2621 results about "Image processing" patented technology

The invention discloses a video transmission method and system based on streaming processing, and relates to the technical field of video transmission, and the method comprises the steps: collecting a video stream, carrying out the preprocessing, extracting a video frame, packaging the video frame into a picture stream, carrying out the data fragmentation, distributing an initialization path for fragmented data, and calculating an initial traffic proportion; and optimizing path selection and adjusting path flow distribution according to network feedback to obtain a path optimization result, and transmitting the fragmented data according to the path optimization result. Through multi-level feature extraction and key frame selection of video frames, fragmented packaging of video data is realized, a dynamic path optimization technology is combined, path selection and flow distribution are dynamically adjusted based on a Q learning algorithm and a luminescence firefly algorithm, the transmission efficiency is improved, video image processing and a dynamic network optimization technology are fully combined, and the video image processing efficiency is improved. And a brand new solution is provided for efficient and stable transmission of high-definition video streams.

The invention provides a tunnel crack identification method and system based on multi-source image processing, and relates to the technical field of tunnel engineering, and the method comprises the steps: obtaining multi-source data of a horizontal rock stratum tunnel; performing spatial registration on the multi-source data to generate a multi-modal image under the same reference system; extracting multi-modal features according to the multi-modal image, and constructing a multi-source feature map under the same space grid; performing crack initial detection on the multi-source feature map in combination with multi-scale filtering and structure tensor analysis to obtain a crack candidate region mask; accurate crack identification is carried out through the crack candidate area mask, a crack identification result is obtained by combining bedding direction constraint and a microconditional random field, and the crack identification result comprises a crack segmentation map and a crack category. The method solves the problem that existing crack identification does not consider the bedding characteristics of the horizontal rock stratum tunnel.

The invention relates to an infrared and visible light image fusion method based on a cross-domain Transform, and belongs to the field of computer image processing. The method comprises the following steps: respectively carrying out preprocessing operation on an infrared image and a visible light image to obtain a training data set; an end-to-end image generator network is designed, an encoder module is used for extracting deep semantic features of an infrared image and a visible light image, a fusion module introduces an axial attention mechanism to enhance the global modeling capability of the features, and feature fusion is carried out in combination with information of a spatial domain and a frequency domain; the fused features are gradually recovered to an image space through a decoder module, and a fused image is generated; constructing a fusion loss function module, and guiding the network to focus a significant feature difference between the source image and the fusion image based on a comparative learning idea; and finally, inputting the infrared and visible light image Y channel into the network model, generating a fusion image, completing a training process, and realizing unified optimization of fusion performance and visual quality.

Methods and systems for remote check deposit are disclosed. A check for deposit is processed without the need for a server to receive any image of the check initially. Instead, optical character recognition (OCR) data is received at the server from a mobile device. Verification processing for the check is then performed using the OCR data. If the verification process is successful, a confirmation notification is sent to the mobile device. Subsequently, after sending the confirmation notification, a check image is received, from which the OCR data was determined. The check is, in turn, processed for deposit using the received check image.

Methods, systems, and metadata for video communications using generative face video (GFV) are described. In encoder, a GFV bitstream is generating comprising multiplexed coded face video pictures and GFV metadata. Using supplemental enhancement information (SEI), a GFV SEI message comprises syntax elements describing face features and at least one or more of: presence of a single or multiple faces, spatial sampling, temporal sampling, primary code picture characteristics and driving-picture handling, background handling, persistence of SEI, and compression parameters for face features. In a decoder, the decoder combines information extracted from the GFV metadata and the decoded face video pictures to generate a reconstructed output video.

The invention relates to the field of infrared image processing, and discloses a signal transmission method and system of an infrared thermal imaging movement, and the method comprises the steps: obtaining initial infrared thermal imaging data, network environment parameters and packet loss information; according to the initial infrared thermal imaging data and the network environment parameters, transmission strategy self-adaptive dynamic adjustment is carried out, and an optimized transmission strategy is obtained; according to the packet loss information, performing packet loss mechanism analysis to obtain a packet loss mapping relation model; dynamically balancing image quality and transmission real-time performance according to the network environment parameters, the optimized transmission strategy and the packet loss mapping relation model to obtain a balance constraint condition; estimating a future bandwidth demand according to the balance constraint condition to obtain a network resource pre-allocation scheme; and according to the network resource pre-allocation scheme, establishing a closed-loop optimization model and carrying out model optimization iteration to obtain a stable-state transmission scheme. According to the method, self-adaptive dynamically-adjusted infrared thermal imaging movement data transmission can be realized.

