13 results about "Morphological operators" patented technology

The embodiment of the invention provides an edge detection method and device based on gradient weighted fusion and an adaptive threshold, and is applied to the field of computer vision and digital image processing. The method comprises the steps of firstly preprocessing an input image, then extracting two groups of gradient magnitude diagrams and directional diagrams through an adaptive morphological operator and a traditional difference operator, and constructing a weighted fusion function according to the gradient direction consistency of each pixel point to obtain a fused gradient magnitude diagram; and adaptively determining a high threshold and a low threshold based on an information entropy maximization principle, executing an improved Canny process by combining the fused gradient magnitude diagram and the second gradient directional diagram to obtain an initial edge diagram, and outputting a final edge detection result after dynamic structure element optimization. In this way, the defects that in a traditional edge detection method, gradient information extraction is not precise, threshold selection lacks adaptability, and an edge result is fractured can be overcome, more robust and more accurate image edge detection is achieved, and the reliability of an edge detection algorithm in a complex image scene is improved.

The invention discloses a ceramic microstructure defect intelligent identification system based on morphological image analysis, and relates to the technical field of ceramic material quality detection, and the system comprises a multi-scale morphological feature extraction engine, a defect morphological feature analysis module, a deep semantic enhancement module and a defect intelligent identification decision module. The multi-scale morphological feature extraction engine adopts an adaptive rotation morphological operator group to carry out multi-direction morphological operation, a multi-scale morphological gradient pyramid is generated and morphological feature vectors are extracted, the defect morphological feature analysis module carries out feature coding and classification through a morphological feature encoder to generate defect morphological descriptors, and the defect morphological descriptors are subjected to feature extraction; and the deep semantic enhancement module extracts semantic features under the guidance of morphological feature coding, performs feature fusion, generates operator optimization parameters and feeds the operator optimization parameters back to the feature extraction engine to form closed-loop optimization, and the system realizes deep fusion of morphological features and deep semantic features and adaptive operator optimization.

The application discloses a global mesoscale frontal automatic identification method based on a sliding window threshold value, and comprises the following steps: performing pretreatment operation of erosion missing value and noise filtering on input data; calculating frontal intensity according to an improved Sobel operator; marking a candidate frontal area according to a sliding window threshold value; obtaining final frontal area product data by using multiple mathematical morphological operators; forming a candidate frontal line according to multiple direction frontal intensity extreme values and a mask of the frontal area product; and finally obtaining frontal line product data by using multiple mathematical morphological operators. The method provided by the application is not susceptible to noise, is not sensitive to absolute gradient intensity, can identify mesoscale fronts missed by existing algorithms, solves the problem that traditional marine frontal identification methods are sensitive to absolute gradient and cannot identify weak fronts, and the identified frontal has better continuity, and has good applicability to global or regional sea areas.

The invention discloses a power distribution network high-resistance grounding fault detection method based on partial differential morphology, and the method comprises the steps: defining a partial differential morphology optimization model based on the idea of a partial differential equation, simulating a morphological evolution process of a signal through a nonlinear partial differential equation, and achieving the processing and analysis of the local features of the signal. The method gets rid of dependence on discrete structure elements, and converts mathematical morphology operation into a continuous process driven by a partial differential equation, so that signal processing is more flexible and has physical significance. On the basis, a partial differential morphological operator and a discretization implementation method thereof are defined, a partial differential morphological spectrum concept is introduced, and a theoretical tool is provided for signal multi-scale analysis. The method is applied to high-resistance grounding fault detection of the power distribution network, and the robustness and accuracy of a detection system in a low-sampling-frequency and strong-noise environment are remarkably improved through partial differential morphological spectrum analysis.

The application discloses a land surveying method based on aerial image analysis and relates to the technical field of land surveying. The application solves phase jump error caused by sudden deformation of frozen soil through a quantum optimization framework, significantly improves the continuity of the deformation field, simultaneously fuses a classifier designed according to the temperature-elastic modulus attenuation law, enhances the recognition ability of the thermal driving deformation, combines an edge computing architecture with a parameter self-adaptive mechanism, realizes minute-level deformation early warning response, and further introduces a cross-modal fusion strategy to expand the monitoring dimension, effectively compensates for the scene limitation of a single sensor, and simultaneously supports a derivative application such as illegal building detection through a capsule network and a differentiable morphological operator.

This invention discloses a method for detecting high-resistivity grounding faults in distribution networks based on partial differential morphology. Based on the concept of partial differential equations, a partial differential morphological optimization model is defined. This model simulates the morphological evolution of signals through nonlinear partial differential equations, enabling the processing and analysis of local signal features. This invention eliminates the dependence on discrete structural elements, transforming mathematical morphological operations into a continuous process driven by partial differential equations, making signal processing more flexible and physically meaningful. Furthermore, partial differential morphological operators and their discretization implementation methods are defined, and the concept of partial differential morphological spectrum is introduced, providing a theoretical tool for multi-scale signal analysis. Applying the proposed method to high-resistivity grounding fault detection in distribution networks, the partial differential morphological spectrum analysis significantly improves the robustness and accuracy of the detection system in low sampling frequency and high-noise environments.

