109 results about "Image registration algorithm" patented technology

The invention belongs to the field of hollowing detection, and provides an outer wall hollowing detection method and system, and the method comprises the steps: carrying out the multi-mode detection of an outer wall, and obtaining an infrared thermal imaging image and an ultrasonic detection signal; performing image enhancement processing on the infrared thermal imaging image; respectively carrying out feature extraction on the infrared thermal imaging image and the ultrasonic echo image by using a convolutional neural network; extracting and fusing high-dimensional feature vectors, and aligning an infrared thermal imaging image with an ultrasonic echo image through an image registration algorithm and a spatial transformation model; and classifying the fused feature vectors through a multi-layer sensor according to the data aligned in the step, and outputting the specific position, size and severity of the hollowing area. According to the scheme, the outer wall hollowing detection can be efficiently carried out.

The invention provides a two-stage registration method and device suitable for a full-length image of a lower limb, a terminal and a medium. The method comprises the steps of obtaining a to-be-registered CT image and a label of the lower limb, and a target X-ray image and a label of the lower limb; inputting the to-be-registered CT image and the label, and the target X-ray film image and the label into an affine transformation network to generate an affine transformation matrix for performing rough registration on the to-be-registered CT image; and inputting the CT image and the label output in the coarse registration stage, and the target X-ray film image and the label into a deformation field registration network to generate a deformation field for fine registration based on the coarse registration result of the CT image to be registered. According to the method, the deep learning technology is utilized, a brand-new 2D / 3D medical image registration algorithm is constructed, and full-automatic cross-modal registration of lower limb full-length horizontal position CT and standing position X-ray films is achieved for the first time. The research result of the invention can provide effective assistance for preoperative accurate planning, assist the design work of an HTO operation guide plate, and improve the clinical curative effect of early and middle knee OA.

The invention relates to the technical field of historical building protection, and discloses an intelligent identification and diagnosis method for historical building brick wall surface damage, which breaks through the limitations of low efficiency, strong subjectivity and easy detail omission of traditional manual exploration by fusing unmanned aerial vehicle high-precision image acquisition, deep learning semantic segmentation and multi-modal feature analysis technologies. And automatic rapid positioning and accurate classification of large-range wall surface damage are realized. Environmental distortion is eliminated by using a self-adaptive image registration algorithm, fine texture features are extracted in combination with a damage probability prediction model, and complex damage forms such as salting-out crystallization, plant root erosion and weathering spalling are effectively distinguished; through three-dimensional space clustering and visual marking technologies, the damage distribution density and the evolution trend are visually presented, and a reliable basis is provided for scientifically formulating a grading repair strategy. Besides, through establishment of a damage database and iterative optimization of an intelligent diagnosis model, the building health condition can be tracked for a long time, and technical support is provided for preventive protection and sustainable management of cultural heritage.

The invention relates to the technical field of computer vision, and discloses a jade defect intelligent detection method and system based on machine vision and deep learning, and the method comprises the following steps: S1, based on a high-resolution industrial camera and a laser three-dimensional scanner, adopting a multi-mode synchronous collection strategy, and rotating a jade sample through a precise motion control system, a jade surface high-resolution two-dimensional color image and high-precision three-dimensional point cloud data are respectively obtained, and a jade multi-mode original data set is generated. A high-resolution two-dimensional color image and high-precision three-dimensional point cloud data are integrated through a multi-modal synchronous acquisition strategy, and multi-dimensional feature expression under unified coordinates is constructed, so that the limitation of a single data source is effectively overcome; an image registration algorithm and a feature pyramid network are combined with a point cloud network to perform multi-modal feature fusion, complementarity of color texture and geometric morphology information is enhanced, and image quality is optimized based on adaptive histogram equalization and non-local mean filtering.

Systems, devices, and methods are disclosed to correct aimpoint of a system of a weapon and affixed digital weapon sight. The method employs the components of a digital weapon sight configured together for a process of sensing of a ballistic event, storing of digital imagery data before and after a shot or series of shots, and then the performing of an image registration algorithm, via a cross-correlation type function. The image registration determines the misalignment between the images as a Cartesian shift, and aimpoint of subsequent shooting is corrected through application of adjusted reticle symbology or adjusted image sensor windowing. Preprocessing methods for enhancing image registration algorithm accuracy are presented.