A row detection system includes first and second imaging devices to be mounted on an agricultural machine, and a processor configured or programmed to perform image processing for first and second images which are generated by the first and second imaging devices, respectively. The second imaging device is located so that at least a portion of each front wheel and at least a portion of each rear wheel are included in the second image. The processor is configured or programmed to convert the first image into a first plan view image, convert the second image into a second plan view image, select a region of interest from the first plan view image based on a position of each front wheel and a position of each rear wheel in the second plan view image, and perform a row detection process targeted at the region of interest.

The invention relates to a machine vision-based electric power operation risk behavior violation intelligent identification method, which comprises the following steps of: acquiring an image of a violation scene, and labeling the collected image; preprocessing and storing the image; based on the preprocessed image, carrying out classification training on the extracted feature information, and constructing a classifier model used for identifying violation behaviors; performing autonomous feature learning based on calibrated positive and negative samples, and training a classifier model; and collecting behavior pictures of the on-site overhead working personnel in real time, and performing violation information intelligent identification through the trained classifier model. According to the method, the supervision efficiency of electric power safety supervision personnel is improved, electric power operation site violation behaviors can be timely found, effectively handled and reasonably solved, a template matching method, a deep learning algorithm and a feature extraction and video image processing analysis identification technology are utilized, an intelligent violation identification function is realized, and the method is suitable for popularization and application. Intelligent identification, automatic research and judgment, instantaneous alarm and log recording are carried out on common high-altitude operation personnel behaviors (such as ladder climbing), electrified interval or open fire area calibration, scaffold erection, foundation pit soil piling height, fence limb distance measurement and other violation behaviors on an electric power operation site, and a breakthrough from remote inspection to intelligent supervision is achieved.

The invention discloses a virtual fitting method and device, a storage medium and electronic equipment, and is applied to the technical field of computer image processing, and the method comprises the steps: processing a target video, and obtaining the human body posture data of each video frame in the target video; constructing a 3D human body model of each video frame by using the human body posture data of each video frame; for the 3D human body model of each video frame, acquiring clothing rendering information of the 3D human body model, and rendering virtual clothing on the 3D human body model based on a dynamic fitting algorithm and the clothing rendering information to obtain a virtual rendering video frame of the video frame; and generating an AR fitting video based on each virtual rendering video frame, and displaying the AR fitting video to the user. Therefore, the selected costume can be tried on in the AR form, the fitting effect of the costume can be displayed for the user, the problem that the size is improper due to the fact that the costume purchased online cannot be tried on is solved, the probability of refunding and changing goods is reduced, and good shopping experience is provided for the user.

An image processing apparatus is disclosed. The image processing apparatus of the present invention comprises: an image receiving unit for receiving a first image and a second image of the same object taken at different times; a processor for obtaining transformation information by registering the first image on the basis of the second image, obtaining a first segment image corresponding to an area of the object from the first image, and generating a second segment image corresponding to an area of the object of the second image by transforming the obtained first segment image according to the transformation information; and an output unit for outputting the second segment image.

The invention discloses a diabetic foot assessment method based on wound image recognition. The method comprises the following steps: processing a foot infrared thermal imaging image to generate a temperature anomaly graph; fusing the foot wound image and the temperature anomaly graph to generate a fused image; constructing a foot blood flow analysis model, extracting a blood flow influence factor, and correcting the fused image through the blood flow influence factor; and constructing a joint assessment model, taking the corrected fusion image and historical data of the diabetic foot patient as input of the joint assessment model, predicting a risk assessment level of the diabetic foot, and outputting a disease assessment result of the diabetic foot patient according to the risk assessment level. According to the method, multi-modal medical image fusion analysis is realized, the accuracy of wound area positioning and pathological state expression is improved, the accuracy of lesion area recognition is improved, the illness worsening risk can be predicted in advance, clinical decision can be assisted, the risk of serious complications such as amputation of a patient is reduced, and the prognosis and life quality of the patient are improved.

