646 results about "Spatial transformation" patented technology

The invention provides an improved YOLOv11s safety helmet wearing detection model and an optimization method thereof, and relates to the technical field of computer vision target detection. According to the improved YOLOv11s safety helmet wearing detection model and the optimization method thereof, the improved YOLOv11s safety helmet wearing detection model comprises the following modules: a multi-modal fusion module, a space-time analysis module, a domain adaptation module, a topological optimization module and a dynamic architecture module; and the multi-modal fusion module is used for realizing feature decoupling by adopting channel separation convolution based on input RGB and near-infrared images, fusing visible light and thermal radiation features through a dynamic weight distribution algorithm, implementing affine transformation alignment on multi-scale features by utilizing a spatial transformation network, and generating a multi-modal feature graph. Through fusion of visible light and near infrared spectrum features and implementation of dynamic weight distribution, complementarity of target texture and thermal radiation features under a complex illumination condition is enhanced, and the problem of feature distortion of single-mode data in a strong backlight or low-illumination scene is solved.

The embodiment of the invention provides an end-to-end automatic driving control method and device based on multi-camera fusion, and multi-view target detection and tracking are realized through spatial transformation and coordinate mapping by combining front wide-angle camera information and left and right wide-angle camera information. A multi-view feature fusion network architecture is designed, the multi-view feature fusion network architecture comprises three sub-networks of feature extraction, dynamic weight distribution and feature fusion, and the fusion weight is dynamically adjusted based on image definition, detection confidence and view overlapping degree. A geometric consistency constraint between visual angles and a reconstruction loss function are introduced, a deep neural network model is constructed, abnormal conditions such as camera shielding are effectively handled, and an accurate control instruction is output. According to the method, the defects of the traditional technology in the aspects of multi-view information fusion, shielding processing and the like are overcome, and the sensing ability and the control reliability of the automatic driving system are remarkably improved.

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 discloses an industrial surface defect detection method based on multi-scale feature fusion, and the method comprises the steps: collecting the multi-source data of a detected surface in real time through a multi-modal sensor array, and forming a structured data set through time-space synchronization and denoising; constructing an adaptive geometric correction model to realize spatial transformation and scale normalization of multi-scale features, and cooperatively realizing cross-modal alignment and preliminary fusion through texture and physical attribute branches of a double-flow decoding network; dynamically reweighting the fusion features based on a defect physical model, strengthening physical mechanism defect characterization and suppressing interference; combining optical flow compensation and three-dimensional convolution to extract spatio-temporal evolution characteristics, and forming dynamic defect characterization; and finally outputting defect type and severity evaluation through the classification model in combination with the process parameter library. Therefore, the adaptability of the method to a complex industrial environment is enhanced, and the detection stability can be maintained under different materials, illumination conditions and dynamic interference.

The invention discloses a scene topology understanding method and device, a storage medium and a program product, and relates to the field of computer systems based on a specific calculation model, and the method comprises the steps: inputting a multi-view environment image set into a backbone network, and generating bird's-eye view features corresponding to the environment image set; calculating a spatial transformation matrix of the aerial view features at the current moment and the aerial view features of the previous K frames, and performing space-time alignment on the obtained K + 1 frames of aerial view features to obtain multi-frame fusion features; inputting the multi-frame fusion features into a map prior model to obtain aerial view correction features; decoding the aerial view correction features based on a topological decoder, and generating a lane topological graph; comparing the lane topological graph with the annotation data, calculating an error loss function, and optimizing network parameters based on the error loss function; and generating an optimized topological graph, and determining a scene topology result based on the optimized topological graph. By implementing the method, the environment topology understanding capability in a complex scene can be improved, and the generation precision of the lane topological graph is optimized.

The invention relates to the field of image processing, and particularly discloses a welded pipe surface defect detection method based on robot visual inspection. The method comprises the steps that a robot carries a binocular camera and an annular LED light source and moves at a constant speed in the axial direction of a welded pipe to collect orthographic and inclined views, and a three-dimensional point cloud is constructed; and establishing a parameterized mapping function based on the point cloud, and converting the 3D coordinate into a 2D expansion surface coordinate. In the convolutional neural network, a first layer is inserted into a spatial transformation network to correct distortion of the expanded image, deformable convolution is adopted to extract edge, local deformation and specific defect response features, and standard convolution is combined to extract global features; and fusing multi-scale features and adding an attention mechanism to improve the weight of a defect region, and outputting a defect category and a bounding box offset after generating a candidate box. The method effectively solves the problems of stretching, deformation and defect distortion of welded pipe curved surface imaging, reduces the imaging difference of the same defect, and remarkably improves the defect positioning precision and recognition accuracy.

