist-wearable device. The method includes displaying, at a wrist-wearable device, a user interface including a plurality of representations of contacts. The plurality of representations of contacts includes a first representation of a first contact that is associated with an image-sharing application, and a second representation of a second contact that is associated with a fitness application that allows a user of the wrist-wearable device to compare performance of a physical activity against at least the second contact. The method includes, responsive to an input selecting the first representation of the first contact, causing activation of an imaging device for capturing an image. The method includes responsive to another input, capturing an image using the imaging device and causing the image to be sent to the first contact via the image-sharing application.

Disclosed in the present invention is a soft tissue photoacoustic / ultrasonic multi-modal image fusion method based on deep learning. The method comprises the following steps: an ultrasonic-photoacoustic imaging device collecting a human body soft tissue photoacoustic image and ultrasonic image, and performing size normalization processing; inputting the images into a spatial conversion module to convert same into a YCbCr space; inputting the images into a pre-convolutional module to change the number of data channels; inputting the images into a multi-scale feature extraction module to extract salient features of the source images; inputting the features into a filter prediction module to obtain a multi-scale filter; and inputting the filter into a filtering fusion and adaptive enhancement module and combining same with the input source images, so as to obtain a final fusion result. Compared with several traditional fusion methods and deep-learning-based fusion methods, the method provided in the present invention has a greater fusion effect, and more importantly, has an excellent real-time performance. Moreover, multiple modes of photoacoustic / ultrasonic fusion extension experiments are performed on a photoacoustic / ultrasonic multi-modal imaging system, verifying the effectiveness of the method in the present invention.

The invention relates to the technical field of display panel manufacturing, in particular to a liquid crystal module hot-pressing binding method based on intelligent temperature control, which comprises the following steps: step 1, hot-pressing area partitioning and dynamic temperature calibration: dividing a contact surface of a hot-pressing head into a plurality of independently controlled temperature areas, each area is provided with at least one temperature sensor, and the temperature sensors in all the areas are calibrated through thermal imaging equipment, so that the deviation between the sensors and the real temperature is eliminated; 2, dynamic temperature compensation and pressure cooperative control are carried out; step 3, iteratively optimizing hot pressing process parameters, and training the deep learning model based on historical binding data to predict an optimal temperature-pressure parameter combination of a next binding period; and automatically correcting the initial temperature threshold in the step 1 and the target pressure in the step 2 according to a prediction result. Through the intelligent temperature control and pressure cooperative control technology, the temperature and pressure in the hot pressing process of the liquid crystal module are accurately adjusted, and the bonding quality and the production efficiency are remarkably improved.

According to the intelligent imaging generation system and method, parameters of a spraying mechanical arm and imaging equipment are adjusted in a linkage mode in real time through a dynamic parameter adjusting module, and closed-loop control over the spraying quality is achieved in combination with a defect prediction and optimization decision module and a multi-modal process knowledge base. A high-resolution visual sensor, a capacitive thickness detector and a temperature and humidity sensor are integrated in the system, multi-source data are fused through a Kalman filtering algorithm, and the mechanical arm speed, the nozzle pressure and the imaging gain coefficient are dynamically adjusted. The defect prediction module simulates a coating defect form based on a physical constraint generative adversarial network and generates a process optimization instruction set through reinforcement learning. The multi-modal process knowledge base stores a correlation model of material characteristics, environmental data and defect modes, and supports incremental learning and historical data attenuation updating. And through dynamic parameter collaborative optimization, defect prediction preposition and multi-modal data fusion, the spraying quality detection efficiency and the coating consistency are improved, and the rework rate is reduced.

The invention relates to the field of traditional Chinese medicine diagnosis, and discloses a traditional Chinese medicine intelligent diagnosis system based on multi-source data fusion and an AI technology, and the system comprises a tongue picture collection and classification unit which is used for collecting tongue picture data of a patient through imaging equipment, carrying out the feature extraction of the tongue picture data, and obtaining a tongue picture feature data set; performing tongue picture pathological mode classification on the tongue picture feature data set based on a deep convolutional neural network to obtain tongue diagnosis identification result data; and the face diagnosis feature correlation analysis unit is used for inputting the tongue diagnosis identification result data into a multi-modal data fusion engine and carrying out face diagnosis pathological feature correlation analysis based on a graph attention network to obtain face diagnosis identification result data. The deep semantic understanding is realized by introducing a plurality of data acquisition modes such as three-dimensional imaging, thermal imaging, micro-expression recognition, spectral information and pulse condition harmonic analysis and combining advanced models such as a graph neural network, U-Net and Bi-LSTM.

