8 results about "Gabor wavelet" patented technology

The present application relates to the technical field of fault type identification acceleration, in particular to a kind of multi-modal sound feature fusion's fluid transmission equipment fault classification acceleration method and system;After sound sensor collects fluid transmission component operation sound signal, pre-processing is removed interference noise;Time domain feature extraction module extracts multi-scale space-time feature by multi-scale time convolution fusion unit;Frequency domain feature extraction module is converted by FFT, amplitude and phase separation processing and IFFT restoration, and time domain feature after frequency domain processing is obtained;Dual-domain feature fusion module fuses dual-domain feature by real Gabor wavelet function, and then excavates deep feature by Transform module;Fault diagnosis module outputs fault classification result by convolution, fully connected and Softmax function, and hardware platform outputs recognition accuracy by UART serial communication.This application detects comprehensively, responds quickly, has strong stability, low power consumption, good portability, can be adapted to different environments, identifies multiple fault types, and can also be extended to other related fields.

The invention discloses a near-fault multi-point multi-dimensional fully non-stationary seismic oscillation dimension reduction simulation method and system, and belongs to the technical field of seismic engineering and disaster prevention and reduction. The method provided by the invention comprises the following steps: firstly, screening near-fault strong earthquake records containing horizontal and vertical velocity pulses, fitting the extracted pulses by using a Gabor wavelet model, identifying low-frequency key parameters and establishing probability distribution; a high-frequency component is simulated by a 1D-nV non-stationary random vector process, and dimensionality reduction is carried out in combination with a random orthogonal function; and generating a representative point set through number theory point selection and equal probability inverse transformation, substituting the representative point set into the model to generate high and low frequency speed time histories, and superposing the high and low frequency speed time histories to obtain a seismic oscillation sample with complete probability information. The system comprises a data screening module, a parameter identification module and the like. According to the method, samples meeting the precision are generated by using extremely few random variables, the simulation efficiency is improved, and the method can be combined with the probability density evolution theory for engineering structure dynamic response and anti-seismic reliability analysis.

The application discloses a kind of needle textile surface defect visual detection methods, it is related to computer vision technical field, the method includes: real-time acquisition surface image and utilize adaptive band-stop filter to shield periodic texture to obtain residual image;Adopt multi-scale gabor wavelet to enhance tiny defect, construct multidimensional feature vector by fusing local binary pattern, grey level co-occurrence matrix and information entropy feature;Vector is input deep residual convolutional neural network to realize the classification identification and positioning of defect.The application significantly improves the detection sensitivity and identification accuracy of microscopic defects, has extremely strong complex environment adaptability and real-time processing capability, can match the demand of high-speed production line, realizes intelligent quality monitoring and closed-loop management in the production process of needle textile.

Disclosed is a Gabor wavelet-fused multi-scale local level set ultrasonic image segmentation method. In the method, non-uniformity of the grayscale of an ultrasonic image is taken as a texture having cluttered directions, the multi-directional property of Gabor wavelets is used to process the image, and intermediate images in different filtering directions are fused by taking maximum values, so as to obtain an intermediate image having a weakened texture effect and an enhanced difference between a foreground and a background. For the feature of a weak edge of an ultrasonic image, a concept of multi-scale is used to improve the conventional LIC method, Gaussian convolution kernels having different variances are set, and a final edge is obtained by means of average fusion.

The application discloses a large-amplitude motion estimation method, system, device and medium based on multi-scale phase video processing. The method is to down-sample the original image by using an image pyramid, reduce the image resolution of each scale layer by layer, use a Gabor wavelet as a filter for processing each scale image, and use the filter response amplitude as the confidence of phase estimation reliability; after constructing a phase-based motion constraint model, starting from the lowest resolution scale image, the least square fitting method is used to solve the optimal motion solution of the scale; the motion solution of the previous scale is used as the initial motion of the next scale, until the final motion solution is output. The significant effect of the application is that by constructing a layered phase unwrapping algorithm based on a Gaussian pyramid, the limitation of traditional amplitude is broken through, and high-precision tracking of large-amplitude motion is realized.

The invention relates to the technical field of traditional Chinese medicine intelligent diagnosis, and discloses a traditional Chinese medicine constitution classification and eruptive therapy feature and clinical curative effect correlation analysis system and method, and the system comprises a multi-modal data collection module, an eruptive therapy feature quantitative processing module, a constitution-eruptive therapy-curative effect correlation modeling module and a result output module. The multi-modal data acquisition module is used for acquiring data such as images (eruption images), signs and the like of people with different physiques after scraping therapy in a standardized manner; the eruptive image feature quantitative processing module is used for converting the eruptive image into quantitative features; the constitution-eruptive therapy-curative effect association modeling module is used for establishing an association model of eruptive therapy features, constitution and curative effects and realizing prediction; color (12-dimensional), form (8-dimensional) and texture (10-dimensional) features are fused, Lab color space, Gabor wavelet and other technologies are applied to acute therapy image analysis for the first time, the breakthrough of acute therapy image features from'qualitative 'to'quantitative' is achieved, the feature dimension and description precision are far better than those of the prior art, and 3-5-dimensional color features are mostly adopted in the prior art.

This invention relates to a method for 3D reconstruction of the material surface in electric shovels based on Gabor wavelet periodic activation function neural networks, belonging to the field of intelligent operation and industrial 3D vision technology in mining machinery. Multimodal data of the material pile is simultaneously acquired by a binocular camera and a LiDAR mounted on the shovel boom. After point cloud denoising and coordinate normalization preprocessing, an initial 3D point cloud is formed. Subsequently, a Gabor wavelet network with periodic activation functions is used as input to learn a continuous, differentiable implicit symbolic distance field. This invention can faithfully fit complex geometric surfaces of the material surface containing rich high-frequency details. It can quickly and faithfully reconstruct a 3D model of the material surface with rich details from sparse and noisy point cloud data collected on-site by the electric shovel, directly serving the key problem of optimal excavation trajectory planning. This achieves a closed-loop process from perception to planning for the electric shovel, significantly improving the intelligence level and operational efficiency of electric shovel operations.

The invention discloses an industrial furnace working condition mode recognition system based on image recognition. The system comprises an image acquisition module, an edge processing module, an image texture analysis module, a data association module and a working condition recognition module. The image acquisition module can acquire and preprocess source image data in the furnace through a near-infrared camera and a polarization filter, and then construct a thermal radiation distribution diagram reflecting material component differences; and the edge processing module can perform frequency domain decomposition of scale and direction on source image data through a Log-Gabor wavelet filter bank algorithm to obtain a pixel phase value. According to the invention, the image acquisition module adopts the combination of a near-infrared camera and a polarization filter, an image data basis is provided for subsequent texture analysis, and the edge processing module can extract closed continuous edge lines formed at the initial stage of crusting, so that the detection sensitivity of fine textures is improved, and the detection precision is improved. And meanwhile, a periodic structure of a crusting area and random textures generated by material flowing can be effectively distinguished.