The invention provides a cultural relic image acquisition system combining a depth-of-field stacking technology and a high dynamic range (HDR) splicing technology, and belongs to the technical field of image acquisition. According to the system, depth-of-field stacking and HDR splicing technologies are combined, focal plane positions and various exposure times are set firstly, and different exposure images are collected by each focal plane. And carrying out space alignment and brightness correction on the image by adopting an image registration algorithm based on feature points, and carrying out HDR synthesis on different exposure images of each focal plane to obtain a high dynamic range image. And calculating a definition evaluation value through a Laplace operator, and selecting the pixel with the highest definition to generate a panoramic depth image. Image feature point matching and alignment are carried out on multiple panoramic depth synthesis, seamless fusion is carried out through a smooth transition function based on weight, and the problems of splicing gaps and inconsistent brightness are eliminated. And finally, enhancing details by using a multi-scale Retinex algorithm, and performing color correction by using a standard color card to generate a high-precision and large-format cultural relic panoramic depth image.

The invention relates to a multi-modal registration method and device based on medical images, and the method comprises the steps: obtaining a brain registration data set, and dividing the brain registration data set into a training set and a test set; and constructing a registration network based on a UNet network structure. The method comprises the following steps: selecting an input image, inputting the input image into a dual framework, performing bidirectional primary registration on the input image to obtain forward and reverse deformation fields and a primary registration image, and applying the dual two-party exchange deformation field to the primary registration image to perform secondary registration to obtain a secondary registration image. And calling a Laplace operator to extract structural features from the input image, the primary registration image and the secondary registration image, and constraining the registration network through structural similarity measurement. And training an end-to-end unsupervised image registration algorithm through the training set and the test set, and optimizing the algorithm according to loss constraints. And inputting a to-be-detected registration image into the registration network, applying the optimal model weight, outputting a registration result, and ensuring the uniformity and accuracy of the result.

The invention discloses an automatic system for packaging substrate detection, and relates to the technical field of data encryption, and the system comprises a data collection module which is used for collecting multi-view image data and carrying out the preprocessing of the multi-view image data; the feature extraction module is used for performing feature extraction on the preprocessed multi-view image data; the feature map fusion module is used for carrying out registration fusion on the feature maps through an image registration algorithm based on the extracted feature maps to form a three-dimensional feature map of the substrate; the defect probability prediction module is used for constructing a defect identification model and predicting the defect probability of the substrate based on the three-dimensional feature map of the substrate; and the defect grade evaluation module is used for predicting a substrate defect degree score according to the substrate defect probability, evaluating a substrate defect grade according to the substrate defect degree score, and generating a substrate detection report. According to the method, intelligent identification and evaluation of multiple defect types are realized, and the detection precision and the automation level are remarkably improved.

The invention discloses a power line identification method, device and equipment based on image fusion and a medium, and the method comprises the steps: obtaining an infrared image and a visible light image of a power line of a power distribution network, and carrying out the registration of the infrared image and the visible light image through a preset image registration algorithm, and generating a first image; wherein the infrared image and the visible light image are derived from the same picture; performing feature extraction and feature fusion on the first image and the visible light image through a preset image fusion algorithm to generate a second image; and performing power line identification on the second image through a preset power line identification algorithm. According to the method, the infrared image and the visible light image which are derived from the same power line picture are fused to obtain the fused image which contains more source information and has more obvious power line characteristics, so that power line recognition is performed, the difficulty of power line recognition is greatly reduced, and the accuracy of power line recognition of the power distribution network is further improved.

Methods and systems for motion-corrected and motion-compensated laser speckle contrast imaging are disclosed. The method comprises: exposing a target region to coherent first light of a first wavelength, the target region comprising living tissue; and capturing at least one sequence of images. The at least one image sequence includes a first speckle image that is captured during exposure with the first light. The method further includes determining one or more registration parameters of an image registration algorithm for registering the first speckle images to each other. The method may further include determining a registered first speckle image by registering the first speckle image based on one or more registration parameters and an image registration algorithm, and determining a combined speckle contrast image based on the registered first speckle image; or determining a first speckle contrast image based on the first speckle image, determining a registered speckle contrast image by registering the first speckle contrast image based on one or more registration parameters and an image registration algorithm, and determining a combined speckle contrast image based on the registered first speckle contrast image.

