39 results about "Anisotropic diffusion filtering" patented technology

The invention provides a scanning electron microscope image edge detection method, which comprises the following steps: carrying out anisotropic diffusion filtering on a scanning electron microscope image to suppress noise and reserve edges to obtain a filtered image; an original scale gradient and a down-sampling scale gradient of the filtered image are calculated, a plurality of pixel regions in the original scale gradient and the down-sampling scale gradient are fused based on a plurality of adaptive weights to obtain a gradient map, the adaptive weights are determined based on the complexity of the texture of the filtered image, and each pixel region corresponds to one adaptive weight; multi-dimensional features are extracted from the gradient map, the multi-dimensional features are input into a machine learning model for threshold prediction, a continuous pixel-level threshold map is generated based on a predicted threshold, and a gradient threshold included in the continuous pixel-level threshold map is a critical value for distinguishing different types of pixels in the gradient map; and comparing the gradient map with the continuous pixel-level threshold map to obtain a target edge pixel, and generating an edge detection result map based on the target edge pixel.

The invention discloses an AI-based ultrasonic image feature extraction and recognition system and method, and particularly relates to the field of image feature recognition, and the system comprises a bimodal imaging module, an image preprocessing and registration fusion module, an AI feature extraction module, an AI classification and recognition module, and a diagnosis report generation module. The method comprises the following steps: constructing a bimodal image data set by synchronously collecting ultrasonic and photoacoustic information; after anisotropic diffusion filtering and photon inversion reconstruction preprocessing are adopted, sub-pixel-level space alignment is realized based on B-spline non-rigid registration, and multi-scale fusion is completed by using a Laplacian pyramid; designing an improved DenseNet-121 network to extract form, texture, blood flow and functional four-dimensional feature vectors, inputting the feature vectors into an integrated classifier after L1 regularization dimensionality reduction, and outputting benign and malignant probabilities through a dynamic weighting strategy; clinical priori knowledge is combined to calibrate confidence, and a structured report containing three-dimensional positioning, BI-RADS grading and risk prompting is automatically generated.

The invention relates to the technical field of image recognition, and discloses an industrial pipeline welding defect recognition system based on a DR detection image, and the system comprises the steps: obtaining geometric parameters through a laser contour scanner, calculating a radial distortion amount, and carrying out the curved surface distortion correction of an industrial pipeline; exposure parameters are obtained, a welding seam type and exposure parameter mapping table is constructed, an amplitude value is obtained, and the edge diffusion amount is reduced; after the edge diffusion amount is reduced, a scattered field model of a multi-layer medium is constructed, an effective gray value is calculated, anisotropic diffusion filtering is combined to enhance welding seam area features, and material difference correction and gray offset compensation correction are carried out on dissimilar steel welding; establishing a double-branch decoupling network, converting a welding seam path into a welding seam binary template, calculating fusion line constraint loss, and training an enhancement strategy by adopting a mixed data set; constructing an exposure parameter transfer learning model, and normalizing the cross-working-condition image through a gray transfer function; and constructing standard rule libraries, and calling multiple standard rule libraries in real time to carry out compliance judgment.

The invention belongs to the technical field of condition judgment, and particularly relates to an electrode foil corrosion condition judgment method and system, and the method comprises the following steps: S1, obtaining a grayscale image of a to-be-analyzed electrode foil; constructing a structure tensor based on the composite gradient reflecting the local brightness and texture information of the pixel points, determining an anisotropic diffusion coefficient according to the structure tensor, and performing iterative anisotropic diffusion filtering processing on the grayscale image by using the anisotropic diffusion coefficient to obtain a filtered image; and S2, fusing the anisotropic diffusion coefficient determined for each pixel point with the pixel intensity of the filtered image to obtain a high-contrast corrosion significance map. According to the method, the contour edge information of the corrosion area can be kept while the complex texture and noise of the background area of the electrode foil image are smoothed, the contradiction between denoising and edge protection of a traditional filtering method is solved, the result of the corrosion condition is more reliable, and the automation level of electrode foil product quality detection is improved.

