19results about How to "Improve registration" patented technology

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 belongs to the technical field of medical image processing, and relates to a medical image registration method based on multi-scale deformation decomposition. Firstly, a multi-scale feature extraction network is constructed, the method focuses on extracting features of a mobile medical image, and the features are transmitted to a hierarchical feature fusion module to be processed; secondly, the hierarchical feature fusion network is used for establishing a mapping relation between the mobile medical image and the fixed medical image; in a feature decoding stage, a mobile decoding feature network and a fusion decoding feature network are respectively constructed, multi-scale decoding features are utilized to output a multi-scale deformation field, and a coarse-to-fine registration strategy is gradually realized by fusing the deformation fields of adjacent stages layer by layer. By designing the double-branch network structure and connecting the gating updating modules in the adjacent stages, the complex deformation processing capability of the registration network is enhanced, so that the final registration effect is improved.

The invention relates to the technical field of image processing, in particular to a remote sensing image registration method based on convolution attention, which comprises the following steps: inputting an original remote sensing image pair into a convolution layer of a modal adapter to obtain original features, and processing the original features by adopting a convolution attention method to obtain target features; inputting the target feature as a modal adaptive feature into a feature encoder to obtain a feature descriptor; inputting the feature descriptors into a feature detector, and estimating global detection feature points by using conditional probability; and performing preliminary matching on the feature descriptors and the feature points by using an FLANN algorithm, and performing outer point elimination in combination with an RANSAC algorithm to obtain a registration result. According to the method, richer feature representation can be provided for remote sensing images of sparse and repeated texture areas, and the registration effect is improved.

The invention discloses a multi-modal image registration method and system based on feature decoupling and computer equipment, and the method comprises the following steps: 1, obtaining and processing a plurality of multi-modal images, and obtaining a multi-modal image registration data set with diversity; 2, constructing a multi-modal image registration neural network model based on feature decoupling; 3, designing a loss function to supervise training of a multi-modal image registration neural network model, and optimizing a registration process; and 4, processing a to-be-registered multi-modal image by using the trained multi-modal image registration neural network model to obtain an image conversion parameter matrix, and converting the to-be-registered image in the image pair according to the image conversion parameter matrix to obtain a registered image. According to the multi-modal image registration method based on feature decoupling, the performance of multi-modal image registration can be effectively improved, and particularly, good robustness is shown under the condition of large difference between different modals.

The invention discloses a medical image registration method based on image segmentation and a convolutional neural network, and relates to the field of medical image registration, and the method specifically comprises the steps: obtaining image data, carrying out the segmentation and preprocessing based on the medical image data, constructing a multi-dimensional space-time memory network based on the segmented and preprocessed data obtained in S100, and carrying out the segmentation and preprocessing based on the multi-dimensional space-time memory network. S200, training and optimizing the multi-dimensional space-time memory network, performing weight adjustment and optimization based on the multi-dimensional space-time memory network obtained in the S200, constructing and optimizing a deformation model based on a result output by the multi-dimensional space-time memory network after weight adjustment and optimization in the S300, and performing adjustment and continuous optimization based on a result output by the deformation model in the S400. By constructing a multi-dimensional space-time memory network and combining spatial features extracted by a convolutional neural network and a dynamic deformation model, the precision of dynamic image registration is improved, and through an adaptive weight adjustment mechanism, registration weights among frames in an image sequence are dynamically adjusted, so that the continuity of space-time embedded features and the precision of a registration result are ensured.

The invention belongs to the technical field of ecological monitoring, and discloses a forest ULS-TLS point cloud automatic registration method based on ground context description, and the method employs a ground point as a registration element, constructs a ground context, can directly achieve the registration of TLS single scanning data to a ULS point cloud, and does not need the pre-registration of different scanning point clouds of a TLS. The extraction process of the ground points is relatively standardized, stable and reliable results can be obtained generally, and the method is not affected by distribution, density and shape complexity of trees in the forest. Compared with extraction of tree attributes, the ground points are used as registration primitives, so that the calculation cost is reduced, and high calculation overhead caused by extraction of the tree attributes is avoided. The invention aims to provide a forest ULS-TLS point cloud automatic registration method based on ground context description so as to quickly, accurately and automatically realize registration of forest ULS and TLS point clouds, comprehensively obtain forest structure information and promote application of a laser radar technology in forest investigation.