The application relates to an airplane change detection method based on a deep learning and an image registration algorithm, and the method comprises the following steps: acquiring a first remote sensing image and a second remote sensing image of the same region at different time points; performing airport detection on the first remote sensing image and the second remote sensing image to obtain an image of a first airport region in the first remote sensing image, a first offset of the first airport region, an image of a second airport region in the second remote sensing image and a second offset of the second airport region; performing image registration on the image of the first airport region and the image of the second airport region according to a surf image registration algorithm to obtain a registration image; performing airplane detection on the registration image to obtain an airplane target; performing change detection on the result obtained by the airplane detection; and restoring a target frame corresponding to the airplane target to the first remote sensing image and the second remote sensing image. The application can solve the problems of low detection precision, slow detection process running speed and large hardware resource occupation in the prior art.

Systems and methods are disclosed to correct aimpoint of digital weapon sight. The method employs the components of a digital weapon sight configured together for a process of sensing of a ballistic event, storing of orientation data and digital imagery data before and after a shot or series of shots, and then performing an image registration algorithm using the imagery data stored. The image registration determines the translational offset between the images, and aimpoint of subsequent shooting is corrected through application of adjusted reticle symbology or adjusted image sensor windowing.

The invention provides a temporal-mandibular joint deflection determination method and system based on deep learning, and the method comprises the steps: obtaining a multi-view two-dimensional image and surface scanning data of a user; performing three-dimensional reconstruction on the multi-view two-dimensional image of the user by adopting a preset multi-view photo reconstruction algorithm, and determining a three-dimensional face model of the user; adopting a preset point cloud feature extraction network to determine a three-dimensional face model of the user and key feature points of the face scanning data; inputting the three-dimensional face model of the user and the key feature points of the face scanning data into a preset multi-modal image registration algorithm, and determining multi-modal data of the same three-dimensional space; inputting the multi-modal data into a preset deep neural network for diagnosis, and determining a diagnosis result of the user; and according to the multi-modal data, determining a facial index of temporal-mandibular joint deflection corresponding to the user. According to the method and the device, early screening of temporomandibular joint deflection is realized by adopting the two-dimensional image, the data acquisition cost is reduced, and the efficiency and the precision of medical diagnosis are improved.

The invention discloses a video image fusion processing method and device for a capsule endoscope, and the method comprises the steps: loading a plurality of segments of videos at different angles, which are shot by the capsule endoscope, into a processing module, carrying out the image enhancement preprocessing of the nth frame of each segment of video, carrying out the rough image registration of the preprocessed image through the pose data of a camera, and carrying out the image fusion processing; and carrying out image fine registration by using an SURF algorithm to determine an overlapping region, searching an optimal suture line in the overlapping region by using an optimal suture line image fusion algorithm, carrying out image stitching, and carrying out video stitching on the processed stitching image set. According to the invention, image coarse registration is carried out according to the pose data of the capsule endoscope, an SURF image registration algorithm is improved, and optimization and improvement are carried out in a feature point matching stage, so that the accuracy and timeliness of the algorithm are improved.

The present description relates to an image sequence motion correction method and apparatus based on intensity correction optical flow registration. The method comprises: determining a reference image of each deformed image in a deformed-image sequence, and performing motion correction on the deformed image by means of adjusting the weight of a mechanical regularization term between the reference image and the deformed image, so as to obtain a displacement field of the deformed image relative to the reference image; on the basis of the displacement field and an intensity correction value of a finite element node, performing intensity correction on the reference image; adjusting the weight of the mechanical regularization term, performing motion correction on the deformed image, performing intensity correction on a first corrected reference image on the basis of an optimized displacement field, repeatedly executing the process until an image residual function converges, determining that the deformed image has been corrected, and recording the optimal solution of a weight value of the mechanical regularization term of the deformed image; and on the basis of the optimal solution of the weight value, determining the actual displacement of the deformed image. In the present description, the mechanical model behind physiological motion and an intensity correction technique are integrated into an image registration algorithm to reduce image noise, thereby improving the algorithm accuracy.