The invention provides a seismic fault identification method and system based on multi-attribute collaboration and spectral domain graph enhancement, and relates to the technical field of seismic exploration, and the method comprises the steps: carrying out the all-directional dip angle and azimuth angle scanning of a seismic data volume, extracting multi-scale guide field information through combining structure tensor decomposition, and executing anisotropic diffusion filtering; calculating characteristic value distribution through a characteristic value coherence algorithm based on the filtering data volume, and determining the structural consistency difference of adjacent seismic traces to obtain a fracture coherence attribute data volume; carrying out azimuth gather sorting and pre-stack time migration processing on the seismic data volume, extracting seismic wave dynamic response characteristics, carrying out Fourier series expansion on the azimuth change rate to obtain a crack indication information data volume, splicing the data volume and executing multi-scale three-dimensional convolution solution, and constructing a spatial dependency graph through a spectral clustering algorithm; spectral domain enhancement features are obtained through spectral domain graph transformation and frequency selective filtering reconstruction, and morphological connectivity analysis is executed to obtain a crack prediction result.

This invention discloses a method and system for following up on magnetic resonance imaging (MRI) images of amyloid-related radiological abnormalities, belonging to the field of medical image processing. The method includes: acquiring multimodal MRI image data; performing bias field correction and intensity normalization based on a bias field fitting model to generate a first image set; performing global alignment and edge detection on the first image set to extract anatomical physical boundaries, generating brain region mask data; performing branch deformation field registration on the first image set within the brain parenchyma defined by the brain region mask data to generate a spatially aligned second image set; calculating the average intensity of normal brain parenchyma regions in the second image set to obtain a scaling factor, acquiring a three-dimensional residual image; performing anisotropic diffusion filtering denoising on the three-dimensional residual image, and performing differential denoising and morphological screening processing, outputting the detection results. This invention eliminates systematic artifacts caused by insufficient bias field correction.

This invention relates to the field of image data processing technology, specifically to a method and system for detecting the edge polishing quality of mobile phone glass covers. The method includes: capturing an image of the mobile phone glass cover to be inspected; filtering the image using an improved anisotropic diffusion filtering algorithm; and performing edge detection on the filtered image to obtain multiple edge contours. If the number of edge contours exceeds a set threshold, the image is deemed to be of substandard quality. This invention solves the problem of low accuracy in edge polishing quality detection of mobile phone glass covers using existing anisotropic diffusion filtering algorithms.

The invention relates to the technical field of medical image processing, and discloses a chronic obstructive pulmonary emphysema distribution quantitative method and system based on multi-scale segmentation, and the method comprises the steps: carrying out the anisotropic diffusion filtering noise reduction of a chest CT image; segmenting a lung field and removing a blood vessel bronchial structure by adopting a region growing algorithm; multi-scale image representation is constructed based on a Gaussian pyramid, an emphysema candidate area is identified in a coarse scale layer, and a boundary is accurately drawn by adopting a self-adaptive threshold value in a fine scale layer; extracting local texture features to distinguish the lobular central emphysema and the total lobular emphysema; dividing severity levels according to spatial aggregation characteristics and density gradient distribution, and calculating an air swelling volume ratio and a distribution heterogeneity index; the three-dimensional pseudo-color volume is used for drawing visualization, a structured quantitative report is generated, accurate segmentation and subtype classification of the emphysema area are achieved, and comprehensive quantitative analysis indexes are provided.

