92 results about "Gabor filter" patented technology

A method is provided for improving image classification accuracy in few-shot learning scenarios, where only a limited number of training examples are available. The method combines the use of Gabor filters and convolutional neural networks (CNNs) to extract detailed texture and orientation features from images. These features are then enhanced through global average pooling, aggregated into comprehensive feature vectors, and refined using an attention mechanism that identifies and emphasizes the most relevant features for classification. Masks generated from this attention process selectively enhance critical features, which, after optional re-encoding, are used to train a classifier via a metric learning approach. This method aims to increase feature separability and classification performance, facilitating more accurate classification of new images with minimal training data.

The invention relates to the technical field of image data processing, in particular to a forge piece surface defect detection method and system based on machine vision. The method comprises the steps of obtaining a gray image of a to-be-detected forging surface; determining a boundary significance weight; determining a path consistency weight; screening the boundary significance weight and the path consistency weight to obtain a final weight; and carrying out local adaptive threshold segmentation on the final feature map to obtain a binary image, and carrying out defect identification on the surface of the forge piece to be detected based on a connected region in the binary image. According to the method, the boundary significance weight and the path consistency weight are constructed and are respectively used for accurately positioning defect edges and verifying structure continuity, texture interference is effectively inhibited, and false alarms are reduced; through adaptive Gabor filtering, the problem of a response blind area of a traditional method is solved, finally two weights are fused to modulate filtering response, and the accuracy of forging surface defect detection is improved.

The invention discloses an intelligent identification system for an osteoporosis area, belongs to the field of image processing calculation, and aims to solve the problems of limited technical coverage and lack of a dynamic optimization mechanism. In a feature extraction stage, a system combines traditional image processing and deep learning technologies in parallel, extracts bone trabecula multidirectional texture features by using a Gabor filter and a local binary pattern algorithm, analyzes bone contour curvature by combining Sobel edge detection and morphological operation, constructs geometric morphological parameters, and performs feature extraction on the bone trabecula. The local branch focuses on the porosity and arrangement rule of the bone trabecula by adopting a 3D convolutional network, the global branch is embedded into a compression excitation module based on an improved MobileNetV3 network to strengthen the overall morphological expression of the bone, the response intensity of a local microstructure is enhanced by space attention, the weight of global bone topological characteristics is calibrated by channel attention, and a multi-scale splicing strategy is combined, so that the overall morphological expression of the bone trabecula is optimized. And finally, outputting a fusion feature matrix after noise suppression, and remarkably improving the expression ability of pathological features.

The invention belongs to the technical field of image processing, and particularly relates to a screw fastening quality evaluation method and system based on image features, and the method comprises the steps: collecting a screw fastening image, and obtaining a complex response diagram through a Log-Gabor filter; obtaining a phase congruency diagram according to each complex response diagram; acquiring gradient directions of pixel points in the phase consistency graph, and constructing an accumulator graph according to the gradient directions and offset points in a preset radius range; according to salient points in the accumulator graph and local signal-to-noise ratios of the salient points, screw positioning points are determined through two-dimensional quadratic polynomial function fitting; the torque value of the screw is obtained according to the screw positioning point driving torque measuring device, and whether the screw is fastened or not is judged. According to the method, the screw is positioned by using phase consistency in the frequency domain, so that the interference of specular reflection and extreme shadow on positioning is overcome, the accuracy of screw positioning is improved, and screw fastening quality evaluation is assisted.

The invention relates to a bearing unbalanced sample fault diagnosis method based on dynamic modeling and a causal interpretable GAN, and belongs to the technical field of mechanical intelligent detection. Aiming at the problems of low model diagnosis precision caused by data imbalance in the prior art and deficient physical significance and poor interpretability of existing GAN network generation samples, the invention provides a technical scheme of fusing dynamic modeling and an interpretable generative adversarial network. The method comprises the following steps: generating a fault response signal with physical significance through a two-degree-of-freedom dynamic model; constructing a GabcauACGAN network, combining a causal loss function to restrain the relevance between the generated features and fault tags, and extracting time-frequency features by adopting a learnable Gabor filtering layer. The method has the technical effects that the time-frequency similarity SSIM of a generated fault sample and a real signal reaches 99%, and the diagnosis accuracy is improved to 98% or above.