The invention discloses a multi-modal image common visible region extraction method, system and device and a medium, and relates to the technical field of image processing. Comprising the following steps: performing scale transformation processing of different magnification times on an original image pair for multiple times to obtain a plurality of initial processing image pairs; carrying out key point matching processing on the original image pair to obtain a first matched key point pair and a second matched key point pair; respectively carrying out key point matching processing on the original image pair and the initial processing image pair to obtain a third matched key point pair and a fourth matched key point pair; combining and splicing the first matching key point pair and the third matching key point pair to obtain a first matching point pair; projecting the second matching key point pair and the fourth matching key point pair to the position of the original image pair, and then combining and splicing to obtain a second matching point pair; removing abnormal key points of the two matching point pairs to obtain final matching key points; and determining a common visible area. According to the invention, the matching precision of the common visible region of the multi-modal image can be improved, and a better registration effect is realized.

The embodiments of the present invention relate to an image registration method, device, electronic device and storage medium, and relate to the field of image processing technology. The method acquires a moving image to be registered and a fixed image to be registered; inputs the moving image to be registered and the fixed image to be registered into a pre-trained first registration model to obtain a deformation field of a region of interest; applies the deformation field of the region of interest to the moving image to be registered to obtain a deformed image of the region of interest; and registers the deformed image of the region of interest with the fixed image to be registered to obtain a registered image; that is, the embodiment of the present invention inputs the moving image to be registered and the fixed image into a model specifically used for region of interest registration for registration, thereby reducing the interference of non-regions of interest in its registration process and improving the image registration effect.

The invention discloses a two-stage different-source remote sensing image registration method with radiation and rotation invariance. The method comprises the following steps: performing WLD filtering on a to-be-registered image group to obtain a WLD structure saliency map and a WLD directional diagram sequence of the to-be-registered image group; constructing a feature description vector of the to-be-registered image group based on the WLD structure saliency map and the WLD directional diagram sequence; matching feature points among the to-be-registered image groups according to the feature description vectors to obtain rotation reference point pairs; extracting WLD structural feature description vectors of the image groups to be registered according to the rotating reference point pairs, and matching feature points among the image groups to be registered again according to the WLD structural feature description vectors to obtain multiple pairs of final matching point pairs; and registering the to-be-registered image group according to the final matching point pair. According to the invention, the registration precision and effect can be enhanced.

This application discloses a method, circuit, device, electronic device, and storage medium for detecting image corner points, belonging to the field of electronic device technology. The image corner point detection method includes: processing the image to be detected to obtain N feature values ​​corresponding to N first pixels in the image, where N is a positive integer; processing the N feature values ​​to obtain distribution information corresponding to the N feature values; determining a corner point threshold based on the distribution information and the number of targets, where the number of targets is the number of target corner points; and determining the target corner points among the N first pixels based on the corner point threshold.

This disclosure relates to a method and model for determining the homography of multimodal images, belonging to the field of image registration technology. The method involves: embedding an initial noisy image into a diffusion denoising process to generate an initial intermediate image; using a cross-modal reference image as guidance, progressively denoising the initial intermediate image based on the diffusion denoising process to generate intermediate images at multiple intermediate steps; and performing complete denoising on the intermediate image at the current time step to generate a noise-free approximate image at the current time step; predicting the increment matrix at the current time step based on the cross-modal reference image and the noise-free approximate image at the current time step; selecting the increment matrix with the smallest registration error from the increment matrices at multiple time steps as the registration increment matrix, and determining the homography matrix of the time step corresponding to the registration increment matrix as the registration homography matrix. The above method for determining the registration homography of multimodal images is used to determine the registration homography matrix.

The present invention provides an image processing method, device and electronic system, which obtains a plurality of images to be processed whose exposure corresponding to a target scene is lower than a preset exposure threshold and whose exposure is the same; performs image registration processing on the plurality of images to be processed; and performs image fusion processing on the plurality of images to be processed after image registration to obtain an image processing result. In this method, in a scene with insufficient lighting such as at night, a plurality of images to be processed whose exposure is lower than a preset exposure threshold and whose exposure is the same are captured, and the plurality of images to be processed are processed by image registration and image fusion. This method can keep the image brightness of the plurality of images to be processed consistent, which is conducive to improving the image registration effect, avoiding ghosting of the fused image, and thus improving the overall image effect.

The embodiment of the invention provides a monitoring registration method and system for a Beidou remote sensing image of a power grid system and a storage medium, and belongs to the technical field of power grid remote sensing image processing. The monitoring registration method comprises the following steps: acquiring a to-be-registered image and a reference image of a Beidou remote sensing image in a power grid system; obtaining feature points of the to-be-registered image and the reference image and feature vectors of the feature points; according to the method, the feature points and the corresponding feature vectors of the to-be-registered image and the reference image are extracted, then the feature points are optimized to obtain the initial matching point pair, the initial registration of the to-be-registered image and the reference image can be realized according to the initial matching point pair, and the initial registration image is obtained. And finally, carrying out further registration on the preliminary registration image to obtain a final registration image. And a dual registration mode of preliminary registration and re-registration is adopted, so that the capability of capturing image features and details is enhanced, and the registration precision and effect are effectively improved.