The invention discloses an unmanned aerial vehicle aerial photography-based unbounded scene three-dimensional reconstruction method, and relates to the technical field of image processing. According to the technical scheme, the method comprises the following steps: obtaining a multi-view image set of a target area, and constructing a spatial topology association network; image features are extracted based on a Hessian matrix and an SIFT operator, and scene partitions are generated through Euclidean distance matching; 3D Gaussian sputtering optimization is carried out on each partition, reusable 3D basic assets are synchronously generated, and the basic assets comprise structural components and texture features; based on a spatial transformation rule defined by a programmed code, carrying out dynamic combination on the basic assets to generate a hierarchical three-dimensional model; performing geometric consistency verification in a potential space through joint optimization of a diffusion model and a differentiable renderer; and assembling the optimized hierarchical model into the unbounded three-dimensional scene according to the spatial layout parameters. The method aims at solving the problems that three-dimensional modeling is high in resource consumption and insufficient in geometric accuracy in a large-scale scene.

The invention provides a multi-view-oriented three-dimensional scene image reconstruction registration and optimization method and system, and relates to the technical field of image processing, and the method comprises the steps: obtaining multi-frame three-dimensional scene image data, extracting a multi-level feature set, constructing a cross-view-angle semantic association graph, building a feature corresponding relation, and calculating a multi-view-angle spatial transformation relation parameter. Performing coordinate system alignment on the image data to generate an initial three-dimensional reconstruction result, and performing optimization in combination with a multi-target joint optimization function and a dynamic adaptive weight regulation and control mechanism. According to the method, the precision and robustness of three-dimensional scene reconstruction are improved, and the problem of registration errors caused by large view angle difference in a complex scene is solved.

The invention relates to the technical field of surface defect recognition, and discloses an intelligent recognition method and system for surface defects of an autoclaved aerated concrete member. The method comprises the following steps: carrying out omnibearing shooting and spatial transformation matrix mapping on the surface of the autoclaved aerated concrete member to obtain a preprocessed image; performing texture feature extraction on the preprocessed image to obtain texture feature data; performing spatial feature analysis on the preprocessed image and the texture feature data to obtain spatial relation feature data; performing feature importance weighting and bidirectional cross fusion processing on the texture feature data and the spatial relation feature data to obtain comprehensive defect feature representation; and performing surface defect analysis on the autoclaved aerated concrete member based on the comprehensive defect feature representation, and outputting a surface quality comprehensive score and grade division result. According to the method, the problems of dead angles and image deformation existing in traditional single-view-angle detection are solved, and the completeness and accuracy of defect detection are ensured.

The invention discloses an image classification method for cross-domain transfer learning and related equipment, and relates to the technical field of image classification, and the method comprises the steps: obtaining a source domain image set and a target domain image set; extracting a first multi-level semantic feature of the source domain image set and a second multi-level semantic feature of the target domain image set based on a heterogeneous feature extraction network; generating a cross-domain migration weight matrix through a dynamic domain similarity measurement module based on the first multi-level semantic features and the second multi-level semantic features; based on the deformable feature pyramid, performing spatial transformation on the first multi-level semantic features to generate a migration feature map; based on the cross-domain migration weight matrix, channel recombination is carried out on the migration feature map, and target domain adaptive feature representation is constructed; and based on the target domain adaptive feature representation, outputting a classification result of the target domain image through a target domain classifier. According to the method, the feature matching precision and the classification robustness in a cross-domain scene are improved.

The invention relates to a multi-modal image registration method based on modal alignment distillation, and belongs to the technical field of image processing. The method comprises the steps of adopting a distillation learning guide type training process, performing guide type supervision on a multi-modal registration student model by using intermediate features of a same-modal registration teacher model with excellent performance and an output deformation field, migrating modals of the intermediate features of the student model, and effectively reducing modal differences, a high-quality deformation field close to teacher model estimation is output, and the multi-modal registration performance is improved; a deformable mask training strategy is adopted to deal with the problem that a registration result is inconsistent with a label image boundary caused by a black edge effect of spatial transformation in a registration process (namely, a black edge of the registration result and an original boundary of the label image), so that the negative influence on registration is effectively reduced, and the registration performance of the model is improved. According to the invention, a multi-modal image registration task can be realized, registration challenges caused by modal differences are effectively solved, and the effect of multi-modal image space alignment is achieved.