This invention relates to the field of image processing technology and discloses a multimodal data fusion method for online quality monitoring of plastics. The method includes: simultaneously acquiring a reference optical image of the plastic surface and a temporal infrared thermal distribution image containing the current frame and the previous frame infrared image; calculating a spatial temperature gradient matrix; correcting the geometric affine matrix based on the displacement vector normal component determined by the dense optical flow field, constructing a spatiotemporal compensation mapping matrix to achieve alignment of heterogeneous pixel coordinate systems, and generating a structure-guided tensor; applying morphological opening operations to remove low-frequency background thermal fluctuations from the structure-guided tensor to obtain a pure-state high-frequency thermal gradient matrix; adjusting the diffusion coefficient using the pure-state high-frequency thermal gradient matrix as a constraint, applying anisotropic diffusion filtering to the reference optical image, and extracting defect regions. This invention achieves physical-level decoupling between optical artifacts and real defects, eliminates feature mapping deviations caused by thermal conduction hysteresis, and improves the signal-to-noise ratio of image feature extraction.

The present application belongs to the technical field of image data processing, and particularly relates to a method for analyzing pressure uniformity of an impression roller based on indentation image features, which comprises the following steps: obtaining an indentation grayscale image; determining local texture complexity of a pixel point; determining neighborhood gradient direction consistency and real noise disturbance index of the pixel point; determining an adaptive gradient threshold value of the pixel point; obtaining a filtered indentation grayscale image, extracting indentation width features, and evaluating pressure uniformity of the impression roller. The present application analyzes the gray value features and gradient direction features of the pixel points in the neighborhood of the pixel point, extracts the indentation under the interference of wear texture, overcomes the difficulty of distinguishing the wear texture and the indentation by using a fixed gradient threshold value in the traditional anisotropic diffusion filtering algorithm, filters out the wear texture noise interference and extracts the indentation by using the adaptive gradient threshold value, and improves the accuracy of the evaluation of the pressure uniformity of the impression roller.

The invention discloses a marine sandstone inter-gravel pore semantic segmentation method based on multi-model collaboration, and relates to the field of marine sandstone inter-gravel pore image segmentation, and the method comprises the steps: obtaining multi-source marine sandstone data, carrying out format analysis, metadata extraction, gray level normalization and size unification, and outputting standardized data; performing anisotropic diffusion filtering, directional gradient enhancement and channel-space joint noise sensing on the standardized data to generate intermediate representation with a high signal-to-noise ratio; respectively inputting the intermediate representation into three model branches of U-Net, DeepLabV < 3 + > and PSPNet for collaborative segmentation, and outputting a plurality of model features; performing weighted fusion on the plurality of model features based on an attention mechanism to obtain fused features; and performing inter-gravel pore repair and artifact suppression on the fused features, and outputting a final segmentation result. The method has the beneficial effects that the precision, robustness and practicability of the scheme are improved through innovative technologies such as multi-model collaboration, dynamic fusion, noise suppression and adaptive receptive field adjustment.

The present application belongs to the technical field of situation judgment, and particularly relates to a kind of electrode foil corrosion situation judgment method and system, comprising the following steps: S1, the gray scale image of electrode foil to be analyzed is obtained;Based on the composite gradient reflecting the local brightness and texture information of pixel point, the structure tensor is constructed, and the anisotropic diffusion coefficient is determined according to the structure tensor, the gray scale image is processed by iterative anisotropic diffusion filtering using the anisotropic diffusion coefficient, and the filtered image is obtained;S2, the anisotropic diffusion coefficient determined for each pixel point is fused with the pixel intensity of the filtered image to obtain a high-contrast corrosion significance map.The present application can smooth the complex texture and noise in the background area of electrode foil image while maintaining the outline edge information of the corrosion area, solve the contradiction between denoising and edge preservation in traditional filtering method, make the corrosion situation result more reliable, and improve the automation level of electrode foil product quality detection.