This application relates to an image subtraction method, apparatus, and storage medium. The method includes: registering a current frame's overlay image and a previous frame's target mask image to determine the current frame's target mask image; and determining the current frame's subtraction image based on the current frame's target mask image and the current frame's overlay image. Through this application, the current frame utilizes the previous frame's target mask image during the registration process, transferring the registration trend of the previous frame to the current frame. This is equivalent to the current frame undergoing several registrations, thereby reducing the computational load during image registration and improving the registration effect.

The invention provides a point cloud noisy point determination method and device and a storage medium, and the method comprises the steps: determining adjacent point data adjacent to each point data in a point cloud based on the device parameters of a collection device and the three-dimensional coordinates included in each point data, and determining the point cloud noisy point in the point cloud based on the distance. According to the scheme provided by the embodiment of the invention, through the determination of the adjacent point data, the disordered and semantic-free point data in the point cloud is changed into the point data with the relative position relationship, so that the determination of the point cloud noisy points based on the distance becomes possible. Therefore, by adopting the scheme of the embodiment, unstable noisy points such as leaves can be removed at a relatively high accurate removal rate, the point cloud registration effect is effectively improved, map ghosting is eliminated, and the map quality is improved. In addition, the scheme of the embodiment does not need a large number of computing resources and data training, real-time processing can be achieved, and high practicability and feasibility are achieved.

The application discloses a kind of based on deformation adaptation multi-modal image registration method, system and computer equipment, comprising: acquisition multiple multi-modal images and carries out gray standardization, constructs diversified registration dataset;Build registration network model including pyramid coding module, deformation self-adapting module, cross-modal interaction module and registration parameter estimation module;Image pair is sequentially input into the above module, respectively extract basic feature mapping, deformation feature mapping and interactive enhanced feature mapping, finally output estimated conversion parameter matrix;Through preset loss function supervision training process, select optimal network parameter, obtain trained registration model;When practical application, the image pair to be registered is input into trained model, obtains conversion parameter matrix and completes image registration.Can effectively improve multi-modal image registration performance, especially in the case of severe geometric distortion and significant modal difference, still have good robustness and adaptability.

The present invention discloses a DRR image generation system and generation method based on dynamic step length, the system comprises an image preprocessing module, a dynamic step length calculation module and a ray projection calculation module, wherein the image preprocessing module is used to establish a preprocessing three-dimensional voxel model based on the three-dimensional voxel data of CT images and the relationship between the CT values ​​between voxels; the dynamic step length calculation module is used to calculate the segmented sampling step length of the ray according to the tissue to which the voxel of the preprocessing three-dimensional voxel model through which the projection line passes belongs or the relative relationship with the tissue to which the surrounding voxels belong; the ray projection calculation module is used to calculate the projection value of the ray on all pixels of the flat receiving point. The present invention ensures the calculation accuracy of DRR image generation and speeds up the calculation time.

The invention discloses an optical and SAR image registration method and system based on multi-scale phase antisymmetry. The method comprises the following steps: acquiring an optical and corresponding SAR image pair as an original image pair; calculating the phase antisymmetry of each scale by using a multi-scale single gene signal to obtain the multi-scale phase antisymmetry of the image, and performing feature point detection on the original image to obtain the feature point of each original image; constructing a multi-scale phase antisymmetry directional diagram by using the phase antisymmetry of each scale, and generating descriptors of the feature points; similarity measurement of descriptors between original images is utilized to carry out approximate matching on feature points of image pairs to obtain matched feature point pairs, and mismatched feature point pairs are eliminated through an FSC method to realize accurate registration of the images; and transforming the image by using a transformation matrix obtained by FSC screening to obtain a registered optical and SAR image pair. According to the method, the registration effect of low-quality optical and SAR images can be effectively improved, and rapid and robust registration is realized.

The present invention relates to the field of image processing technology, and in particular to a remote sensing image registration method based on convolutional attention, comprising: inputting the original remote sensing image pair into the convolution layer of a modality adapter to obtain the original features, using the convolutional attention method to process the original features to obtain the target features; inputting the target features into a feature encoder as modality adaptation features to obtain feature descriptors; inputting the feature descriptors into a feature detector to estimate global feature points using conditional probability; performing preliminary matching of the feature descriptors and the feature points using the FLANN algorithm, and removing external points in combination with the RANSAC algorithm to obtain the registration results. The present invention can provide richer feature representations for remote sensing images in sparse and repeated texture areas, and improve the registration effect.