The invention relates to a bridge structure modal parameter identification method, which is based on a bridge structure tiny vibration video acquired by a camera. VMD (variational mode decomposition), PBVM (phase-based video motion amplification), Gabor filtering-based phase extraction method GBP (Gabor filtering), an improved clustering algorithm ICA (independent clustering algorithm) and an integrated covariance-driven random subspace recognition (SSI-Cov) and FDD (frequency domain decomposition) algorithm are fused to construct a set of complete non-contact structural modal parameter recognition system. According to the method, effective decoupling of multiple vibration modes of a bridge structure, frequency domain feature extraction of small-amplitude vibration and intelligent and automatic modal parameter extraction are achieved under the conditions that the target frequency bandwidth does not need to be known in advance, environmental noise interference exists and no obvious feature target exists, and the method belongs to the technical field of bridge structure health monitoring.

The invention discloses a space-time image velocity measurement method and system based on multi-direction Gabor filtering, and the method comprises the steps: carrying out the filtering processing of a space-time image through Gabor filters in multiple directions, so as to enhance the texture features in multiple directions, carrying out the image quality evaluation of a Gabor filtering image in each direction, and obtaining the velocity measurement of the space-time image. The filtering direction with the best textural feature enhancement effect is screened out; a clustering point set with the highest comprehensive evaluation score is screened out through a two-layer evaluation screening mode of clustering quality evaluation and line segment quality evaluation, straight line fitting is carried out based on the clustering point set, an optimal flow velocity line can be obtained, and the river surface flow velocity is obtained through calculation; according to the method, a three-layer evaluation system composed of filtering direction evaluation, clustering quality evaluation and line segment quality evaluation is combined, a high-quality, correct and effective flow velocity line can be accurately extracted from a space-time image, and the accuracy and robustness of a flow velocity measurement result are greatly improved.

The invention provides a few-sample steganalysis method based on feature enhancement and sample expansion, and the method combines a few-sample learning theory, enhances the diversity of feature space, and uses the generated pseudo steganalysis sample to finely adjust the pre-training model, thereby improving the training effect and detection precision of the steganalysis model. Specifically, firstly, high-frequency noise features of an image are extracted through a spatial domain rich model and a 2D Gabor filter, and a steganographic feature map with the most representative is screened through normalization feature saliency measurement; thirdly, generating a steganography feature prototype by adopting a variational auto-encoder VAE, and generating a large number of pseudo-steganography samples through methods of sampling, threshold segmentation and the like, so as to enhance the diversity of training samples; and finally, in combination with the real steganography sample and the pseudo steganography sample, carrying out hierarchical training on the pre-trained steganography analysis model, and gradually optimizing the model performance. The method is suitable for various steganography analysis networks, and steganography images generated by different steganography algorithms can be effectively detected.

The invention belongs to the technical field of image analysis and processing, and relates to an automobile circuit board solder paste printing quality detection method and system. The method comprises the steps of constructing a Laplacian pyramid for a target solder paste area image, extracting multi-level geometric and position features, performing weighting based on local phase consistency to obtain a first fusion feature, and comparing the first fusion feature with a first fusion feature of a historical qualified sample to obtain a second fusion feature; calculating a time sequence drift correction characteristic capable of correcting the drift in the production process; constructing a neighborhood feature map by combining the first fusion features of the target region and the spatial neighborhood of the target region, and extracting spatial morphological features by using an orthogonal Gabor filter; and performing vector splicing on the first fusion feature, the time sequence drift correction feature and the spatial form feature to generate a comprehensive combined feature, and inputting the comprehensive combined feature into a predetermined classification model to obtain a printing quality detection result. According to the invention, the detection rate of the solder paste area defect on the automobile circuit board can be improved.

The invention discloses a micro-scratch filtering method and device in wafer defect detection, equipment and a storage medium, and the method comprises the steps: extracting the frequency spectrum characteristics of a wafer image, carrying out the direction characteristic analysis based on the frequency spectrum characteristics, and carrying out the calculation to obtain the center frequency of a target region suspected to have micro-scratches; constructing a Gabor filter according to the center frequency, and reinforcing the target area by using the Gabor filter; respectively carrying out gray integration along the direction vector and the vertical direction vector of the linear defect of the strengthened target area, and calculating the strength attribute of the target area; and when the strength attribute is higher than a preset threshold value, marking the defect of the target area as a micro-scratch and filtering out the micro-scratch. According to the method, the micro scratches in the image can be accurately positioned and filtered out, and the influence of the micro scratches on the accuracy of wafer defect detection is prevented.