The invention discloses an engineering building deformation monitoring method and system, and the method comprises the steps: obtaining a texture target image on a building, carrying out the preprocessing of the texture target image through employing a self-adaptive illumination compensation algorithm, and adjusting the brightness and contrast of the texture target image if the illumination change is detected, and obtaining a first image; performing denoising processing on the first image, removing noise in the first image by using a Gaussian filtering algorithm, and if rain and fog weather is detected, enhancing the definition of the first image by using a haze removal algorithm to obtain a second image; and carrying out displacement calculation on the fifth image, calculating the displacement amount of the feature points in the fifth image by adopting an optical flow method, and if image drift is detected, correcting the position of the fifth image by using an image registration algorithm to obtain the final deformation displacement amount of the building.

The invention relates to the technical field of textile double-sided printing, in particular to a high-permeability and double-sided positioning printing method for all-cotton towel products, which comprises the following steps: pre-treating all-cotton towel gray cloth, including desizing, scouring and bleaching procedures; padding the treated gray cloth in slurry containing urea, sodium bicarbonate, anhydrous sodium sulphate and a penetrant, and then drying and sizing; printing on the front surface of the towel by adopting a digital printing machine, and spraying a penetrating agent with the surface tension of 25-35mN / m to a printing area through an independent jet printing channel, so as to promote the dye to fully permeate to the back surface of the fabric and form a clear pattern outline; according to the invention, an image acquisition system composed of an industrial camera, an annular LED coaxial light source and a polarization filter captures fabric back pattern contour features in real time, and generates a translation, rotation and zooming transformation matrix based on an image registration algorithm; in combination with closed-loop feedback control of the digital printing machine, the positioning precision is improved by more than 75% compared with a traditional method.

The invention provides a same-layer registration method for an MR cross section diagram and a total tumor pathological section based on 3D printing. The same-layer registration method comprises the steps of obtaining an MR image, extracting an interested volume, designing a 3D printing mold matched with a focus in shape and assisting registration of a pathological section and the MR image. MR image layering thickness information is embedded into mold design, the thickness of a mold section is consistent with the thickness of an MR scanning layer, and direction information is marked in a mold to ensure spatial alignment of a pathological section and an MR image. The manufactured pathological section is accurately matched with an MR cross section diagram through a non-rigid image registration algorithm, and layer-by-layer comparative analysis of a tumor image and pathology is achieved. The whole tumor area can be covered, and registration errors caused by needle biopsy limitation, tissue deformation and resolution difference in the prior art are overcome. The method is high in applicability and can be used for image-pathology conjoint analysis of various types of tumors, the registration precision is remarkably improved, the error is controlled within 0.1 mm, and important support is provided for tumor research and accurate diagnosis.

The invention discloses a cutting path planning method and system for ultrasonic knife equipment, and relates to the technical field of medical treatment. The invention discloses a cutting path planning system of ultrasonic knife equipment. The cutting path planning system comprises a cutting path planning module and a cutting path calibration module, according to the method, image recognition is carried out by inputting the cut image annotation model, the cut object feature map is obtained, and the improved space trajectory planning algorithm and the image registration algorithm are utilized, so that the initial cutting path can be efficiently predicted and optimized, and the accuracy of the cutting path is ensured; according to the method, the image of the simulated cutting object is registered, analysis is carried out based on the cutting error judgment model, potential cutting errors can be recognized before an operation, a cutting path is adjusted in advance, and therefore the influence caused by the errors is reduced. And the initial cutting path is updated by using the error judgment result, so that the path deviation caused by the error is effectively prevented.

The present invention discloses a deep learning SAR image registration algorithm based on a dual transformation network, which mainly solves the problems of difficult acquisition of matching image pairs, low matching accuracy, too high cost of manually labeled data sets and class imbalance in existing deep learning SAR registration methods. The implementation scheme is as follows: 1) Generate training and test data sets. 2) Set the number of categories of the classification head of the deep image classification network and construct two branches of the dual transformation network; 3) Train the two branches of the dual transformation network and perform preliminary matching using the trained dual transformation network, 4) Perform fine matching on the basis of the preliminary matching; 5) Take the point pairs jointly recognized by the two branches and update the point pairs according to the results of the fine matching to obtain the final matching result. The present invention is simple in obtaining image pairs and does not require manual annotation, the data set has balanced categories, and has high matching accuracy, and can be used for geological mineral resource detection and digital terrain model extraction.

