4210 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 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.

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.

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 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.

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.

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.

The utility model relates to the technical field of imaging equipment, and particularly discloses a binocular handheld device and a focusing assembly, comprising a first focusing wheel and a second focusing wheel which are coaxially arranged and sequentially distributed along the axial direction; the first rotating shaft is arranged on the first focusing wheel, and the first rotating shaft and the first focusing wheel are coaxial; the second rotating shaft is arranged on the second focusing wheel, the second rotating shaft and the second focusing wheel are coaxial, and the second rotating shaft is arranged outside the first rotating shaft in a sleeving mode and can rotate relatively; the first moving part is used for being connected with the first movement, and rotation of the first rotating shaft can be converted into forward and backward movement of the first moving part so as to change the distance from the first movement to the first lens; and the second moving part is used for being connected with a second movement, and rotation of the second rotating shaft can be converted into forward and backward movement of the second moving part so as to change the distance from the second movement to the second lens. By applying the binocular handheld device and the focusing assembly provided by the utility model, the focal length adjustment operation is convenient.

The invention belongs to the technical field of image processing, and particularly relates to a casting surface defect detection method and system based on image segmentation, and the method comprises the steps: obtaining a gray-scale image and a brightness background image of a vortex casting to be detected, and calculating the illumination deviation of each position in the image; segmenting the grey-scale map of the vortex casting to be detected by using an Otsu method to obtain a binary image; constructing a circular structural element according to the width of the optical interference region and the resolution of the imaging equipment; performing corrosion operation on the binary image based on the structural elements to generate a structural saliency image; obtaining a defect saliency value by using a product of a gray value and an illumination deviation in the structure saliency map; and calculating a segmentation threshold value based on the statistical characteristics of the defect-free sample, and identifying the position of which the defect saliency value is greater than the segmentation threshold value as a defect point. Through physical constraint and illumination feature fusion, the problem that a dark vortex structure and a real defect are difficult to distinguish is effectively solved, and the detection precision is improved.

An embodiment of an image recording apparatus includes an eyeglass frame, at least one first optical unit disposed on the eyeglass frame for capturing a main scene image, and at least one second optical unit disposed on the eyeglass frame for capturing an auxiliary scene image. The first and the second field of view of the user at least partially overlap. The image resolution of the main scene image is lower than the image resolution of the main scene image and the field of view of the main scene image is larger than the field of view of the auxiliary scene image. The image recording apparatus further includes at least one processor for receiving the main and the auxiliary scene images, and generating a modified resolution copy of the main scene image based on the auxiliary scene image.

The invention discloses an image super-resolution reconstruction method and system based on a deep learning model, and the method comprises the steps: collecting a preset number of high-resolution images, and obtaining a degradation phenomenon which may occur during the imaging of a target imaging device; degenerating the high-resolution image according to a preset rule on the basis of a degeneration phenomenon possibly occurring during imaging of the target imaging equipment to obtain a corresponding low-resolution image; establishing a training data set according to the high-resolution image and the corresponding low-resolution image, constructing a preset neural network and a loss function, and inputting the training data set into the preset neural network for training until the loss tends to be stable to obtain an image super-resolution reconstruction model; and obtaining a to-be-reconstructed low-resolution image, and inputting the to-be-reconstructed low-resolution image into the corresponding image super-resolution reconstruction model to obtain a corresponding reconstructed high-resolution image. The low-resolution image generated by the method is closer to real degradation distribution, so that the quality of super-resolution reconstruction of the image is improved.

A system includes a processor circuit that receives intravascular imaging data from an intravascular imaging catheter, an x-ray image from an x-ray imaging device, and intravascular pressure data from an intravascular pressure-sensitive guidewire. The processor circuit correlates the intravascular imaging data and the intravascular pressure data with locations along a body lumen shown in the x-ray image. The processor circuit generates a longitudinal view of the body lumen based on the intravascular imaging data and outputs a screen display including the longitudinal view of the body lumen with an overlaid graphic representation corresponding to the intravascular pressure measurements.

The invention provides an image fusion method and device based on a Mangbar and a convolutional neural network, and the method comprises the steps: collecting a plurality of image pairs of infrared light and visible light through an infrared imaging device and a visible light imaging device in the same scene, and carrying out the preprocessing of the plurality of image pairs, constructing a model training set and a test set by using the preprocessed image pair; constructing an infrared and visible light image fusion model MCDFuse by adopting a Mangbar and a convolutional neural network; the training set is used for training an infrared and visible light image fusion model MCDFuse, the input of the infrared and visible light image fusion model MCDFuse is the preprocessed infrared and visible light image pair, and the output of the infrared and visible light image fusion model MCDFuse is the fused image; and carrying out image fusion test on the test set by utilizing the trained infrared and visible light image fusion model MCDFuse.

The invention discloses an AI algorithm-based full-appearance visual detection method and system for an energy storage socket, and the method comprises the steps of detection image collection, data analysis and processing, socket defect detection and detection optimization, and comprises the steps: adjusting the position of imaging equipment according to the specification parameters of the energy storage socket; acquiring socket and wiring copper column images through a color imaging device and a black-and-white imaging device respectively; processing the image to generate a model training data set; and constructing a defect detection model, outputting a comprehensive detection result through multi-module output consistency verification, and dynamically optimizing model parameters based on detection accuracy. The system comprises a detection image acquisition module, a data analysis processing module and a socket defect detection module, can realize high-precision and high-efficiency online detection of full appearance defects of energy storage sockets of multiple specifications, remarkably improves detection compatibility and reliability, and guarantees product quality.

The invention relates to a strain gauge patch microscopic inspection device and method based on multi-modal fusion, the device comprises an imaging device, an illumination and optical assembly and an auxiliary module, and the imaging device comprises a visible light camera, a polarization camera and an infrared camera. According to the invention, high-precision measurement requirements and challenges of complex industrial scenes are fully considered, and the method has the characteristics of multi-modal fusion, high-precision identification and dynamic self-adaption; a visible light-polarized light-thermal imaging multi-mode cooperative detection technology based on deep learning is used as a core method, and position deviation, adhesive layer defects and a curing state of a strain gauge are used as core monitoring indexes, so that the comprehensiveness and reliability of detection are remarkably improved; cross-modal feature fusion is realized through a cross attention mechanism and LSTM time sequence modeling, adaptive polarization illumination and online feedback control are combined, the method can adapt to various materials, deployment is simple and convenient, real-time detection is supported, and a guarantee is provided for high-reliability measurement in the field of precision manufacturing.

The invention discloses a multi-parameter cooperative control method for non-excavation repair of a drainage pipe network in urban updating, and relates to the technical field of municipal engineering, and the method comprises the steps: firstly constructing a quantum entanglement sensing pipe network digital twinborn model, fusing multi-source data, monitoring micro-strain in real time through a nano sensor, deploying a self-organizing intelligent node and terahertz imaging equipment, and constructing a quantum entanglement sensing pipe network digital twinborn model; multi-dimensional features are extracted, repair parameters are dynamically regulated and controlled through technologies such as magnetofluid plugging and multi-robot cooperation based on digital twinning virtual simulation, finally, the repair effect is evaluated and the scheme is optimized in combination with cognitive calculation and microbial self-healing technologies, and full-process intelligent control is achieved. The intelligent level of non-excavation repair of the drainage pipe network is improved, the problem that traditional monitoring is one-sided is solved through high-precision data fusion, precise construction control is achieved through cooperation of magnetofluid leaking stoppage and the robot, durability is enhanced through the microorganism self-healing technology, repair quality and efficiency are improved, and urban updating and sustainable development are promoted.