Systems and methods are provided for performing optical coherence tomography (OCT) angiography for the rapid generation of en-face images. According to one example embodiment, differential interferograms obtained using a spectral domain or swept source OCT system are convolved with a Gabor filter, where the Gabor filter is computed according to an estimated surface depth of the tissue surface. The Gabor-convolved differential interferogram is processed to produce an en-face image, without requiring the performing of a fast Fourier transform and k-space resampling. In another example embodiment, two interferograms are separately convolved with a Gabor filter, and the amplitudes of the Gabor-convolved interferograms are subtracted to generate a differential Gabor-convolved interferogram amplitude frame, which is further processed to generate an en-face image without performing a fast Fourier transform and k-space resampling. The example OCTA methods disclosed herein are shown to achieve faster data processing speeds compared to conventional OCTA algorithms.

The invention discloses an outdoor dot matrix display screen picture flaw detection method, device and system based on texture features and a server. According to the method, at least two pure color test pictures are obtained, and defect full-coverage detection is realized by utilizing sensitivity differences of different color channels; besides, multi-scale feature extraction is carried out based on a Gabor filter, so that scale features such as single-point dead pixels, regional dark spots and module-level distortion can be captured, and multiple types of flaws such as brightness deviation and large-area uneven brightness can be covered. According to the method, the detection rate of traditional leak detection scenes such as small dead pixels and color deviation is increased to 95% or above, the environment robustness is high, the false detection rate in the complex illumination and vibration environment is low, and a high-reliability automatic defect detection solution is provided for the field of outdoor advertisements and public display.

The invention discloses a sandstone slice image component identification method based on a GL-SLIC algorithm and semi-supervised learning. The method comprises the following steps: firstly, acquiring a sandstone slice image amplified by 200 times under single polarization, converting an RGB image into a CIE-Lab color space, and extracting a GLBP texture feature vector; then a GL-SLIC superpixel segmentation algorithm is constructed, and adaptive segmentation based on texture and color features is realized in combination with a Gabor filter and a local binary pattern; then implementing a region merging algorithm to generate complete mineral particles and pore fragments; finally, a classifier based on VGG16 and a discriminator based on ResNet18 are constructed, a semi-supervised self-training framework is adopted, a model is initialized by using about 6% of manual labeling samples, a high-confidence-coefficient pseudo-label extension training data set is generated through iteration, and automatic recognition of sandstone components such as quartz, pores, kaolinite, rock debris and a matrix is achieved. According to the method, the recognition accuracy of 96.3% on a test set is achieved, compared with a traditional method, the segmentation precision and the recognition accuracy are remarkably improved, the data labeling cost is greatly reduced, and an efficient technical scheme is provided for automatic analysis of geological images.

The invention belongs to the technical field of image data processing, and particularly relates to a large shaft forging blank center forging method and system based on machine vision, and the method comprises the steps: carrying out the preprocessing of a high-temperature steel ingot image; carrying out convolution on the image by adopting a logarithm-Gabor filter bank to obtain complex response and local direction energy; for each pixel point, obtaining the structural significance of the pixel point based on the sum of the energy in the real part, the imaginary part and the local direction of the complex response; obtaining the direction certainty based on the local direction energy and the direction vector; obtaining a final voting weight by combining the structure significance and the direction certainty; accumulative voting is carried out based on the weight, the geometric axis of the steel ingot is calculated, and an anvil of the forging press is aligned with the geometric axis of the steel ingot. According to the method, weighted voting is carried out by combining the authenticity and directivity of the pixel points, strong interference such as water vapor and oxide skin is effectively restrained, and stable and accurate positioning of the steel ingot axis under the complex working condition is achieved.

The present application relates to the technical field of image data processing, and more particularly to a forging surface defect detection method and system based on machine vision. The method comprises: acquiring a grayscale image of a forging surface to be detected; determining a boundary saliency weight; determining a path consistency weight; screening the boundary saliency weight and the path consistency weight to obtain a final weight; performing local adaptive threshold segmentation on the final feature map to obtain a binary image, and based on the connected regions in the binary image, performing defect recognition on the forging surface to be detected. By constructing the boundary saliency weight and the path consistency weight, the present application is respectively used for accurately positioning the defect edge and verifying the structural continuity, effectively suppresses the texture interference and reduces false alarms; by adaptive Gabor filtering, the response blind area problem of the traditional method is solved, and finally the two weights are fused to modulate the filtering response, thereby improving the accuracy of the forging surface defect detection.