The invention provides a knowledge graph dynamic updating method and device based on a crop growth process, and the method comprises the steps: inputting environment factor data and a crop growth stage into a trained Transform model, and obtaining a time sequence feature; performing relation graph construction based on the environmental factor data and the crop growth indexes through a graph convolutional network to obtain an environmental crop growth relation graph; performing feature extraction based on the environment crop growth relation graph through a graph attention network to obtain a spatial feature matrix; performing feature extraction based on the spatial feature matrix through a spatial graph convolutional network to obtain high-dimensional spatial features; performing alignment and conversion based on the high-dimensional spatial features through a spatial transformation network to obtain geographic spatial features; performing feature fusion based on the time sequence features and the geographic space features through a deep fusion model to obtain comprehensive features; and performing incremental updating on a preset knowledge graph based on the environmental factor data, the crop growth indexes and the comprehensive characteristics.

The invention provides an infrared and visible light image end-to-end registration method based on phase consistency enhancement, and belongs to the technical field of electric digital data processing. The invention discloses an infrared and visible light image end-to-end registration method based on phase consistency enhancement, which comprises the following steps of: firstly, preprocessing infrared and visible light source images in an image database, and dividing the infrared and visible light source images into a training set and a test set; and constructing a feature extractor based on a phase consistency attention mask, realizing cross-modal consistency feature extraction, and focusing an important space region by using phase information. A self-defined ResSobeNet network is adopted for parameter estimation, and Haar wavelet transform is used for replacing pooling operation, so that information loss in a traditional down-sampling method is avoided. A double-branch generator structure is designed, affine parameters and an optical flow field are generated respectively, meanwhile, constraint on rigid registration and non-rigid registration is achieved, and finally a registration result is obtained through resampling of a space conversion module. According to the method, the problems that cross-modal consistent features are difficult to extract in the infrared and visible light image registration process and explicit registration steps are complicated and difficult to generalize are solved, and the method has good universality and practicability.

The invention provides a three-dimensional modeling method and device based on visual fusion, a terminal and a storage medium, and relates to the technical field of computer vision and three-dimensional modeling. The method comprises the steps that multi-angle shooting is carried out on a target object through a multi-binocular-camera system after internal and external parameter calibration, a disparity map, shot by each binocular camera, of the target object is obtained, the multi-binocular-camera system comprises multiple binocular cameras, and each binocular camera comprises a left camera and a right camera; fusing each disparity map by using a spatial transformation fusion algorithm to obtain a complete disparity map of the target object; and calculating point cloud data by using the complete disparity map of the target object, and constructing a visual three-dimensional model of the target object based on the point cloud data. According to the method, not only can the integrity and accuracy of the visual three-dimensional model in a complex scene and an object structure be greatly improved, but also the modeling efficiency can be improved.

The invention relates to a teacher behavior recognition method based on a cross-modal dynamic query network, and belongs to the field of artificial intelligence education application. The problem that in the prior art, adaptation to education scene dynamic interactivity, semantic complexity and environment interference is insufficient is solved. The method comprises the following steps: extracting video spatio-temporal characteristics through a time-space transformation network, and generating a semantic prototype by combining a comparison language image pre-training model; a multi-region attention module is adopted to realize hierarchical feature fusion, and semantic related visual clues are screened; generating a self-adaptive query vector driving classification decision by using a dynamic query decoding module; and outputting a behavior recognition result based on a cross attention mechanism. The method has the technical effects that the characterization distinction degree of complex teaching behaviors is remarkably improved, the robustness of a dynamic classroom environment is enhanced, the reliability of multi-class behavior decisions is optimized, deep education semantic understanding is realized, and the high-efficiency and low-consumption landing application requirements are met.

The invention relates to the technical field of industrial intelligent manufacturing, in particular to a flexible production line appearance defect optical detection method. Comprising the steps of performing image acquisition on semi-finished products and finished products on a production line; carrying out preprocessing and spatial transformation on the collected image; performing feature extraction and clustering analysis on the positive sample image of the to-be-detected product by using unsupervised learning to form a feature library; comparing the feature vector of the to-be-detected image with a feature library, and outputting the defect position and type of the sample; an improved RTDETR model with supervised learning is adopted, a sample judged as a defect is used for training, and the improved RTDETR model adopts a ResNet and Visual Transform network structure; and performing real-time detection and result feedback on the deployment model. According to the technical scheme, the appearance defect detection efficiency and the product quality of products manufactured on the flexible production line can be improved.

