17 results about "Contourlet" patented technology

The invention discloses a device and a method for accurately acquiring working face abnormal body information by a multi-frequency ground penetrating radar. The device comprises a multi-frequency data acquisition module, a data alignment module, a data fusion module, a data management module, a model training optimization module and an abnormal body information identification module. The method comprises the following steps: acquiring ground penetrating radar data of different center frequencies of a target by using a multi-frequency ground penetrating radar; preprocessing the original multi-frequency ground penetrating radar data; performing spatial alignment on the preprocessed ground penetrating radar data; fusing the ground penetrating radar data through improved non-subsampled contourlet transform (NSCT) to obtain a multi-frequency ground penetrating radar fusion profile; fused images are collected to make a ground penetrating radar image data set; and importing an improved YOLOv11 model to perform underground anomalous body identification and marking on the ground penetrating radar image data set. Through multi-frequency data complementation and deep learning optimization, anomalous body detection precision and efficiency are significantly improved, and the method is especially suitable for geological exploration of complex underground environments such as a coal mine.

The invention relates to the technical field of power cable intelligent monitoring, and aims to solve the technical problems that in an existing cable state monitoring system, multi-source heterogeneous data fusion is insufficient, the coupling relation between physical quantities is difficult to reveal, and insulation aging, current-carrying capacity and fault probability cannot be quantitatively predicted according to evaluation results. The invention discloses a cable state multi-source data fusion and prediction system. The system comprises a data acquisition module, a feature processing module, a prediction analysis module and a cloud management platform. The system collects partial discharge, surface temperature and vibration data of the cable through the distributed terminal; performing spectrogram decomposition and fusion of the multi-source data by adopting a non-subsampled contourlet transform algorithm to generate multi-scale fusion features; building a prediction model based on a particle swarm optimization least square support vector machine, and outputting an insulation aging state prediction value, a current-carrying capability evaluation value and a fault probability prediction value of the cable; and data management and full-life-cycle service are realized through the cloud platform.

The invention discloses a tunnel water leakage detection method based on refrigeration type infrared thermal imaging. The method comprises the following steps: obtaining a temperature value two-dimensional matrix; traversing each part of original temperature field data, and extracting a feature threshold value; calculating a second-order gradient field by adopting an eight-direction Laplacian operator; extracting a second-order temperature gradient characteristic average value of the low-temperature domain; selecting a second-order temperature gradient upper limit and a second-order temperature gradient lower limit as leakage water quantitative screening criteria based on temperature field second-order gradient characteristics; selecting data meeting quantitative screening criteria, and preliminarily rejecting non-leakage data; for the data meeting the quantitative screening criterion, dividing dry and wet areas of the data by taking the dry and wet boundary temperature as a temperature contour line, and drawing; data which cannot be eliminated through a single temperature field identification mode and contains a regular-shape non-leakage area are eliminated; and outputting the data information containing the leakage water and a corresponding leakage water segmentation result image. The method has the advantage that the tunnel leakage water disease identification accuracy is improved.

The application discloses a GIS partial discharge pattern recognition method based on polar coordinate distribution entropy optimization, and belongs to the field of large power grid on-site power equipment defect diagnosis and recognition. Firstly, aiming at the signal missing phenomenon of photoelectric partial discharge signals, a non-subsampled contourlet transform is used to fuse and process photoelectric PRPD graphs, so that photoelectric fusion graphs are obtained. Then, feature points and scale vectors of the fusion PRPD graphs are extracted based on a KAZE algorithm. Next, the feature points are dispersed to polar coordinate expressions according to coordinates and scale vectors. Distribution entropy of each region divided on the polar coordinates is calculated to form a feature vector. Finally, the feature vector is loaded into a long short-term memory network optimized by an Adaboost algorithm to verify the accuracy of partial discharge pattern recognition. Compared with a statistical parameter method and a KAZE feature extraction method, the algorithm can better extract a PRPD graph feature vector, and the accuracy of partial discharge defect pattern recognition is significantly improved.

The invention discloses a communication line insulator fault detection method and system, and belongs to the technical field of communication line maintenance, and the method comprises the following steps: collecting an image of a communication line insulator; the method comprises the following steps of: preprocessing an acquired original image on the basis of a complex algorithm combination of non-subsampled Contourlet transformation and grey wolf optimization algorithm optimization guide filtering-Retinex; carrying out edge extraction on the preprocessed image; comprehensively extracting geometric and textural features of the insulator from an edge image obtained by edge detection, wherein the geometric and textural features comprise an edge feature, a textural feature and a contour feature; inputting the traditional RGB channel data and the extracted edge feature, texture feature and contour feature vectors into a DeepLabv3 + semantic segmentation model, and identifying an insulator fault; and outputting a fault identification result. According to the invention, rapid and accurate detection of the communication line insulator fault is realized, the maintenance efficiency and safety of the communication line are improved, and the maintenance cost is reduced.