The invention discloses a video image dithering processing method and system based on a rescue scene, and relates to the technical field of video image processing. In order to solve the problems that in the prior art, the jitter compensation effect in a complex dynamic scene is poor, the processing precision cannot meet the requirement of a high-intensity rescue scene, and in a low-light or smog environment, the definition of an image is difficult to effectively improve, so that the visibility of a video picture is insufficient in an extreme rescue scene. Through the steps of video jitter detection, video stream correction, edge processing and video data transmission, the visibility of a video picture in an extreme rescue scene is effectively improved, jitter in video stream data can be accurately identified and corrected, edge distortion generated by jitter compensation is eliminated through edge processing, and the video jitter compensation accuracy is improved. According to the method, the image definition in a low-light environment is remarkably improved through brightness compensation, contrast optimization and color correction processing, the stability and definition of video images in a rescue scene are improved, and more reliable video support is provided for high-intensity rescue actions.

The invention discloses a hydraulic engineering dam body crack detection method and system based on machine vision, and relates to the technical field of computer vision. A high-definition camera is used for shooting a dam body image, obtaining a sample data set, extracting feature parameters of different noise types, and constructing a feature database; filtering the current dam body image to obtain a first dam body image, enhancing the crack gray scale difference of the first dam body image, obtaining a second dam body image, segmenting the second dam body image, screening candidate cracks in the crack image, and identifying real cracks according to the edge features of the candidate cracks. According to the method, a feature database is constructed, a targeted dam body image processing flow is combined, a high-definition camera collection and automatic processing flow is used, filtering parameters are dynamically adjusted by constructing the feature database, gray level enhancement and precise segmentation are combined, real cracks are precisely recognized through edge feature analysis and comparison, and quantitative parameters are output; and a reliable basis is provided for dam body safety assessment.

The invention discloses an alkali metal liquid drop shaping mechanism and an alkali metal content measuring device and method. The alkali metal liquid drop shaping mechanism comprises a shell, an alkali metal liquid drop quasi-circular shaping system, an alkali metal content analysis system and a control system, wherein the alkali metal liquid drop quasi-circular shaping system is used for driving disorderly distributed alkali metal liquid drops to a certain fixed surface of the air chamber and shaping the shape of the alkali metal liquid drops into a quasi-circular shape; the alkali metal content analysis system comprises a gas chamber analysis chamber, a gas chamber placement block, a digital microscope, an annular light source and a development board, and is used for acquiring a shaped alkali metal liquid drop image and processing the image to obtain required information; and the heating assembly, the refrigerating assembly and the alkali metal content analysis system are respectively connected with the control system in a wired or wireless manner. Quasi-circular shaping and content measurement of the alkali metal liquid drops in the air chamber can be realized, so that a more convenient and lossless air chamber internal alkali metal content testing mode is provided for researchers, and service life information of the alkali metal air chamber is perfected.

The embodiment of the invention provides an image-based multi-target detection method and device, electronic equipment and a storage medium, and belongs to the technical field of financial science and technology. The method comprises the following steps: performing frequency domain transformation on an initial image to obtain an image frequency domain feature; enhancing the initial image based on the image frequency domain features to obtain an enhanced image; processing the enhanced image based on the multi-scale feature fusion sub-model to obtain a multi-scale fusion feature map; detecting the multi-scale fusion feature map based on a key point detection sub-model to obtain key point detection data; calculating the candidate key points based on the key point position data to obtain key point distance similarity; calculating the candidate key points based on the predicted key point category and the key point distance similarity to obtain key point semantic similarity; and based on the key point grouping sub-model, grouping the key point semantic similarity, the key point distance similarity and the candidate key points to obtain a target key point group. According to the embodiment of the invention, the accuracy of image-based multi-target detection can be improved.