The present invention provides an automatic focusing method, an electronic device, and a readable storage medium. The automatic focusing method comprises: using a target detection model to detect a target object in an image frame acquired at an initial focal length by an optical imaging device to be focused, so as to acquire a target object detection result; on the basis of the target object detection result, determining whether a target object is present in the image frame; if yes, determining an actual object distance on the basis of the target object detection result and the initial focal length, and on the basis of the actual object distance and a mapping relationship between the object distance and an optimal imaging focal length, determining the optimal imaging focal length; and if not, using a preset search algorithm to search for a focal length until an image having the highest definition value is found, and using a focal length corresponding to the image having the highest definition value as the optimal imaging focal length. The present invention primarily employs a deep learning-based automatic focusing method, supplemented by an image definition evaluation method. Compared with traditional passive focusing methods, the present invention greatly improves the focusing efficiency. Compared with traditional active focusing methods, the present invention eliminates a need for adding an additional optical ranging component, ensuring that the imaging device has a simple structure and a low cost.

A method for visually identifying crop rows includes acquiring imagery from at least one imaging device operatively connected to an agricultural vehicle while the agricultural vehicle is traversing a field, the imagery includes plants arranged in rows, processing the imagery at a computing device to preserve spatial information along a first dimension while reducing data complexity along a second dimension to generate processed data, analyzing the processed data to identify locations of the rows within the field. The method may include dividing the imagery into multiple sections along a direction of travel of the agricultural vehicle and for each section, performing statistical analysis to generate a one-dimensional intensity profile, applying an edge detection operation to identify predominantly vertical features within the imagery and filtering out predominantly horizontal features to isolate plant stalks, and / or identifying spatial relationships between neighboring rows and using the spatial relationships to validate row identification.

The present technology relates to an information processing device, an information processing method, and a program that enable appropriate monitoring of a condition of a patient even from a remote location. There are provided an acquisition unit that acquires at least video data from an imaging device that images a patient and video data from an imaging device that images an instrument monitoring the patient; and a detection unit that detects an abnormality of a condition of the patient by analyzing the video data acquired by the acquisition unit. A plurality of imaging devices that images a plurality of instruments monitoring the patient is grouped by reading a patient ID assigned to the patient using the imaging device. The present technology can be applied to an information processing device that monitors the condition of the patient.

A system includes a processor circuit that receives intraluminal images obtained by an intraluminal imaging device during movement through a patient's body lumen. The processor circuit outputs, to a display, a visual representation of user guidance in response to the processor circuit identifying, among the intraluminal images, a candidate intraluminal image. The user guidance includes stopping the movement and instructing the patient to initiate deep breathing. The processor circuit receives additional intraluminal images obtained by the intraluminal imaging device while the movement is stopped and the patient is deep breathing. The processor circuit determines if a shape and / or size of the body lumen changes in the additional intraluminal images. The processor circuit accepts or rejects the candidate intraluminal image based on if the shape and / or size of the body lumen changes. The processor circuit outputs, to the display, a visual representation corresponding to accepting or rejecting the candidate intraluminal image.

The invention is suitable for the technical field of illumination, and provides a near-illumination lamp illumination method, device and system and a near-illumination lamp, and the method comprises the steps: obtaining an original light field image, which is collected by imaging equipment and comprises an array composed of a plurality of independently addressable and brightness-controllable light-emitting units, projected on a target plane by the near-illumination lamp; generating a brightness nonuniformity map representing brightness distribution according to the original light field image; calculating a brightness correction matrix for compensating brightness nonuniformity through a reverse solution algorithm on the basis of the brightness nonuniformity map and in combination with a model for describing a light transmission response relationship between the array and a target plane; and loading the brightness correction matrix into a controller of the near-lighting lamp, and performing differentiated brightness modulation on each light emitting unit in the array by the controller according to the brightness correction matrix. The problem that an existing near-illumination lamp cannot achieve high-precision and high-stability uniform light field output is solved.