The invention discloses a GNSS interference identification method based on spatial transformation, multi-scale feature fusion and KAN. The method comprises the steps of forming an interference signal data set; obtaining a time-frequency image training set and a time-frequency image test set; constructing a GNSS interference identification classifier composed of a spatial transformation module, a multi-scale feature fusion module and a KAN module; a trained GNSS interference identification classifier is obtained; and obtaining a final GNSS interference identification classifier. And finally, outputting the category of the GNSS interference signal acquired in real time by the GNSS interference identification classifier. According to the invention, the spatial transformation module is used to carry out translation, scaling and other operations on the interference signal time-frequency image, and the number of training samples is increased. And the multi-scale feature fusion module adopts parallel convolution kernels of different scales to extract multi-scale spatial features of the time-frequency graph, so that the richness of feature representation is increased, and the recognition accuracy is further improved. The KAN module uses a KAN to replace a traditional multi-layer perceptron to execute a final classification task, and a more complex nonlinear function can be fitted by using fewer parameters.

The invention discloses a tumor space-occupying brain network neural image alignment method based on multi-modal fusion, and belongs to the technical field of medical image processing and artificial intelligence crossing. The method comprises the following core steps of multi-modal image heterogeneous feature decoupling, tumor occupation deformation field modeling, functional network topological structure maintenance, cross-modal feature adversarial alignment, dynamic deformation constraint optimization and clinical interpretability verification, and construction of a three-dimensional non-rigid registration network based on a double attention mechanism. And differential homeomorphic mapping of a tumor focus area and normal brain tissue is realized through the cascaded spatial transformation module. Aiming at the problems of insufficient multi-modal feature alignment and brain network topology distortion in the prior art, the invention provides a function connection constrained cross-modal fusion strategy, a graph convolution network is adopted to encode resting state function connection features, and network node displacement caused by tumor occupation is dynamically corrected in combination with deformable convolution and a bidirectional feature competition mechanism; a space consistency loss function based on white matter fiber bundle tracing is designed, and through diffusion tensor imaging feature guide structure-function bimodal joint optimization, the problems of insufficient registration precision in a focus area and whole brain network connection distortion of a traditional method are solved. Experiments show that the registration precision of the method in glioma cases reaches 0.82 mm and is improved by 37% compared with that of a traditional method, and dissection-function consistency of functional network reconstruction around tumors is remarkably improved.

The invention discloses a deep learning-based multimodal image fusion method for soft tissue photoacoustic / ultrasound imaging. Steps: an ultrasound-photoacoustic imaging device acquires photoacoustic and ultrasound images of human soft tissue and performs size normalization processing; an input spatial transformation module converts the images to the YCbCr space; an input pre-convolution module modifies the number of data channels; an input multi-scale feature extraction module extracts salient features from the source images; an input filter prediction module derives multi-scale filters; and an input filter fusion and adaptive enhancement module combines the input source images to obtain the final fused result. The invention has superior fusion performance compared to several traditional fusion methods and deep learning-based fusion methods, and more importantly, it exhibits excellent real-time performance. Furthermore, various modes of photoacoustic / ultrasound fusion extension experiments have verified the effectiveness of the method proposed in the invention.

The invention relates to a light-weight low-light image enhancement method based on illumination iterative adjustment, and the method comprises the steps: carrying out the HVI color space transformation of a to-be-processed image, obtaining an HVI image, separating an illumination intensity component, obtaining an illumination intensity feature map, and carrying out the convolution processing of the HVI image, and obtaining a shallow feature map; feature fusion and brightness adjustment are carried out on the illumination intensity feature map and the shallow layer feature map to obtain a depth feature map, up-sampling is carried out step by step through two layers of decoders to recover the size, channel adjustment is carried out on each layer of decoder by adopting convolution of a specified size, and up-sampling is realized through bilinear interpolation; a Mama-based structure refining module is introduced into the tail end of each layer of decoder to improve the structure restoration capability, the number of channels of HVI features output by the last layer of decoder is adjusted to a specified value through a convolution of a specified size, residual connection is carried out on the HVI features and an initial HVI image, an enhanced HVI image is obtained, and a restored image is obtained through inverse HVI transformation. Adaptive enhancement is carried out under various low light conditions, multi-directional spatial context aggregation is realized, and texture representation is improved.