The present invention discloses a convolution kernel generation method, device, and electronic device, belonging to the field of medical image processing technology. The convolution kernel generated by the present invention is applied to a tubular structure segmentation system, wherein the convolution kernel generation method includes: implementing a three-dimensional Gabor filter according to the Gabor filter principle and constructing a Gabor convolution kernel; based on the backpropagation principle, transferring gradients according to the loss value obtained in each iteration to update the weight of the Gabor convolution kernel; and combining a target model constructed with the Gabor convolution kernel based on a CNN model structure of the Gabor convolution kernel, wherein the target model includes three parts: feature mapping, feature extraction, and feature fusion. The weighted learnable Gabor convolution kernel for tubular structure segmentation constructed by the embodiment of the present invention can directly enhance the features of the tubular structure in terms of scale and direction, reduce redundant parameters of the CNN, and extract more accurate tubular tissue.

The present invention discloses a method and system for testing the durability of pantograph contact networks for highway trucks. The method comprises: step S1, using a high-definition area array camera to capture a pantograph image, and employing a Gabor filter and a support vector machine to identify a pantograph image to be tested in the pantograph image; step S2, using an improved edge detection algorithm to extract a pantograph edge image from the pantograph image to be tested; step S3, locating the pantograph contour based on the pantograph edge image, and obtaining pantograph contour similarity using an invariant moment and a similarity calculation formula; and step S4, obtaining the degree of wear of the pantograph contact network for highway trucks based on the contour similarity, and determining the durability of the pantograph contact network based on the wear degree. By employing a Gabor filter and a support vector machine (SVM) to identify the pantograph image, the present invention can effectively extract pantograph shape information and improve the recognition accuracy of the pantograph image to be tested.

The present application relates to the technical field of coast water edge line detection, and is an island water edge line detection method based on phase consistency random walk, comprising: converting a single polarization SAR image into a phase consistency image by using a two-dimensional logarithmic Gabor filter; obtaining a sea-land membership function by using an FCM method, and creating a global sea-land priori; constructing a cross-sea bridge marker field based on LSD, and creating a marker field priori of a non-water-permeable structure connected to an island; introducing a super-pixel layer to construct a water edge line detection model; inputting the single polarization SAR image into the water edge line detection model to obtain an output label of each pixel, and realizing island water edge line detection. The present application systematically solves the problems of coherent noise interference in the single polarization SAR image, difficulty in seed point initialization of the traditional random walk method, insufficient water edge line extraction precision caused by complex island contour, and invalidity of gray scale similarity in edge detection caused by large differences in ground object backscattering, and can effectively extract the island coast line of the single polarization SAR image.

The invention provides a fingerprint image processing method which can be applied to the technical field of artificial intelligence. The fingerprint image processing method comprises the following steps: carrying out segmentation processing on an input fingerprint image by adopting a Gaussian mixture model based on an expectation maximization clustering algorithm to obtain a segmented fingerprint image; enhancing the segmented fingerprint image by using a direction and frequency adaptive Gabor filter to obtain an enhanced fingerprint image, the direction of the Gabor filter being determined by a fingerprint local ridge direction, and the frequency being determined by a local ridge frequency; performing binarization processing on the enhanced fingerprint image by adopting a local threshold method to obtain a binarized fingerprint image; and processing the binarized fingerprint image by adopting a sequential refinement algorithm, deleting boundary pixels until a skeleton with a single-pixel width is reserved, and obtaining a processed fingerprint image. The invention further provides a fingerprint image processing device and equipment, a storage medium and a program product.

The application provides a replay voice attack detection method for a voiceprint security authentication system. First, the voice signal is preprocessed, then a plurality of sub-band signals are obtained through linear equal-width Gabor filters, each sub-band signal is processed through FDEO to obtain an instantaneous amplitude and an instantaneous frequency, then the instantaneous amplitude and the instantaneous frequency are respectively taken as inputs of an SENet to obtain enhanced IACC and IFCC features, and the features are respectively processed through windowing and averaging and discrete cosine transformation to obtain respective low-dimensional feature vectors. Then, the extracted IACC and IFCC feature vectors are respectively used to train respective Gaussian mixture model classifiers to obtain respective classifier model parameters. In detection, the IACC and IFCC feature vectors of the voice to be detected are respectively input into the respective GMM classifiers and are scored for credibility, and finally score level fusion is performed to realize discrimination of true and false voices.