The application discloses a kind of based on morphological extraction point cloud local surface feature method, steps are as follows: S1 acquires point cloud data and carries out data pretreatment to point cloud data;S2 searches the neighborhood point of each point of point cloud in three-dimensional space, calculates normal information, then based on normal information, the curvature of each point is calculated, and curvature value is obtained;Then refer to the curvature value range corresponding to local surface feature and filter out target surface;S3 uses progressive morphological filter to adjust filter parameter constantly, according to the curvature change of selected local area, the size of filter window is adjusted accordingly, non-target point in point cloud data after curvature screening is filtered out, and the point of target local area is extracted;S4 uses dilatation, corrosion three-dimensional morphological operator and dilatation corrosion combination open operation and close operation to optimize target local area, and extract the local surface feature area of complete smooth point cloud data.The method is extracted to local area with obvious curvature change, high accuracy, and wide adaptability.

The invention relates to the technical field of image processing, and particularly discloses a draw shaft repairing operation auxiliary system based on point cloud, which is used for solving the problem that when the point cloud and images of the wall of a draw shaft are blocked by dust and influenced by reflective stripes and holes lacking measurement, hole filling by a fixed morphological operator is easy to cross a real boundary or is insufficient in propagation. And morphological hole filling errors of the point cloud image are caused. Comprising a point cloud image data acquisition module and a data analysis module, self-adaptive morphological hole filling correction is carried out in the data analysis module in combination with draw shaft point cloud data features, and point cloud image morphological hole filling errors are corrected; through design drawing reference registration, draw shaft feature deep extraction and three-time recommendation constraint self-adaptive morphological correction, point cloud image morphological hole filling correction can still be stably carried out on point cloud under the conditions of dust shielding, reflective stripes and missing measurement holes, and point cloud image hole filling errors caused by hole filling crossing a real boundary or insufficient propagation are avoided.

Lithograph mask generation processes that apply optical lithography simulation on a mask image to generate a resist image, perform curvilinear design retargeting on the resist image, determine a gradient from the generated resist image and a configured target resist image, and apply morphological operators and the gradient to update the mask image.

The invention relates to an extrusion-blow molding product quality monitoring method based on machine vision, which comprises the following steps: acquiring a sensor trigger signal, and performing image acquisition when a sensor is triggered to obtain an extrusion-blow molding product image; carrying out image enhancement and image correction on the extrusion blow molding product image to obtain a preprocessed image; performing edge detection on the preprocessed image based on a morphological operator to obtain a coarse positioning edge, and performing sub-pixel edge contour extraction on the coarse positioning edge to obtain a final edge; performing product quality monitoring based on the final edge to obtain a monitoring result; and carrying out visual display on the monitoring result. According to the invention, online non-contact detection can be realized; and during detection, through a preset image processing mechanism, the detection efficiency can be improved on the premise of ensuring the accuracy of the detection result, so that full-automatic quality monitoring of a large batch of products can be realized.

This invention relates to the field of image processing technology, specifically disclosing a point cloud-based auxiliary system for well repair operations. This system addresses the problem that fixed morphological operators for patching holes in well walls can easily cross the true boundary or fail to propagate sufficiently, leading to morphological hole patching errors. The system includes a point cloud image data acquisition module and a data analysis module. The data analysis module incorporates the characteristics of the well point cloud data for adaptive morphological hole patching correction, correcting these errors. Through design drawing reference registration, deep extraction of well features, and adaptive morphological correction with three-dimensional recommended constraints, the system ensures stable point cloud image morphological hole patching correction even under conditions of dust obstruction, reflective stripes, and missing holes, preventing errors caused by hole patching crossing the true boundary or insufficient propagation.

The application aims to provide a rolling bearing fault diagnosis method based on morphological product envelope spectrum, and belongs to the field of bearing fault diagnosis. Different order median filters are used to carry out noise reduction processing on the vibration signal; then, the minimum amplitude of the waveform between adjacent local minimum points in the noise-reduced signal is set to zero to construct an adaptive enhanced time-varying structure element. A morphological gradient operator is used to carry out morphological filtering on the original signal in combination with the constructed adaptive enhanced time-varying structure element, and the envelope spectrum of the filtered signal is calculated. Meanwhile, an improved characteristic frequency intensity coefficient is proposed to adaptively select the optimal order of the median filter, and then the envelope spectrum corresponding to the morphological operator is obtained. The application can effectively suppress the interference of background noise by constructing the morphological product envelope spectrum, and the improved characteristic frequency intensity coefficient can effectively represent the information richness of bearing faults.

This invention discloses a method for outlier detection in multi-beam data based on adaptive progressive morphological filtering. The method employs a closed-open-open combined average morphological operator for filtering, extracting a local terrain slope tolerance parameter. Based on this parameter, the elevation threshold is adaptively and dynamically adjusted during iterative morphological filtering. Through multi-level filtering windows and iterative calculations, the morphological reference plane gradually approximates the actual seabed until the iteration termination condition is met. Then, the terrain slope is extracted and a global judgment threshold is calculated. The depth deviation between the actual measured depth and the reference elevation is compared based on spatial location, thereby achieving accurate removal of outliers from the original discrete point cloud. By establishing a local dynamic adjustment and global decision mechanism for the filtering threshold, the method effectively avoids the filtering and under-filtering defects caused by fixed parameters, improving denoising accuracy while ensuring high fidelity of the actual seabed micro-topography.