The invention discloses an adversarial sample generation method based on a partially perceptible patch, and the method enables a specific region of a target image to be visually close to the characteristics of a natural image through the optimization disturbance of the specific region of the target image, maximizes the interference to a target detection model, and improves the attack effectiveness while maintaining the concealment. The invention further provides a device of the adversarial sample generation method based on the partially perceptible patch. According to the method, color space transformation and random deformation are combined, the adaptability of the adversarial sample in different environments is further enhanced, and the method has higher robustness and universality in practical application.

The invention belongs to the technical field of medical image digital image processing, and particularly relates to a prostate TRUS-MRI registration method based on multi-scale weighting and an adaptive window, and the method comprises the steps: firstly collecting an ultrasonic image and a magnetic resonance image of a prostate, dividing the ultrasonic image and the magnetic resonance image into a training set, a verification set and a test set, and sketching a mask for a prostate gland in each image; then the input image is preprocessed; then, UNet is adopted as a feature extractor, a deformation field is output in combination with a spatial transformation network, deformation is applied through an STN pair, and a registration image is obtained; the method comprises the following steps: firstly, acquiring three parts of an ultrasonic image, then constructing a network loss function, finally, acquiring a total loss function according to the three parts of the network loss function, performing staged training, finally, outputting the total network loss function, processing a subsequent ultrasonic image and a magnetic resonance image, and outputting a registration image. Compared with the prior art, the method has the comprehensive advantages of cross-modal adaptability, detail optimization capability, calculation efficiency and generalization performance, so that the method is remarkably improved.

The invention belongs to the technical field of steel reinforcement framework three-dimensional reconstruction, and particularly discloses an automatic high-precision three-dimensional dense reconstruction method for a box girder steel reinforcement framework, which comprises the following steps of: dividing a scanning area into a plurality of sub-areas according to a depth image by moving an inspection robot along the steel reinforcement framework, and performing multi-angle local scanning in each sub-area; constructing a local point cloud model by combining camera poses during acquisition, further calculating a relative spatial transformation relationship by using an overlapping region between adjacent local models, constructing a pose map and implementing global optimization, and uniformly adjusting the poses of the local models in a world coordinate system; and finally, all corrected local point clouds are fused to generate a complete global three-dimensional model, so that collaborative reconstruction operation of domain-divided scanning, segmentation reconstruction and global optimization is realized, frame-by-frame accumulation of errors in traditional scanning-while-splicing reconstruction is effectively blocked, long-distance drift is remarkably inhibited, and the precision of three-dimensional reconstruction of the reinforcement cage is greatly improved.

The invention relates to the technical field of lock manufacturing machine tools, in particular to an error coupling compensation and positioning precision control method for a lock manufacturing machine tool. The method comprises the following steps: acquiring the structure and operation data of a lock making machine tool through a sensor, detecting the operation condition of the lock making machine tool and detecting the spatial transformation of each motion unit of the lock making machine tool, and generating the spatial transformation data of each motion unit; according to the spatial transformation data of each motion unit, error coupling positioning of a workpiece and a locking piece of the locking machine tool and operation coupling error analysis of each component of the locking machine tool are carried out, and operation coupling error data of each component are generated; setting precision compensation control parameters of the locking machine tool according to the operation coupling error data of each component, and generating precision compensation control parameter data; and real-time precision compensation and machining track correction of the locking machine tool are carried out on the operation error coupling data of all the components based on the precision compensation control parameter data. Error coupling positioning and precision compensation control of the lock manufacturing machine tool are achieved.

The invention belongs to the field of instrument identification, and particularly relates to a pressure gauge identification method and equipment in an industrial valve pressure testing process. The method comprises the following steps: extracting an instrument image from a collected video stream, and carrying out distortion correction and illumination normalization processing; feature extraction is carried out on the processed image, instrument types are identified based on a training model of multiple types of instruments, and an instrument image with angle deformation is corrected through a spatial transformation network; performing spatial sorting on the identified instrument images according to the coordinates of the central point of the detection frame, and generating a data array containing instrument information; and finally, performing enhancement processing on a single instrument image in the data array, positioning a dial plate area, performing perspective correction, determining geometric parameters of the dial plate, calculating an original reading based on the parameters, and outputting a final reading in combination with inclination compensation. Through the image processing and recognition steps, accurate recognition and reading of the pressure gauge in the industrial valve pressure testing process are achieved, and the recognition efficiency and accuracy are improved.