The invention provides a rock image classification method based on super-resolution and texture feature fusion. According to the method, the accuracy of mineral classification in the rock slice image is remarkably improved by combining a super-resolution model and a texture feature analysis technology, the enhanced image texture features are extracted by utilizing a Gabor filter, and classification is carried out by adopting a support vector machine. By comparing the performance of various SR models, the optimal model is selected to enhance the image resolution, so that the recognition effect of minerals and pores is improved. In addition, the system further optimizes the classification result through multi-color space feature fusion, finer mineral distribution data is provided for geological analysis, the mineral classification efficiency and precision are improved, and reliable technical support is provided for geological evaluation of oil and gas reservoirs.

The invention discloses a fault sand body identification method based on seismic attribute phase consistency, and the method comprises the steps: S1, extracting the root mean square attribute of a target horizon in a block, and obtaining basic input data; s2, performing normalization and transformation processing on the basic input data to obtain a radial component and an angle component of a phase consistency filter; s3, a multi-scale Log-Gabor filter is constructed; s4, circularly traversing different directions and different scales, calculating the filter response for the convolution of each direction and each scale, respectively accumulating the amplitude response of each scale as SumAn, accumulating the convolution result of an even number filter as SumE, accumulating the convolution result of an odd number filter as SumO, and then calculating a weighted mean response vector; s5, calculating phase consistency energy and phase consistency; s6, constructing covariance data of each point in the image, and calculating the maximum moment of the matrix according to the covariance data; and S7, on the basis of the weighted mean response vector obtained in the step S4, calculating a feature type, namely obtaining a lithologic feature.

The present invention discloses a method for segmenting wire rope images, which includes two parts: body segmentation and strand segmentation. First, the texture features of the image are extracted by Gabor filtering using the C++ programming language, and a marker map is obtained by combining morphological processing methods. Then, the watershed function is applied for watershed segmentation to obtain the body segmentation result. Subsequently, based on the obtained body segmentation result, strand segmentation is continued, and finally, the segmentation result of the area between the wire rope strands is obtained, which can perform efficient and automated processing on wire rope images and has high accuracy, thereby realizing accurate evaluation of the state of wire rope and ensuring the safe operation of equipment.

The invention relates to the technical field of computer vision and image processing, and discloses a magnet surface defect detection method based on computer vision. Obtaining a grayscale image, and carrying out median denoising and linear normalization; a plurality of Gabor filtering kernels are generated in a multi-scale and multi-direction self-adaption mode, and convolution is carried out on the preprocessed image to obtain a response; extracting a phase and accumulating cosine and sine components to calculate an average phase; calculating phase consistency based on the phase deviation; performing adaptive threshold segmentation to generate candidate masks; carrying out 8-neighborhood connected domain analysis on the mask and filtering small-area noise; and calculating a bounding box, a centroid and an area of each effective connected domain and outputting a report. Through Gabor phase consistency measurement and self-adaptive segmentation, the problems of process fragments, parameter mismatch, missing and false detection and the like are solved, high robustness and accuracy are achieved, and the method is suitable for industrial automatic quality inspection.

The invention relates to the field of image classification, in particular to a hyperspectral image classification method based on a decoupling Gabor network. Two one-dimensional Gabor kernels along an orthogonal space axis are adopted, each one-dimensional Gabor kernel comprises a decoupling Gabor filter in the x direction and a decoupling Gabor filter in the y direction, a decoupling Gabor convolution module is constructed based on the decoupling Gabor filters, and specific redundant parameters of channels are greatly reduced while the effective characterization capability is reserved by sharing Gabor parameters and a parameter scattering scheme between the channels, so that the performance of the system is improved. And the overall complexity of the network is obviously reduced. And through a decoupling Gabor convolution module, a global average pooling layer and a multiple perceptron layer are combined to obtain a decoupling Gabor network. After input data is preprocessed, a network is trained, and finally, a hyperspectral image is put into the network to realize a classification effect. According to the method, efficient deployment is realized in a resource-limited hardware environment, the calculation cost and model parameters are remarkably reduced when high-dimensional data are processed, and the training and reasoning efficiency of the network is improved.