This invention relates to the field of image data processing technology, specifically to a fastener quality traceability method based on the Industrial Internet of Things (IIoT). The method includes: acquiring surface images of multiple fasteners to be inspected and their corresponding timestamps; smoothing the surface images using an improved anisotropic diffusion filtering algorithm to obtain multiple smoothed surface images; performing edge detection on the smoothed surface images to obtain multiple edge contours; obtaining the area of ​​the bounding rectangle of each edge contour; if the area is not within a set allowable range, determining that the fastener to be inspected has a quality defect; and tracing the source based on the timestamps of the surface images of the multiple fasteners to be inspected. This invention solves the problem of low accuracy in quality inspection.

The invention discloses a cardiac death area image detection method and system based on a cardiovascular image, and relates to the technical field of image detection, and the method comprises the steps: obtaining cardiovascular image data; in the current de-noising stage, de-noising processing is carried out on the cardiovascular image data by adopting an anisotropic diffusion filter; evaluating a noise suppression effect according to the denoised cardiovascular image data; according to the noise suppression effect, a de-noising processing strategy is adjusted, and the de-noising processing strategy is used for executing the next de-noising stage; after noise suppression processing of the cardiovascular image data is completed, a denoising quality grade is generated, and the noise suppression processing comprises a plurality of denoising stages; and according to the de-noising quality grade, carrying out myocardial death area image detection on the cardiovascular image data after noise suppression processing. According to the method, fine-grained de-noising processing can be carried out by dividing multiple de-noising stages, the de-noising effect is enhanced, image detection is achieved, and the accuracy and reliability are improved.

The application relates to an anti-NMDAR encephalitis clinical prognosis evaluation method based on artificial intelligence and belongs to the technical field of artificial intelligence and data processing. The method comprises the following steps: acquiring multi-modal neural image data of a patient; adopting a tensor decomposition fusion strategy to perform fusion preprocessing on the multi-modal neural image data, retaining cross-modal spatial correlation through low-rank constraint to obtain an output tensor after fusion; performing lesion-aware anisotropic diffusion filtering on the output tensor after fusion to obtain an output image after diffusion filtering; constructing an anti-NMDAR encephalitis clinical prognosis evaluation model, inputting the output image after diffusion filtering into the model for training, optimizing the training process by adopting an Adam adaptive optimizer, and finally obtaining a trained model; and inputting the output image after diffusion filtering to be evaluated into the trained model to obtain an evaluation classification result. The application can enhance the lesion recognition and classification capability of the model.

The invention provides a step-by-step frequency expanding method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring a pre-stack CRP gather; performing interpretive CRP gather superposition on the pre-stack CRP gather to obtain superposed seismic data; performing spectrum equalization processing of a scale domain on the superposed seismic data to obtain a reconstructed seismic image; carrying out smooth filtering processing on the reconstructed seismic image by adopting anisotropic diffusion filtering; selecting a scale range with a high signal-to-noise ratio in the reconstructed seismic image after smooth filtering processing to extrapolate other scale segments to obtain seismic data of a plurality of scale segments; and performing signal reconstruction on the seismic data of all the scale segments to obtain broadband seismic data. According to the method, the relative effective frequency band of seismic data can be remarkably widened, stratum details are well depicted, the resolution of the seismic data is effectively improved, and the method has high industrial practical value and application prospects in oil and gas seismic exploration.

The present application relates to the technical field of fault diagnosis, and more particularly to an air conditioning unit fault monitoring method and system based on multi-modal data fusion. The method comprises: acquiring state values of the air conditioning unit in multiple dimensions in real time; determining the abnormality degree of each dimension at the current time; determining the time step optimization coefficient of each dimension at the current time; determining the adaptive time step of each dimension at the current time; using an anisotropic diffusion filtering algorithm for denoising and obtaining the abnormal score of the air conditioning unit at the current time to realize fault monitoring of the air conditioning unit. The present application dynamically adjusts the time step according to the sorting position of the target dimension state value within the time period of the current time, the median and the coefficient of variation in the ranking, and other indicators, so as to more flexibly and accurately capture the change speed of the state value in different time periods, avoid the defect that the fixed time step in the traditional algorithm cannot adapt to the fast and slow state changes, and make the subsequent fault monitoring more accurate.