The application provides a pinhole image splicing true value acquisition and splicing method based on a heterogeneous camera combination. The acquisition method first symmetrically installs two pinhole cameras on a support, and installs an fisheye camera between the two pinhole cameras. Based on a double spherical camera model, the three cameras are calibrated to obtain the internal parameters and external parameters of the three cameras. The left eye pinhole view, the right eye pinhole view and the middle eye fisheye view of the target scene are simultaneously acquired by using the three cameras. The middle eye fisheye view is subjected to equirectangular projection. According to the internal parameters and external parameters of the three cameras, the left eye pinhole view and the right eye pinhole view are projected to the equirectangular projection view plane respectively, multi-camera coordinate system one is completed, and the splicing true value is obtained. The application has the core advantages of the hardware structure design and the heterogeneous camera image registration algorithm, can stably acquire images without seams and time sequence consistency, and provides strong support for the true value reference of the precision verification and model training of subsequent panoramic video splicing.

Some embodiments of the present disclosure provide a method, device and computer readable medium for identifying an analog instrument, the method comprising: inputting a first instrument panel image to be tested into a first neural network model, and outputting the first instrument panel image; extracting feature points of the first instrument panel image, and performing feature point registration on the first instrument panel and a standard instrument panel based on an image registration algorithm, and outputting a second instrument panel image after registration; inputting the second instrument panel image into a second neural network model, and outputting pointer position information on the second instrument panel image; and calculating a reading of the analog instrument based on the pointer position information. Embodiments of the present disclosure achieve identification and reading of the analog instrument based on an improved neural network model.

This invention discloses a multimodal image real-time registration and fusion system and method for cardiac interventional surgery, comprising: a multimodal image acquisition module, an image data transmission module, an intraoperative twin image registration and analysis platform, an algorithm calculation module, a parameter adjustment module, and an image fusion output module. After acquiring image data, the multimodal image acquisition module sends it to the intraoperative twin image registration and analysis platform via the image data transmission module. The platform incorporates a cardiac deformation adaptive optical flow algorithm, a multi-resolution hierarchical mapping algorithm, and a large-deformation cardiac image registration algorithm. The algorithm calculation module performs registration calculations, and the parameter adjustment module optimizes the fusion parameters based on the calculation results. Finally, the image fusion output module generates and outputs the fused image. This invention achieves real-time and accurate multimodal image registration and fusion, providing reliable image support for cardiac interventional surgery and improving surgical accuracy and safety.

The invention relates to the field of image recognition, in particular to an in-transit transportation abnormity intelligent early warning system for an intelligent supply chain. The visual information acquisition module is used for acquiring land visual representation, vehicle motion time sequence signals, operation flow time sequence and intersection point visual representation; the morphological correlation model construction module is used for outputting seaport scene visual representation through semantic segmentation; outputting a sea-land intersection point form correlation model through an image registration algorithm; the event attribution module is used for adding the vehicle motion time sequence signal and the sea transportation environment data to the sea-land intersection point form correlation model to obtain sea-land visual time sequence representation; performing feature analysis to obtain attribution judgment; generating a transportation event attribution heat map through a feature attribution algorithm; and the risk judgment module is used for generating an early warning signal when the cause is judged to be damage and the visual activation degree is lower than a threshold value. According to the method, end-to-end multi-dimensional coupling from geometric modeling to space-time attribution to risk decision is carried out, so that the logicality and credibility during damage distinguishing are improved.

The visible light and near infrared registration system and registration method based on laser radar, which relates to the image processing of vehicle-mounted visual system and the field of computer vision technology, solves the problems of slow speed, poor real-time performance and low efficiency of existing multi-sensor image registration. It includes a visible light camera, a near infrared camera, a laser radar, an image registration module and a display module for displaying the spliced ​​image and the original image; the image registration module is provided with an input end, an output end and an image registration internal processor, the image registration internal processor receives the image information from the input end and uses the image registration algorithm for calculation, the near infrared camera output interface and the visible light camera output interface are respectively connected to the input end of the image registration module, the output port of the laser radar is connected to the input end of the image registration module; the image display module is connected to the output end of the image registration module, and is used to display the images before and after registration. The method of the present invention has fast processing speed and high efficiency, and is suitable for visual systems that require real-time processing.