This application proposes a hyperspectral image classification method for uncertainty estimation, which relates to the technical field of hyperspectral image classification. The method includes: obtaining a hyperspectral image; constructing an adaptive dimensionality reduction layer, inputting the hyperspectral image into the adaptive dimensionality reduction layer to extract spectral features, and obtaining a reduced-dimensional hyperspectral image; constructing a Gabor convolution layer using a Gabor filter, characterizing the parameter distribution of the Gabor convolution layer using Bayesian deep learning, and inputting the reduced-dimensional hyperspectral image into the Gabor convolution layer to extract the spectral-spatial joint features of the hyperspectral image; constructing a Bayesian fully connected layer, causing the Bayesian fully connected layer to learn the mask distribution of a standard Dropout layer, inputting the spectral-spatial joint features into the Bayesian fully connected layer, and performing classification through the Bayesian fully connected layer to obtain a classification result for the hyperspectral image. This application improves the classification accuracy of the hyperspectral image classification method and provides uncertainty estimation of the hyperspectral classification results, which can reflect the degree of confidence in the classification results.

The invention provides a motion texture joint threshold (WTJT) method, which comprises the following steps of: firstly, comparing the motion speed and the motion vector of a current frame with the motion speed and the motion vector of a previous frame and a next frame through the sum of absolute differences (SAD) in a frame interpolation information acquisition mode, and screening candidate jump blocks; secondly, extracting two textural features of direction and frequency details from adjacent frames based on a local binary pattern (LBP), and screening candidate hop blocks again in combination with Gabor filtering; and finally, performing spatial-temporal feature decomposition on an input video sequence, establishing a proportional relation between motion information and texture feature information, and calculating a joint weighting coefficient (JWC) so as to dynamically adjust a block jumping threshold value of each frame and realize adaptive allocation of a block jumping proportion. Through united weighting after motion and texture features are normalized, the method provided by the invention improves the accuracy of block jumping judgment, optimizes the distribution of block jumping rate, and achieves balance between algorithm complexity and reconstruction quality.

The invention discloses a metal curved surface grating three-dimensional contour macro-micro cross-scale characterization method and system. The method comprises the following steps: reconstructing global three-dimensional morphology data of a measured metal curved surface diffraction grating based on original data of sub-aperture splicing measurement of the measured metal curved surface diffraction grating; performing frequency domain filtering separation to obtain microscopic diffraction structure surface data and macroscopic substrate surface shape data; performing iterative nearest point surface shape registration on the macroscopic substrate surface shape data to calculate a grating substrate surface shape error; carrying out two-dimensional Gabor filtering on the surface data of the microscopic diffraction structure to calculate a mean value and a standard deviation of a grating period, and carrying out local plane least square fitting to calculate spatial distribution of the grating period and a blaze angle; and outputting the processing results of the macroscopic substrate surface shape data and the microscopic diffraction structure surface data as a three-dimensional contour cross-scale representation result. The invention aims to simultaneously realize high-precision characterization of the macroscopic substrate and the microscopic diffraction structure of the curved-surface grating aiming at the microstructure of the metal curved-surface diffraction grating.

The invention provides a method for predicting spinal refracture of an OVCF patient based on an X-ray image. According to the method, a multi-scale space-time convolutional neural network is adopted to analyze a standardized thoracolumbar spine X-ray image sequence, a vertebral body morphology analysis module, a microstructure texture feature extraction module and a time sequence dynamic modeling module are integrated, and the vertebral body morphology analysis module is used for extracting vertebral body geometric morphology features including the height ratio relation of the front edge, the center and the rear edge; the microstructure texture feature extraction module adopts a multi-direction Gabor filter combination and a local binary mode operator to extract the directivity, density and connectivity features of the bone trabecula; the time sequence dynamic modeling module adopts a hybrid architecture of combining a three-dimensional convolutional neural network with a long-short-term memory network; the system further comprises inter-vertebral-body relevance modeling. According to the method, a multi-task loss function item is adopted for joint optimization. According to the method, the prediction accuracy reaches 92.6%, patients can be divided into three risk levels, and effective decision support is provided for clinical precise prevention and treatment.

The invention provides a printing pattern retrieval method and device based on multi-feature fusion matching, equipment and a medium, and the method comprises the steps: processing an input printing image through an LBP operator, and obtaining the first texture information of the printing image; acquiring second texture information of different directions and frequencies of the printed image through a Gabor filter; fusing the first texture information and the second texture information to obtain fused texture information; the method comprises the following steps: setting an IP-Adter extractor to be in a stroke transfer mode, and extracting a corresponding style feature from a printed image; fusing the texture information and the style features to form a final feature vector; and carrying out similarity calculation on the final feature vector and the feature vector of each picture, and displaying the picture with the highest similarity, so that the required pattern is accurately retrieved, and the error detection condition is avoided.