The application provides a high-end equipment weld seam radiographic defect semantic reasoning method and system under a few sample conditions. First, U-Net is used to extract the weld seam area and adaptively slide window and cut, and the anisotropic diffusion filtering and the variance guided CLAHE enhancement bottom layer features are combined; a controllable diffusion model of a boundary box perception symbol distance and a category condition is proposed to generate a high-fidelity synthetic defect image. Second, a light-weight visual detection model is used to output defect positioning and classification results. Then, an industrial weld seam knowledge graph is constructed. Finally, a large-small model coordination center of a physical-semantic dual state space is constructed, visual features are mapped to a large language model for semantic review, batch statistics and trend early warning are realized by analyzing the lead type identification, and a customized diagnosis report containing defect type qualitative analysis, mechanism tracing, physical troubleshooting and trend early warning is output. The application realizes a leap from bottom layer visual perception to deep layer causal cognition.

The invention discloses an electric arc multi-dimensional quantitative analysis system and method. The method specifically comprises the following steps: framing an electric arc video and performing time error compensation; taking the arc-free reference image as a benchmark, adopting anisotropic diffusion filtering noise reduction, converting the reference image and the target image into a YUV color space, and generating an arc region mask based on brightness channel difference; a low-temperature area, a medium-temperature area and a high-temperature area are obtained through segmentation in the mask by adopting a maximum between-class variance threshold value; establishing a pixel-level radiation power model, and calculating the energy of each temperature zone and the energy ratio of each temperature zone; extracting a histogram of oriented gradient feature in the temperature partition; recognizing and combining quasi-steady-state intervals by combining time and area double threshold values, a time change rate threshold value and a sliding variance threshold value; and outputting a temperature distribution diagram, a curve of energy and energy ratio along with time and a gradient field thermodynamic diagram. The system is composed of a video framing processing module, an electric arc characteristic acquisition module, a quasi-steady state analysis module and an energy visualization module, and zoning of the electric arc and joint quantification and display of energy and form are achieved according to the process.

The invention relates to the technical field of fault diagnosis, in particular to an air conditioning unit fault monitoring method and system based on multi-modal data fusion. The method comprises the steps that state values of the air conditioning unit in multiple dimensions are obtained in real time; determining the anomalous degree of the current moment in each dimension; determining a time step optimization coefficient of the current moment in each dimension; determining the self-adaptive time step length of the current moment in each dimension; and denoising by using an anisotropic diffusion filtering algorithm, and obtaining an abnormal score of the air conditioning unit at the current moment so as to realize fault monitoring of the air conditioning unit. According to the method, the time step length is dynamically adjusted according to the indexes such as the sorting position, the ranking median and the variation coefficient of the target dimension state values in the time period to which the current moment belongs, so that the change speeds of the state values in different time periods are more flexibly and accurately captured; the defect that a fixed time step length in a traditional algorithm cannot adapt to the speed of state change is avoided, and follow-up fault monitoring is more accurate.

The invention relates to the field of defect detection, in particular to a flexible gear tooth surface micro defect detection method based on machine vision, and the method comprises the steps: obtaining a gray level image of a flexible gear tooth surface, constructing a structure tensor, calculating local texture coherence, and carrying out the statistics of a global dominant knife grain direction; based on the local texture coherence and the consistency between the local texture direction and the global dominant knife grain direction, calculating the background texture confidence; self-adaptive anisotropic diffusion filtering is carried out on the flexible gear tooth surface image by using the background texture confidence coefficient to obtain a background suppression image, a conduction coefficient during self-adaptive anisotropic diffusion filtering comprises a threshold item, and the threshold item is positively correlated with the background texture confidence coefficient; and extracting a defect area based on the difference operation of the background suppression image and the gray level image so as to realize the detection of the tooth surface defect of the flexible gear. According to the method, the signal-to-noise ratio and the accuracy of weak defect detection under the strong texture background are improved.

