9 results about "Morphological filter" patented technology

The present application belongs to the technical field of image recognition, and particularly relates to a liquid crystal screen defect detection method based on image analysis. The method comprises: acquiring a liquid crystal screen surface image frequency domain signal and wavelet packet decomposition, and extracting a low frequency and medium-high frequency coefficient matrix; performing convolution on the low frequency coefficient matrix with an adaptive morphological filter kernel, fitting to generate a uniform illumination reference matrix; after weighted correction and denoising of the medium-high frequency coefficient matrix, inverse wavelet packet transformation is performed on the medium-high frequency coefficient matrix and the uniform illumination reference matrix to generate a reconstructed difference image; the local pixel gradient direction vector and the gray level co-occurrence matrix contrast feature of the reconstructed difference image are input into an isolation forest classifier to output a defect position and category. The present scheme shifts illumination separation to the frequency domain, avoids damage to defect pixels by spatial domain filtering, eliminates non-uniform illumination interference, retains the edge gradient and texture structure of small defects, and solves the problem of missing detection of small dark spots under a gradual illumination background.

This invention belongs to the technical field of wear condition identification for hot forging press components, specifically relating to a feature signal enhancement method for wear condition identification of hot forging press components, including: S1, enhancing the feature signal x of inferior vibration signal x poor The repair signal x is obtained through processing. re S2. Construct a closed-open self-complementary top-hat morphological filter FCO-NSTH to enhance the characteristic frequency amplitude of the vibration signal to be enhanced; S3. Adaptively select the optimal structural element parameters of the closed-open self-complementary top-hat morphological filter using a particle swarm optimization algorithm; S4. Use the closed-open self-complementary top-hat morphological filter FCO-NSTH with adaptively selected optimal structural element parameters to enhance the repair signal x. re The characteristic frequency amplitude is enhanced to obtain an enhanced vibration signal; S5. The enhanced vibration signal obtained in S4 is input into the intelligent model to identify and predict the wear state of hot forging press components. This method can accurately identify the wear state of hot forging press components by combining with the intelligent model.

The application provides a method and related equipment for enhancing a while-drilling seismic signal. The method comprises: acquiring a while-drilling seismic signal; calculating a time-varying structure element according to a local variance of each trace of the while-drilling seismic signal; performing morphological dilation and corrosion operations at different scales for each time point and spatial trace of each while-drilling seismic signal to calculate a multi-scale morphological gradient value; constructing a weight matrix according to the multi-scale morphological gradient value; and performing weighted morphological filter iteration reconstruction on the while-drilling seismic signal based on the weight matrix to obtain an enhanced while-drilling seismic signal. The embodiment of the application realizes real-time enhancement of the while-drilling seismic signal by dynamic structure element generation and multi-scale morphological gradient weighting, and combines weighted morphological filter iteration reconstruction, thereby significantly improving the signal-to-noise ratio, retaining lithology interface reflection characteristics, suppressing noise and interference, and providing reliable technical support for underground coal mine seismic monitoring.

The application discloses a kind of fusion multi-scale morphological and adaptive energy threshold electrocardiosignal processing method and system, comprising: obtaining electrocardiosignal, utilize multi-scale morphological filter, to electrocardiosignal Parallel signal path calculation and baseline path calculation, the morphological reconstruction signal of weighted subtraction calculation result is obtained;By non-linear energy operator to morphological reconstruction signal is amplified, and energy sequence is obtained;Energy value in energy sequence is detected gradually, if the energy value is greater than main threshold, then directly confirm as R wave, and update main threshold;If the energy value is less than main threshold, judge distance last R wave time whether greater than 1.5 times average RR interval, if not greater than, then read next energy value;Otherwise, by backtracking search to obtain backtracking maximum value, judge backtracking maximum value whether greater than auxiliary threshold, if greater than, then mark as supplementary R wave;Otherwise, read next energy value;Based on confirmed R wave, output denoised electrocardiosignal waveform and heart rate parameter.

The present application relates to a kind of SAR data statistical analysis and adaptive target detection method and system, main steps are as follows: S100, data upload and pre-processing: receiving and verifying SAR data is pre-processed.S200, block and extraction: data is intelligently grid block processing.S300, multiple probability distribution fitting: using multiple probability distribution model fitting block data.S400, adaptive target detection: based on optimal distribution calculation adaptive threshold, combined with morphological filter and connected component analysis extraction target.S500, interactive visualization: the dynamic interactive visualization display of result is generated.The system fuses statistical modeling and constant false alarm detection to reduce complex sea conditions false alarm rate, support high-resolution data memory optimization and full-process automation.Through Web interface, improve ease of use, applicable to marine remote sensing and environmental monitoring etc.

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.