The invention discloses a self-adaption eye fundus image processing method and device based on a passive field. The method comprises the following steps: acquiring an eye fundus image with a label in a source domain and an eye fundus image without a label in a target domain, and preprocessing the eye fundus image; training a teacher model and optimizing model parameters; using teacher model parameters to initialize two student models with the same structure; inputting the target domain unmarked fundus image into the teacher model and the student model for pixel-level learning; constructing category prototypes of optic cups and optic disks based on high-confidence features extracted by the teacher model, and guiding student model features to be aligned with the category prototypes; calculating the prediction difference of the teacher model and the student model in the boundary region, and correcting the segmentation result of the fuzzy boundary; constructing a total loss function, and iteratively updating teacher model parameters to obtain a fundus image segmentation model; and performing optic cup and optic disk segmentation on the target domain eye fundus image by using the eye fundus image segmentation model, and performing quantitative analysis. According to the method, the generalization and segmentation performance of the model on the target domain is remarkably improved.

The invention discloses a two-stage unsupervised defogging method based on a pseudo haze image, and belongs to the technical field of computer image processing, and the method comprises the following steps: building a training data set containing a haze image and an unpaired clear image; constructing a two-stage unsupervised defogging network based on the pseudo haze image; training the defogging network by adopting the training data set until a preset loss function is converged; and inputting a to-be-defogged image into the trained defogging network to obtain a defogged image. According to the invention, learning training is carried out on the unpaired real haze-clear image pair, so that the defogging effect of the network on the haze image is improved, and the generalization ability of the network is improved; according to the method, traditional dark channel prior and decomposition and reconstruction of an atmospheric scattering model are introduced into a deep learning framework, so that the network defogging performance is improved, and the network interpretability is improved.

The invention provides a robot electrical equipment defect detection system based on multi-modal image processing. The system improves the accuracy and reliability of electrical equipment defect identification. Infrared and visible light images are jointly collected, and through a registration algorithm of multi-source features and equipment structure priori, space-time alignment of multi-modal images is achieved. Then, a dynamic weighted fusion strategy is utilized to generate fusion features with higher discriminative ability, and abnormal features are extracted through a double-branch mechanism to be verified with thermophysical consistency; and finally, constructing a neural network model fused with physical prior, and performing defect classification and positioning output on the verified feature data. According to the method, the structure and thermal information are fused, a physical constraint mechanism and a joint training strategy are introduced, the robustness and engineering interpretability of the system under complex working conditions are remarkably improved, and the method has a wide application prospect.

An infrared image processing method, apparatus and device, and a storage medium are provided. The infrared image processing method includes: acquiring an infrared image; performing target detection on the infrared image to determine target objects within the infrared image and obtaining a target detection result that include a category of each of the target objects; determining a target object of interest from the target objects; determining a contour area of the target object of interest; performing true-color processing on the contour area to highlight the target object of interest. By highlighting the target object of interest in the infrared image through true-color processing, the target object of interest can be displayed more prominently with different colors and textures according to its category, so that the target object of interest can be presented more clearly in the infrared image clearer and easier for the human eye to observe and recognize.

The invention discloses a heterogeneous double-flow fusion method and system for diabetic retinopathy grading. The method comprises the following steps: obtaining an output result of diabetic retinopathy grading by utilizing a heterogeneous double-flow architecture; processing an input fundus image into images with different resolutions; extracting global context features from the low-resolution image by using a lightweight visual Transform model distilled by composite knowledge, and extracting local focus features from the high-resolution image by using a convolutional neural network model; performing interactive fusion on the global context features and the local focus features of the double-branch architecture through a symmetric bidirectional cross attention fusion module to obtain enhanced fusion feature representation; and finally, inputting the fusion features into a classifier, and outputting a severity grading result of the lesion. The method aims at improving the accuracy and robustness of hierarchical diagnosis through deep analysis of global information and local details, and can be applied to the medical fields of clinical computer-aided diagnosis, eye image analysis and the like.

The invention discloses an industrial surface defect image classification method based on hybrid query strategy active learning, and belongs to the technical field of computer image processing and machine learning. The invention aims to solve the technical problems of high cost, long period and low rare defect recognition rate caused by category imbalance due to dependence on large-scale manual labeling. The core of the method is to execute a hybrid query strategy in an iterative loop: firstly, screening out a candidate sample set of model cognitive ambiguity through uncertainty measurement of prediction entropy; secondly, in the candidate set, a core set and hierarchical thought diversity sampling method is adopted to select a final to-be-labeled sample with both characteristic representativeness and category balance. And the query batch is manually annotated and then is used for carrying out iterative updating and optimization on the model. According to the method, the recognition precision and generalization ability of the classification model on various defects can be remarkably improved with extremely low manual labeling cost, and the model development period is greatly shortened.