The invention discloses a photovoltaic module hyperspectral image registration splicing method, system and device. The method comprises the following steps: acquiring a hyperspectral image of a to-be-detected region and a high-resolution image of the same region; the acquired hyperspectral image and the high-resolution image are preprocessed; performing feature extraction and descriptor generation; preliminarily matching the feature points extracted from the hyperspectral image and the high-resolution image; performing accurate matching on the preliminarily matched feature points, calculating a spatial transformation model from the hyperspectral image to the high-resolution image, and performing registration on the hyperspectral image by using the transformation model obtained by calculation; and carrying out sequence splicing and fusion on the plurality of hyperspectral images and the registered image. According to the invention, the high-resolution image-assisted photovoltaic module hyperspectral image registration splicing reduces mismatching, reduces the number of times of repeated calculation and adjustment of an algorithm, reduces the consumption of calculation resources, and efficiently completes the registration splicing task under the condition of not increasing the hardware cost.

The invention discloses a multi-modal remote sensing image registration method based on domain self-adaption and an optimizer set algorithm, and relates to the field of image processing, and the method comprises the steps: respectively extracting the feature vectors of a reference image and an image to be registered, carrying out the feature matching of the feature vectors according to the class label containing conditions of the feature vectors, and obtaining a registration result; obtaining a feature matching vector based on a matching result; estimating transformation parameters corresponding to the affine transformation model by using a random sampling consistency algorithm, and optimizing the affine transformation model according to an estimation result and similarity measurement; and performing spatial transformation on a to-be-registered remote sensing image by using the optimized affine transformation model to realize spatial alignment between the reference image and the to-be-registered image. According to the method, firstly, the feature points of the multi-modal remote sensing image are extracted, the difference of feature vectors is reduced by using a domain adaptive method, the precision and robustness of feature matching are improved, transformation parameters are optimized through an optimizer set algorithm, and search is prevented from falling into local optimum.

The invention provides a friction stir welding seam intelligent correction method based on visual identification, and relates to the technical field of friction welding. Image information of a welding seam area of a to-be-welded workpiece is collected in real time, image feature points are recognized, and the spatial pose of the welding seam area is obtained through a spatial transformation model; based on the space position deviation of the actual space pose and the theoretical welding track, a dynamic deviation correction path is generated; and a multi-point iterative optimization algorithm of motion control parameters and a real-time feedback control method are further adopted, so that high-precision real-time adjustment and dynamic compensation of the welding seam track are realized. According to the method, the accuracy and the stability of friction welding seam deviation rectification are remarkably improved.

The invention discloses a method, device and equipment for generating a head model, a storage medium and a program product. The method is characterized by comprising the following steps of: receiving and verifying original three-dimensional T1 weighted image data and an original three-dimensional grid model; performing tissue segmentation based on the verified three-dimensional T1 weighted image data and the original three-dimensional T1 weighted image data to obtain a cerebral grey matter mask, a cerebral white matter mask, a scalp mask and a skull mask; performing topological structure repair on the brain grey matter mask and the brain white matter mask to obtain a brain mask; respectively converting the scalp mask, the skull mask and the brain mask into a scalp mesh model, a skull mesh model and a brain mesh model; calculating and applying a spatial transformation matrix from the verified three-dimensional grid model to the scalp grid model, and performing spatial registration and fusion on the scalp grid model, the skull grid model and the brain grid model to obtain a head model; the method has the advantages that the efficiency, precision and robustness of head model generation are effectively improved, and the method has good cross-platform deployment capability.

The invention provides an aircraft appearance abnormity identification method and system based on cross-category feature registration, and relates to the technical field of aircraft intelligent detection and maintenance, and the method comprises the steps: obtaining an aircraft appearance image, and carrying out the preprocessing of the image; inputting the preprocessed image into a cross-category feature registration network, extracting feature maps by using a DenseNet dense connection feature extraction module, fusing the feature maps of different dense layers, and inputting the fused feature maps into an STN-C spatial transformation and alignment module to obtain a transformed feature map; performing channel and space attention on the transformed feature map to obtain an attention weighted feature map, and inputting the attention weighted feature map into a twin network into which a negative cosine similarity loss function is introduced to obtain an embedded vector; calculating a mahalanobis distance between the embedded vector and normal distribution, generating a pixel-level abnormal thermodynamic diagram, and positioning an abnormal region; and adaptively adjusting the threshold of the abnormal score graph through dynamic threshold segmentation, and realizing segmentation of an abnormal region to obtain a final abnormal result.