The invention discloses a raft culture area extraction method based on a high-resolution optical image, and the method comprises the following steps: screening out a preprocessed high-resolution optical image of a target culture area, fusing R, G and B wave bands and a panchromatic wave band through discrete wavelet transform, and obtaining a high-resolution RGB fusion image; performing image enhancement processing on the RGB fusion image and the panchromatic image by using a non-subsampled contourlet transform mode to obtain an enhanced RGB fusion image; inputting the enhanced RGB fusion image and the initial breeding raft into an SAM model to carry out annotation guided prompt training, generating a breeding raft prompt mask and a candidate mask, and reserving a highly similar area of the prompt mask and the candidate mask as a pseudo breeding raft; evaluating the initial breeding raft and the pseudo breeding raft, making raft breeding area verification samples after the precision is met, and realizing automatic parameter adjustment of topology rules by using a genetic algorithm based on the verification samples so as to achieve parameter optimization.

The invention discloses a mineral target area delineation method based on electrical resistivity and polarizability image fusion, which comprises the following steps of: firstly, preprocessing actually measured electromagnetic data of an exploration area, including data review, gridding registration and image normalization, so as to respectively obtain a standardized electrical resistivity image and a standardized polarizability image; decomposing the two source images into low-frequency sub-band images and high-frequency sub-band images by using non-subsampled contourlet transform; fusing the low-frequency sub-band image and the high-frequency sub-band image by using a fusion rule based on weighted average and a new measurement parameter; and finally, a fusion image is obtained through NSCT inverse transformation, and the fusion image is compared with prior geological data to delineate a prospecting target area. Through a multi-scale image fusion technology, complementary information of resistivity and polarizability is effectively integrated, the accuracy and reliability of mineralization abnormal area identification are remarkably improved, and a new technical means is provided for mineral exploration.

The application discloses a brain image fusion method based on convolution sparse representation, which takes an adaptive convolution sparse algorithm as a fusion framework, takes brain images as training samples to obtain a pre-training dictionary, and takes the brain images as test samples to participate in a fusion process; a non-subsampled contourlet transform (NSCT) is adopted to construct a new activity measure in combination with sub-band correlation; finally, a fused brain image is obtained through two-scale reconstruction; through the above method, the influence of local mismatch and texture structure loss on convolution sparse brain image fusion can be effectively improved, and the time consumption of the algorithm can be significantly reduced.

The application discloses a NSCT domain sonar image denoising method and device based on neutral set and bilateral filtering, and belongs to the technical field of sonar image denoising. In order to solve the problems of poor denoising effect, insufficient edge preservation ability and low operation efficiency of the existing image denoising method, the application first decomposes a noisy image based on a non-subsampled contourlet transform, then converts high-frequency subband images to a neutral set domain, uses a true subset, a false set and an uncertain set for representation, performs beta enhancement on the image of the true subset, and performs bilateral filtering processing on the enhanced true subset image; meanwhile, the low-frequency subband image is converted to the neutral set domain, the true subset image is extracted as a guide image of joint bilateral filtering, and the joint bilateral filtering is guided to perform smoothing processing on the low-frequency subband image; the NSCT inverse transform is used to reconstruct the high-frequency subband image after the bilateral filtering processing and the low-frequency subband image after the joint bilateral filtering processing, so that the finally denoised image is obtained.

The application discloses a radar signal recognition method based on time-frequency image feature fusion, and mainly solves the problem of low radar signal recognition rate in the prior art under a low signal-to-noise ratio environment.The steps of the application are as follows: (1) time-frequency transformation is performed on a radar signal to obtain a time-frequency image; (2) time-frequency image pre-processing; (3) texture feature and shape feature extraction of the image; (4) support vector machine is used to recognize the radar signal.The application extracts the texture feature and shape feature of the radar signal based on the Choi-Williams time-frequency image and the contour map of the fuzzy function, and fuses the two to form a new radar signal recognition characteristic value, thereby solving the problem of low radar signal recognition rate under a low signal-to-noise ratio.

The invention discloses a sound source position and frequency joint characterization method in acoustic imaging, and belongs to the field of microphone array signal processing, and the method comprises the steps: collecting time domain data through a microphone array, converting the time domain data into a plurality of sub-band data through short-time Fourier transform, and constructing an observation model; establishing a sparse Bayesian learning model based on the observation model, performing probability modeling on noise and signals, and setting prior distribution; utilizing array data to iteratively update model parameters until convergence; acquiring a position-frequency energy matrix according to the convergence parameter; a sound source position is determined according to a non-zero line in the position-frequency energy matrix, a frequency sequence is generated for the sound source position, a joint visual graph in a concentric circle or contour line form is constructed, each geometric layer corresponds to a sub-band, and a visual attribute represents an energy amplitude; and finally, superposing the visual graph and the video image. According to the invention, joint visual display of the sound source position and the frequency is realized, and frequency characteristic monitoring and sound source distinguishing are supported.