The invention discloses a laser welding seam online quality detection system and method based on multi-mode visual fusion, and particularly relates to the technical field of laser welding seam quality detection. Comprising a data acquisition and synchronous control unit, a 2D image processing and defect detection unit, a 3D point cloud processing and size measurement unit, a multi-modal information fusion and collaborative analysis unit and a comprehensive quality judgment and output unit. According to the laser welding seam online quality detection system and method based on multi-modal visual fusion, the 2D and 3D multi-modal visual fusion technology is adopted, an improved defect detection algorithm and an accurate size measurement method are combined, welding seam surface defects are accurately recognized through 2D images, key size data are obtained by means of 3D point cloud, and the detection accuracy is improved. And the limitation of'heavy defects and light sizes' or'heavy sizes and light defects' in single-modal detection is avoided, and comprehensive and accurate evaluation of the welding seam quality is realized.

The invention discloses an image strong light inhibition method and system, and relates to the technical field of visual imaging, and the method comprises the steps: obtaining an input video, carrying out the grouping marking, outputting image groups, obtaining and storing edge lines, calculating a brightness histogram of each single-frame image, segmenting regions, storing, and carrying out the image fusion. And converting each brightness value of the high-brightness area and the overexposure area into a brightness value of a corresponding reference area, re-splicing the suppressed single-frame images into a processed video and outputting the processed video, and obtaining a high-brightness image by identifying and grouping key objects in the video, finely adjusting saturation, contrast and brightness and segmenting and standardizing an illumination area. The reference images are fused to balance illumination and color differences, brightness values of high-brightness and overexposure areas are converted and suppressed, finally, the original images are spliced, the illumination, saturation, contrast and brightness of the processed video are remarkably improved, the overall visual effect is more natural and comfortable, and important details and dynamic changes are reserved.

The invention relates to a non-paired high-definition image enhancement method based on frequency decoupling and adaptive fusion, and the method comprises the steps: constructing an enhanced image generator to process an input image into a complete enhanced image, and then carrying out the authenticity discrimination of the enhanced image through a global-local dual-branch discriminator; constructing an enhanced image generator total loss function, a global discriminator loss function and a local discriminator loss function, training the enhanced image generator and a global-local double-branch discriminator by adopting an alternating optimization strategy until the three losses do not change any more to obtain a trained generator, inputting a new image into the trained generator, and obtaining a new image; and outputting an enhanced image corresponding to the instant new picture. Through frequency decoupling and modular processing path design, different frequency information is optimized in a targeted mode, the original structure and details of the image are effectively reserved, and the enhanced image has better visual effects in the aspects of brightness, color, edge and the like.

The invention discloses a breeding duck state image recognition method and system based on video monitoring, and relates to the technical field of breeding duck image recognition, and the method comprises the following steps: obtaining continuous multi-frame breeding duck images based on a monitoring camera; gray level image processing is carried out on the breed duck image to obtain a breed duck gray level image, binarization processing is carried out on the breed duck gray level image to obtain a breed duck binarization image, and a breed duck contour is obtained based on the breed duck binarization image; acquiring a two-foot fluctuation difference value of the breeding duck based on the consecutive multi-frame contour of the breeding duck; a normal fluctuation threshold value is obtained based on the fluctuation difference value of the two feet when the breeding duck walks normally; judging whether the breed ducks are limping or not based on the two-foot fluctuation difference value of the breed ducks and a normal fluctuation threshold value; the method is used for solving the problems of inaccurate breed duck crippling identification and low identification efficiency caused by artificial breed duck identification in the existing breed duck crippling identification technology.

Proposed herein are an apparatus and method for conversion into sketches. The apparatus for conversion into sketches includes: an input / output interface configured to receive an image and output the results of processing of the image; storage configured to store a program for performing a method for conversion into sketches; and a controller including at least one processor, and configured to convert the image into a sketch by executing the program. The controller receives (i) initial stroke information having attribute parameters and (ii) image information about a target image, converts the target image into a plurality of final strokes having geometric information about the target image by using a stroke generation model, and outputs the results of data processing of the plurality of final strokes.