The invention belongs to the technical field of image processing, and mainly relates to a teenager scoliosis detection method based on three-dimensional imaging, and the method comprises the steps: obtaining multi-modal data such as a back depth map, temperature distribution, an attitude angle and a plantar pressure matrix of a detected person through a depth camera, an infrared imaging device, an I MU sensor and a plantar pressure distribution sensor; the cloud platform generates a three-dimensional point cloud and obtains a back three-dimensional curved surface model by adopting a Poisson surface reconstruction algorithm, overall attitude correction is carried out by combining an attitude angle and pressure gravity center shift, and a thermal correction model of local deformation is constructed based on a thermodynamic diagram; automatically identifying spine segment mark points through a deep learning algorithm, fitting a spine center line, calculating a Cobb angle, judging the scoliosis degree and outputting an analysis report; according to the method, X-ray imaging is not needed, the detection safety is high, attitude, mechanical and thermal information can be fused, artificial attitude interference is reduced, and the method is suitable for early screening and dynamic reexamination of teenagers.

The invention, which belongs to the technical field of artificial intelligence, discloses a multi-modal cross-view-angle air-ground cooperation big model system comprising a heterogeneous agent module, a multi-modal data processing module, a cross-view-angle fusion module, a big language model reasoning module and an action decision module. The heterogeneous intelligent agent module comprises an unmanned aerial vehicle, a ground robot and an unmanned vehicle, the unmanned aerial vehicle is provided with a high-resolution camera, an infrared imaging device, a laser radar and a meteorological sensor, the ground robot is provided with the laser radar, a camera and a force sensor, and the unmanned vehicle is provided with a camera, a radar and an inertial measurement unit. The multi-view features are fused through a cross attention mechanism, precise mapping from the features to real coordinates is realized in combination with a ScaleToRealClip model, and scale and coordinate differences of air and ground data are eliminated; meanwhile, the cross-view fusion module can dynamically update environment coordinates, help an intelligent agent to perceive temporary obstacles in real time, and construct a comprehensive and accurate environment cognition model.

A system and method for generating an interactive user interface for inspection visualization. The system includes multiple imaging devices positioned along a inspection passage and at least one processor that executes instructions to: obtain multiple sets of images of vehicle surface segments captured during relative movement between the vehicle and imaging devices; stitch the images into a dataset record mapping vehicle parts and surface anomalies; transform the image data into a moving visual media object using a first generative AI model; compute a mapping record between segmented vehicle parts and target frame areas; and transform the mapping record and visual media object into an interactive interface using a second generative AI model. The interface displays user-selectable markers synchronized with media playback, indicating anomaly locations from multiple viewing angles, and performs data retrieval and display actions based on user selection of anomalies.

The invention relates to the technical field of orthopedic image processing, and particularly discloses an orthopedic disease diagnosis assisting method and system based on multi-modal image fusion, and the method comprises the steps: S1, collecting skeleton image data and soft tissue image data through imaging equipment; s2, performing cross-modal feature interaction evaluation based on the skeleton image data and the soft tissue image data after preprocessing and feature extraction; s3, performing feature dimensionality reduction on the image data to be subjected to dimensionality reduction, evaluating and optimizing a feature dimensionality reduction result, and marking the image data to be subjected to dimensionality reduction after feature dimensionality reduction as low-dimensional image data; and S4, performing disease category probability prediction based on the low-dimensional image data, and generating a disease category probability distribution report. The problems of excessive redundant information, poor feature interaction effect and low disease category probability prediction accuracy in the existing multi-modal image fusion technology are solved.

The invention discloses a medical image semantic segmentation method based on attention mechanism optimization, and relates to the technical field of medical image processing, and the segmentation method comprises the specific steps: S100, data collection and label preprocessing: collecting medical image data from different medical institutions and a plurality of imaging devices, according to the method, the attention mechanism is introduced to carry out deep preprocessing on the medical image data, the precision and efficiency of semantic segmentation of the medical image are remarkably improved, the attention mechanism is utilized, key areas, such as diseased regions or tissue boundaries, in the image can be recognized and enhanced, meanwhile, noise and irrelevant information are effectively removed, and the accuracy of semantic segmentation of the medical image is improved. The refined preprocessing mode not only improves the quality of the image, but also provides a more accurate data basis for subsequent image detection and segmentation, and the method is also combined with a self-adaptive denoising algorithm, dynamic adjustment of contrast, brightness and color and a geometric transformation advanced preprocessing technology, so that the availability and diagnostic value of the image are enhanced.