The invention relates to the technical field of CBCT tooth segmentation and modeling, and discloses a CBCT tooth segmentation and modeling algorithm, which comprises the following steps: acquiring high-resolution 3D image data of an oral cavity of a patient by using CBCT equipment, and acquiring a fine structure of a tooth surface by using an optical scanner; using an image registration algorithm to align the image data from different sources, and using a fusion algorithm to merge the data of multiple modalities into a 3D model; selecting a deep convolutional neural network suitable for processing a 3D medical image, and optimizing a network structure for a tooth segmentation task; dividing the preprocessed and labeled data set into a training set, a verification set and a test set; according to the CBCT tooth segmentation and modeling algorithm, the mark points are placed in the oral cavity of a patient and the positions of the mark points are matched, so that accurate alignment of two kinds of modal data is realized, accurate matching of CBCT images and optical scanning data in space is ensured, reliable reference points are provided for subsequent image registration and fusion, and the accuracy of the CBCT tooth segmentation and modeling algorithm is improved. And the precision and reliability of data alignment are improved.

The invention relates to the field of medical image processing, and aims to provide a DSA image registration method fusing moving target features. Comprising the steps that a DSA image registration model fusing blood vessel information is established, and the model comprises a similarity measurement item, a regularization item of a displacement field and a regularization item of the blood vessel information; taking the DSA angiography image and the background image as the input of a model, and discretizing on a node grid; discretizing the model by using a difference operator to obtain an optimization model; decomposing and optimizing the model through an alternating direction algorithm, iteratively solving a displacement field and updating blood vessel information, and outputting a DSA subtraction image after a stop condition is met. The registration model provided by the invention can effectively cope with artifacts caused by rigid and non-rigid image deformation, more accurately estimate a displacement field, extract a vascular structure and improve the image registration accuracy; the alternating direction algorithm framework enables the solution of the displacement field to utilize the newest image registration algorithm, so that the optimization problem of blood vessel image updating has a closed-form solution, and the solution can be efficiently solved.

The invention relates to the technical field of industrial machine vision detection, in particular to an intelligent grading and feedback system based on image recognition. Comprising a multi-modal image acquisition unit used for synchronously acquiring a visible light image, an infrared thermal imaging image and a hyperspectral image of a target object; the dynamic self-adaptive preprocessing unit is in communication connection with the multi-modal image acquisition unit, performs combined preprocessing on the acquired multi-modal image, and comprises a dynamic noise reduction algorithm based on target edge features, a self-adaptive contrast enhancement algorithm and a multi-modal image registration algorithm; and the closed-loop feedback unit is respectively in communication connection with the multi-modal image acquisition unit and the multi-dimensional dynamic threshold grading unit. Through multi-modal image acquisition and attention-multi-scale feature fusion design, the limitation of traditional single-modal detection is effectively broken through, the recognition capability of micro bubbles and hidden cracks of the quartz crucible is remarkably improved, surface stains and internal impurities are accurately distinguished, and the problem of subjective difference of manual visual detection is solved.

The invention relates to the technical field of biological imaging, and discloses a macromolecular morphology restoration method based on microscopic imaging, which can achieve accurate restoration of macromolecular morphology more simply and conveniently. The method comprises the following steps: generating a grey-scale map according to atomic force microscopic imaging data, wherein the grey-scale map comprises a plurality of macromolecules; applying a migration model to the grey-scale map, performing image detection, extracting an image of macromolecular single particles, filtering defective particles, and retaining conventional particles; wherein the migration model is obtained based on a segmentation model by training data sets in different view ranges to perform multiple rounds of learning; and aligning the images of the conventional particles through an image registration algorithm and averaging to obtain a macromolecule morphology restored image.

A mosaic dislocation repair method and device based on a high-precision image registration algorithm and a medium. It belongs to the technical field of satellite remote sensing, and specifically relates to the technical field of image mosaic dislocation repair. The method comprises the following steps: S1, inputting the dislocated area in the mosaic process in the form of vector data, determining the area to be repaired, and obtaining the local area image to be registered; S2, performing feature point matching on the local area image to be registered; S3, adding fixed points to the buffer area outside the area to be repaired; S4, transforming the local area image to be registered through a thin plate spline function to obtain a final registration image; and S5, replacing the final registration image into the original image for re-mosaic. The method is suitable for dislocation repair requirements in different scenes, significantly reduces the manual repair cost while improving the image product quality, and has important practical value.