The invention discloses a ship target detection and tracking method and system based on video image processing, and the method comprises the steps: 1), collecting a channel panoramic video stream through a high-definition camera, and extracting a continuous image sequence; 2) constructing a dynamic channel background model by using a Gaussian mixture model (GMM), and updating the background in real time to eliminate interference of water surface fluctuation and illumination variation; (3) enhancing the data obtained in the step (1) by adopting a Mosaic enhancement method, and establishing a data set; 4) optimizing a Yov7 model, and introducing a lightweight CBAM attention mechanism to carry out model training; (5) the trained weight model is experimented on the verification set, and target detection of the ship is achieved; and 6) adopting a kernel correlation filtering algorithm to realize real-time tracking of the moving ship. According to the method, the technology in the field of computer vision is utilized, the real-time video image number of the large-flow channel is effectively utilized, multi-target recognition and real-time tracking are achieved, and the method has high accuracy and robustness.

The invention relates to the field of infrared image processing, in particular to an infrared blind pixel detection method based on temperature self-adaption and time-frequency domain synthesis, which comprises the following steps of: traversing an infrared image by using a sliding window to obtain gray values of pixel points around a current pixel point; carrying out image edge judgment by using a Sobel-Otsu algorithm based on working temperature dynamic adjustment, and reserving a non-image edge window to participate in blind pixel judgment; grouping pixel points in the window according to horizontal and vertical directions; calculating the alpha filtering value of the gray scale of each group of pixels; calculating the deviation between the gray value of each pixel in the window and the alpha filtering value of the corresponding row coordinate and column coordinate group; if the deviation is greater than an adaptive threshold, setting the blind pixel as a potential blind pixel, and constructing a potential blind pixel distribution matrix; a time domain accumulation method for pre-screening periodic noise based on FFT is adopted, periodic noise interference is eliminated through frequency domain analysis, and then high-probability blind elements are screened through time domain accumulation; and obtaining a final blind pixel detection result. The method can effectively cope with the working temperature change of the sensor and the noise interference in a complex environment, and remarkably improves the accuracy and anti-interference capability of blind pixel detection.

The invention discloses an intelligent structural deformation and deviation detection method and system based on machine vision, and the method comprises the steps: obtaining the working data and state data of a detection target, carrying out the distortion processing of a camera image, obtaining a corrected image, processing the corrected image, obtaining the coordinate information of a target point, and carrying out the detection. Constructing a detection target three-dimensional model according to the target point coordinate information, comparing the model to obtain basic structure deformation and deviation, obtaining first structure deformation and deviation and second structure deformation and deviation according to the state data, and constructing a structure deformation and deviation intelligent detection model, and inputting working data and state data of a to-be-detected target into the structural deformation and deviation intelligent detection model to obtain a structural deformation and deviation detection result. The method not only can improve the precision of structural deformation and deviation detection, but also has good interpretability, and can be directly applied to a structural deformation and deviation intelligent detection system based on machine vision.

The invention discloses a hot-dip galvanized steel pipe ground cage verticality measuring system based on machine vision, and relates to the technical field of physical measurement, and the system comprises a projection unit which is used for projecting polarization gray scale stripes with polarization codes to a to-be-measured hot-dip galvanized steel pipe ground cage; the imaging unit comprises at least two cameras which form a triangulation base line with the projection unit so as to synchronously acquire multi-view images from different views; and the processing unit is configured to perform polarization difference fusion on the multi-view images to suppress mirror reflection, generate corresponding polarization difference fusion images, reconstruct pipe body three-dimensional point clouds of all the hot-dip galvanized steel pipes in the corresponding hot-dip galvanized steel pipe ground cages, generate pipe body axial vectors by adopting robust cylinder axis fitting, and obtain the pipe body three-dimensional point clouds of all the hot-dip galvanized steel pipes in the corresponding hot-dip galvanized steel pipe ground cages. And calculating the perpendicularity deviation angle of the pipe body according to the axial vector of the pipe body and the gravity direction. Therefore, on the premise that the measurement precision is guaranteed, the spatial postures of the multiple steel pipes are synchronously collected and recognized at a time, multi-point repeated scanning is not needed, and the detection efficiency is remarkably improved.