A system may detect a triggering condition by an imaging device. The system may capture, by the imaging device in response to the triggering condition, an image of a container. The system may perform, by a machine learning model, pixel quantification of the container. The system may determine, based on the pixel quantification, a status of the container.

The invention discloses a lithium ion battery temperature anomaly detection and fault diagnosis method based on modal decomposition and infrared imaging, and belongs to the technical field of battery management. According to the method, basic operation data such as voltage, current and temperature of the lithium ion battery are collected, and an infrared imaging device is utilized to obtain a temperature distribution diagram of the surface of the battery, so that the temperature information of the battery is captured. VMD (variational mode decomposition) and BEMD (two-dimensional empirical mode decomposition) are adopted to perform mode decomposition on acquired temperature signals, and mode signals highly related to battery temperature abnormity or fault modes are extracted. Then, a CNN-LSTM model constructed by using a convolutional neural network (CNN) and a long short-term memory (LSTM) network is used for carrying out time sequence modeling on the extracted features, and hyper-parameters of the model are optimized through a particle swarm optimization (PSO) method; and finally, deploying the trained CNN-LSTM model into embedded equipment to realize real-time monitoring and fault diagnosis of the temperature state of the lithium ion battery.

The invention relates to an SEM (scanning electron microscope) and CT (computed tomography) compatible solid propellant mechanical property testing device and method, belongs to the technical field of precise scientific instruments and material micro-mechanical property testing, and solves the problems that in the prior art, due to the fact that imaging equipment is single, and image quality becomes poor due to creeping accumulation, micro-scale failure information of propellants is incomplete and inaccurate to obtain. In the mechanical property testing device, the in-situ mechanical testing device performs mechanical loading and load holding on the propellant sample, the SEM obtains the surface appearance of the propellant sample, the double-source double-probe industrial CT obtains the internal three-dimensional structure of the propellant sample, and the in-situ mechanical testing device is transmitted between the transfer cabin main cavity and the electron microscope cabin through the transfer channel. And the vacuum transfer cabin containing the in-situ mechanical testing device is transferred between the transfer channel and the scanning turntable of the double-source double-probe industrial CT by the mechanical arm. According to the invention, more comprehensive and accurate mesoscopic failure information of the propellant sample can be obtained while less time cost is paid.

An information processing device includes a location information obtaining unit that, from each of a plurality of imaging devices which is used in a mobile object and which searches for an object, obtains location information indicating the current location of the imaging device; and a deciding unit that, based on the location information of each of the plurality of imaging devices, decides on the imaging device to be used in searching for the object.

An imaging device for capturing images through an eyepiece of an optical viewing device. The imaging device includes a camera that aligns with the eyepiece for capturing the images viewed through the eyepiece, and a display for displaying the images captured by the camera. The imaging device determines a type of the optical viewing device. The imaging device presents a workflow on the display based on the type of the optical viewing device. The imaging device captures metrics related to user interactions with the workflow presented on the display and the images captured by the camera during the workflow.

The invention discloses a food waste detection method and system based on image processing, belongs to the field of image recognition, and aims to realize efficient and automatic recognition and quantification of kitchen waste. According to the method, residual food images are collected at multiple periods and multiple angles in a kitchen garbage can or a dinner plate recovery area through high-resolution and multi-spectral imaging equipment, preprocessing is carried out in combination with an improved Retinex algorithm and a space self-adaptive denoising technology, and the image quality is improved. Afterwards, fine segmentation of a food area is achieved through a multi-scale super-pixel segmentation and graph segmentation algorithm, and multi-category intelligent recognition is conducted on remaining food through a recognition network fused with multi-modal features. The system further combines stereoscopic vision and Monte Carlo sampling to dynamically and accurately count the volume or weight of various residual foods. The method has the advantages of high adaptability and accurate statistical result, and can provide data support for catering management, resource recovery, nutrition evaluation and the like.