This invention discloses a device and method for accurately acquiring information on anomalies at a working face using multi-frequency ground-penetrating radar (GPR). The device includes a multi-frequency data acquisition module, a data alignment module, a data fusion module, a data management module, a model training and optimization module, and an anomaly information identification module. The method involves: acquiring GPR data at different center frequencies of the target using multi-frequency GPR; preprocessing the raw multi-frequency GPR data; spatially aligning the preprocessed GPR data; fusing the GPR data using an improved non-subsampled contourlet transform (NSCT) to obtain a multi-frequency GPR fused profile; collecting the fused images to create a GPR image dataset; and importing an improved YOLOv11 model to identify and label underground anomalies in the GPR image dataset. This invention significantly improves the accuracy and efficiency of anomaly detection through multi-frequency data complementarity and deep learning optimization, making it particularly suitable for geological exploration in complex underground environments such as coal mines.

The invention discloses a whole-body SPECT lung cancer bone imaging data processing method and system, and relates to the technical field of image processing, and the method comprises the steps: inputting whole-body SPECT lung cancer bone imaging data into a trained improved UNet network, extracting local texture features through a similar contourlet decomposition module, fusing the local texture features with an original image, and obtaining a whole-body SPECT lung cancer bone imaging data fusion model; inputting the fusion features into a UNet encoder to extract image features; and acquiring a lung cancer bone metastasis focus area by using a UNet decoder added with a contourlet feature splicing and reconstruction strategy for the extracted image features. According to the invention, the feature extraction capability of the bone imaging lesion segmentation method under the conditions of low resolution and fuzzy boundary can be improved.

The present invention discloses an interactive image segmentation method based on multi-scale geodesics, comprising the following steps: step S100: inputting a grayscale image I: Ω→R, wherein Ω is the image domain of the grayscale image I and R is a real number space; adding noise to the grayscale image I to obtain a noisy grayscale image I0; step S200: performing pyramid decomposition on the noisy grayscale image I0 using a contourlet transform; step S300: performing threshold processing on the contourlet coefficients to reconstruct a denoised image I n (n≥2, n∈Z); Step S400: Calculate the denoised image I n The segmentation results of geodesics at different scales are shown in Figure 2. The multi-scale strategy and contourlet threshold are used in the minimum path model to reduce the influence of noise on image segmentation; and the computational complexity of the minimum path model is reduced by finding geodesics from coarse to fine.

The invention relates to the technical field of image sharpening, in particular to an archive digital image processing method and system.The method comprises the following steps that according to archive page scanning data, a pyramid filter bank and a direction filter bank are called to execute non-subsampled contourlet decomposition, and a multi-scale direction sub-band coefficient set is generated; and dividing the multi-scale direction sub-band coefficient set into overlapped sliding processing units according to preset step length and size parameters, and generating a coefficient sliding window block set. According to the invention, while the sharpness and contrast of the stroke edge are enhanced, the background region coefficient is maintained or suppressed, and synchronous amplification of noise is avoided. And inverse transformation reconstruction is performed on the direction coefficient set subjected to texture adaptive enhancement processing and the low-frequency component, and stroke details and background intensity in the multi-direction feature data stream are fused, so that a restored image with high definition and historical texture can be output, and the visual readability and subsequent character recognition accuracy of a digital archive finished product are improved.

The application provides an irregular fragment mass inversion method based on an event camera, first, binocular event stream data are subjected to density clustering, event clusters corresponding to each fragment are divided, and a centroid trajectory is tracked; then, the event clusters are converted into a spatial density map, discrete contour point sets are obtained by extracting closed contour lines; subsequently, significant corner points are screened in the point sets, are arranged according to the trend, and a normalized edge length sequence is calculated, so that a contour topological feature is generated; then, based on the feature, multiple frame contours of the same fragment are matched between continuous frames, and the largest area is selected as the optimal single-frame contour; finally, the equivalent image plane diameter is converted from the area of the optimal contour, the real physical size is calculated in combination with the depth and the focal length, and the mass of the fragment is finally inverted according to the preset thickness and the material density.

The present application belongs to the field of image fusion, and particularly discloses a polarization spectrum image fusion method based on non-subsampled contourlet transform. The method comprises the following steps: S1, acquiring four spectral images and intensity images I of different linear polarization angles (0°, 45°, 90° and 135°) through a polarization spectrum imaging system; S2, calculating the corresponding Stokes parameters of the images to obtain corresponding Q, U and DoLP images; S3, decomposing the Q and I images by using non-subsampled contourlet transform, taking the mean value of the low-pass subbands, and preliminarily fusing the high-pass subbands by using the maximum amplitude rule to obtain a polarization characteristic S; and finally, decomposing the S image and the intensity image by using non-subsampled contourlet transform, fusing the low-pass subbands obtained by using an improved pulse-coupled neural network method, and fusing the high-pass subbands by using a weighted fusion rule based on local mode spectrum characteristics.