17 results about "Anisotropic diffusion filter" patented technology

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 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 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 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 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 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.

This application provides a method and apparatus for seismic identification of volcanic structure phase interfaces. The method includes: acquiring raw seismic data; calculating the dip angle and azimuth angle of the strata based on the raw seismic data; performing anisotropic diffusion filtering on the raw seismic data under the control of the dip angle and azimuth angle to obtain a structure-guided filtered data volume; extracting seismic reflection coefficient data from the structure-guided filtered data volume using inversion technology to obtain a seismic reflection coefficient data volume; and performing seismic identification and tracking interpretation of volcanic structures and phase interfaces based on the seismic reflection coefficient data volume. By performing structure-guided filtering and reflection coefficient inversion processing on the raw seismic data, the signal-to-noise ratio of seismic data of deep volcanic rocks is effectively improved. The resulting seismic reflection coefficient volume highlights the seismic reflection structural characteristics of volcanic structures, effectively improving the seismic identification capability and interpretation accuracy of deep volcanic structures and phase interfaces.