The invention relates to the technical field of image processing, in particular to a positioning method for processing a mobile phone glass cover plate, and the method comprises the steps: obtaining the structural significance of each pixel point in a grayscale image of the mobile phone glass cover plate; filtering the grayscale image by using improved anisotropic diffusion filtering to obtain an enhanced image; and performing template matching according to the enhanced image to realize positioning of the mobile phone glass cover plate. According to the technical scheme of the invention, the anisotropic diffusion filtering can be improved, so that the improved anisotropic diffusion filtering can effectively retain the edge information in the mobile phone glass cover plate, and the positioning precision is improved.

The invention discloses a carburized gear internal oxidation rating method and system based on deep learning, and belongs to the technical field of carburized gear quality detection.The carburized gear internal oxidation rating method comprises the steps that a scanning electron microscope image of a carburized gear is obtained and preprocessed, and after noise is reduced through anisotropic diffusion filtering, an internal oxidation structure area is segmented through morphological operation; textural features are extracted based on gray-level co-occurrence matrix and local binary pattern fusion, and feature vectors are screened and optimized through Pearson's correlation coefficients; inputting the feature vectors into a support vector machine classification model subjected to particle swarm optimization to obtain an internal oxidation degree preliminary classification result; and in combination with an internal oxidation comprehensive evaluation system, a final quantitative rating result is output through probability threshold judgment, deviation matrix linear analysis correction and service condition and material characteristic matching. According to the carburizing gear internal oxidation grading method, automation, standardization and precision of carburizing gear internal oxidation grading are achieved, and scientific support is provided for gear quality detection and service safety guarantee.

The application discloses a carburized gear inner oxidation rating method based on GLCM-LBP fusion features, and belongs to the technical field of carburized gear quality detection. The method acquires a SEM image of a carburized gear and performs pretreatment, carries out anisotropic diffusion filtering denoising and morphological operation to segment an inner oxidation tissue area; after gray level compression of the area image, GLCM and LBP texture feature vectors are extracted respectively and fused to obtain a final feature vector; the feature vector is input into a PSO-optimized SVM classification model to obtain a preliminary classification result, and a quantitative rating result is output in combination with an inner oxidation comprehensive evaluation system. The application can realize automation, standardization and precision of carburized gear inner oxidation rating, and improve detection efficiency and objectivity.

The embodiment of the application provides an OCT image choroid segmentation network system and method based on a feature integration method, a network model comprises a preprocessing module, a feature extraction module, a feature fusion module and a secondary feature fusion module, firstly, an anisotropic diffusion filtering algorithm is used for filtering in the preprocessing module, and an exponential and linear enhancement method is used for enhancement; then, a residual Inception structure is used in the feature extraction module, different size receptive fields are fused to extract rich feature expressions of the image, a pyramid pooling is used in the feature fusion process to further acquire context information, a channel attention module pays more attention to the relationship between channels, extracts a channel weighting vector of a feature map, acquires the importance of each channel, and a secondary feature fusion process fuses the last layer of the feature encoder with the last layer result of the feature encoder, and loss is calculated. The application effectively improves the performance of choroid segmentation, and shows superiority compared with other existing methods.

The invention is suitable for the technical field of image processing, and provides a multi-modal remote sensing image registration method and system, and the method comprises the following steps: constructing a nonlinear scale space for remote sensing images of all modals through anisotropic diffusion filtering, and obtaining a multi-scale image sequence of each group of images; based on the multi-scale image sequence, performing multi-direction symbiotic filtering on the image of each scale to generate a corresponding symbiotic filtering image pyramid; according to the symbiotic filtering image pyramid, calculating a phase congruency graph of each scale image, and extracting feature points; according to the feature points, constructing a feature descriptor based on weighted phase consistency; and carrying out bidirectional matching on the feature descriptors corresponding to the remote sensing images of each mode, and then eliminating mismatching to obtain a registration result. According to the method, the number of features can be effectively increased, the descriptor robustness is enhanced, the final registration precision is improved, and reliable technical support is provided for cooperative processing and application of multi-source remote sensing information.

The application relates to the technical field of medical image processing, and discloses an intracranial aneurysm recognition method and system based on a brain CT. The method obtains a brain CT image sequence, adopts an anisotropic diffusion filtering algorithm for pretreatment to suppress noise and retain blood vessel edge details. Subsequently, a region growing algorithm is used to segment an intracranial blood vessel region, and a skeletonization algorithm is used to extract a blood vessel center line to calculate curvature and diameter changes. Finally, based on curvature anomaly and diameter ratio analysis, combined with a multi-scale sliding window and a blood vessel topological structure verification, automatic recognition of intracranial aneurysms is realized, and the accuracy and reliability of diagnosis are improved.

The invention relates to the technical field of image detection, in particular to a connector injection molding surface defect detection method based on multi-scale feature fusion, which comprises the following steps: decomposing an original image into low-frequency and high-frequency sub-bands by using an edge strengthening module; performing anisotropic diffusion filtering and second-order structure tensor analysis based on the high-frequency sub-band to generate a multi-scale feature image; carrying out brightness alignment on the low-frequency sub-bands by taking a CAD standard template as a reference and reconstructing an image; performing space alignment and pixel-level difference on the reconstructed image and a template, and extracting a defect residual field; and performing bidirectional weighted projection and task decoupling by using a space-frequency domain decoupling model, decomposing a residual field into parameterized description vectors, and finally generating a detection report. According to the method, CAD template semantic division and a space-frequency domain decoupling mechanism are introduced, so that the interference of a complex geometric structure and background characters on defect characteristics is effectively stripped; and non-subsampled multi-scale feature evolution is utilized, so that the spatial resolution loss of a traditional algorithm is avoided.

The invention discloses an orthopaedic implant position monitoring method and system, and the method comprises the steps: carrying out the structural collection of a multi-mode ultrasonic image data packet, obtaining an initial contour through the combination of anisotropic diffusion filtering and Otsu threshold segmentation, and carrying out the calculation and optimization of a curvature and a gradient distance, and obtaining a precise main boundary contour; normal folding and suspected damaged line segment clusters are efficiently distinguished through edge detection, line segment screening clustering and ladder coefficient calculation; by means of induced stress dynamic sequence processing, feature point vector extraction and pressurization / rebound period fitting, response hysteresis is quantified in combination with a step coefficient, and finally a hierarchical decision is formed by comparison with historical data and a clinical decision rule base. According to the invention, objective evaluation of the position and structural integrity of the implant is realized, traceable data is provided for long-term follow-up visit, the standardization and reliability of clinical monitoring are greatly improved, and a precise basis is provided for diagnosis and treatment.

The application discloses a carburized gear inner oxidation rating method and system based on deep learning, and belongs to the technical field of carburized gear quality detection, and comprises the following steps: obtaining a scanning electron microscope image of a carburized gear and preprocessing, carrying out anisotropic diffusion filtering denoising, and utilizing morphological operation to segment an inner oxidation organization area; based on a gray level co-occurrence matrix and a local binary pattern fusion, extracting texture features, and screening and optimizing feature vectors through a Pearson correlation coefficient; inputting the feature vectors into a support vector machine classification model optimized by a particle swarm, and obtaining an inner oxidation degree preliminary classification result; combining an inner oxidation comprehensive evaluation system, outputting a final quantitative rating result through probability threshold judgment, deviation matrix linear analysis correction, and service condition and material characteristic matching; and the application realizes the automation, standardization and precision of the carburized gear inner oxidation rating, and provides scientific support for gear quality detection and service safety guarantee.