47 results about "Gabor transform" patented technology

The invention provides a facial expression recognizing method based on Gabor transform optimal channel blur fusion, comprising the following steps of: S1, dividing a Gabor filter into 11 channels, carrying out Gabor wavelet transform on a facial expression image subjected to normalization by the Gabor filter to extract texture characteristics of the facial expression image; S2, establishing classifiers corresponding to the channels one by one, respectively sending the texture characteristics extracted from the channels into the classifiers for classifying and recognizing, calculating the recognition rate and the definition of each classifier to various expressions, selecting 4 classifiers with top comprehensive ranking of the recognition rate and the definition to be used as optimal classifiers; and S3, carrying blur fusion on the four optimal classifiers to obtain a facial expression recognition result. The facial expression recognition method has the advantages of small calculated amount, high calculating speed and high recognition precision.

The present invention teaches a system and method for improved signal recovery for range and coverage extension in a heterogeneous cooperative network of digital chaos transmissions with OFDM component signal transmission. The invention improves upon the state of art in side channel information from the transmit side containing information on the clipped amplitude. In-band transmission of the side information is achieved by exploiting the sparsity of the resulting clip amplitude position with improved levels of compression over the prior art using Gabor Transform Multiple Symbol Encoding transmitter. The information rate of the clipped amplitude is sub-Nyquist relative to the original OFDM component signal transmission, which allows very low power spreading by a cooperative digital chaos sequences at a transmit side and recovery of the clipped amplitude at a receive side. Further, an improved noise resistance side channel performance is achieved by decoding Gabor Transform symbols for symbol recovery.

A signal analysis system and method for analyzing an input signal acquired from a mechanical system. The mechanical system may include at least one rotating apparatus. The signal analysis system may be configured to: (a) receive samples of the input signal, (b) perform an invertible joint time-frequency transform (e.g. a Gabor transform) on the samples of the input signal to produce a first array of coefficients which depend on time and frequency, (c) compute an instantaneous rotation frequency signal, (d) select first coefficients from the first array which correspond to one or more order components in the input signal, i.e. one or more multiples of the instantaneous rotation frequency signal, (e) generate a time domain signal from the first coefficients, and (f) present the time domain signal to a user on a presentation device. The signal analysis system may generate the time domain signal from the first coefficients by performing an inverse joint time-frequency transform on the first coefficients.

A signal analyzer, method and memory medium for generating a time varying spectrum for input signals characterized by frequency components which change in time. The signal analyzer includes a source of a sequence of digital signals representative of an input signal, a processor coupled to the source, and a memory medium coupled to the processor. The memory medium stores a software program which is executable by the processor to compute the time-varying spectrum of the input signal. When the processor executes the software program, the processor is operable to first compute a Gabor transform (that is, a sampled short-time Fourier transform) of the digital signals to produce Gabor coefficients. The processor then computes a two dimensional auto-correlation of the Gabor coefficients to produce auto-correlation results. The auto-correlation results are then applied to a 2-dimensional fast interpolation filter to produce the time-varying spectrum, wherein the time-varying spectrum is a Gabor spectrogram. The signal analyzer may repeat the above steps n+1 times, based on the order determined by a user, and sum the results for an n order time-varying spectrum. The process more may then operate to process and / or display the time-varying spectrum.

The invention provides a GrabCut texture image segmentation method based on Gabor transform. The method comprises the following steps that: transforming a two-dimensional image into frequency domain space representation through Gabor wavelet transform, calculating the parametric transform of a frequency spectrum space two-dimensional Gabor filter group to obtain multi-scale multi-direction texture features, constructing a multi-scale texture energy generic function, calculating the GMM (Gaussian Mixture Model) probability distribution model of the texture features, and introducing the GMM of the texture features into GrabCut for image segmentation. The GrabCut texture image segmentation method based on the Gabor transform has the characteristics of being simple in artificial interaction, high in processing speed, good in image segmentation effect and the like.

The invention discloses a seismic wave compensation method constructed through a frequency driving space-variant Q value model. A time frequency spectrum corresponding to each sample point of time domain seismic data is generated through Gabor transformation, center frequencies are calculated point by point, and attenuation values Q<-1> are estimated through the center frequencies. Quality factor Q values are reciprocals of the attenuation values Q<-1>, a Q value model constructed therefore is continuous and spatially variant, and the calculation process is stable and highly efficient. Based on this, high-precision seismic wave attenuation compensation is realized, the problems of amplitude attenuation and phase distortion of seismic data are solved, and the resolution and signal-to-noise ratio of the seismic data are improved.

A system for processing an image data volume acquired with a medical imaging acquisition device 100 from a body comprising a needle is provided. It has been realized it is advantageous to display 150 a plane intersecting the image data volume showing the needle to a user of the system. This allows better positioning of the needle. The system comprises a needle-plane determination 110 module for determining a plane being parallel to and intersecting a representation in the image data volume of the needle and being parallel to a viewing direction. The needle plane determination module may make use of pixel processing and / or spectral transformation, in particular the Gabor transform.

A system for processing an image data volume acquired with a medical imaging acquisition device 100 from a body comprising a needle is provided. It has been realized it is advantageous to display 150 a plane intersecting the image data volume showing the needle to a user of the system. This allows better positioning of the needle. The system comprises a needle-plane determination 110 module for determining a plane being parallel to and intersecting a representation in the image data volume of the needle and being parallel to a viewing direction. The needle plane determination module may make use of pixel processing and / or spectral transformation, in particular the Gabor transform.

The invention provides a facial expression recognizing method based on Gabor transform optimal channel blur fusion, comprising the following steps of: S1, dividing a Gabor filter into 11 channels, carrying out Gabor wavelet transform on a facial expression image subjected to normalization by the Gabor filter to extract texture characteristics of the facial expression image; S2, establishing classifiers corresponding to the channels one by one, respectively sending the texture characteristics extracted from the channels into the classifiers for classifying and recognizing, calculating the recognition rate and the definition of each classifier to various expressions, selecting 4 classifiers with top comprehensive ranking of the recognition rate and the definition to be used as optimal classifiers; and S3, carrying blur fusion on the four optimal classifiers to obtain a facial expression recognition result. The facial expression recognition method has the advantages of small calculated amount, high calculating speed and high recognition precision.

The invention discloses a method for analyzing the surface texture of a carbon fiber composite material based on Gabor conversion of a frequency spectrum method. The method comprises the following steps: carrying out Butterworth filtering pretreatment on a surface image of an original carbon fiber material; carrying out Gabor conversion on the pretreated image, so as to obtain a filtered image Gabout; converting the filtered image into a frequency domain; and carrying out normalization processing on a matrix to obtain a grey level histogram of the matrix, and calculating entropy value characteristics of the image by virtue of the grey level histogram. According to the method, the entropy value characteristics of the carbon fiber composite material can be extracted at different scales and directions in the frequency domain, and the entropy value of the carbon fiber composite material extracted by a Gabor function can be taken as variation basis of mean square root height Sq values, andbesides, relatively fine texture information among approximate Sq values can be further extracted, so that the mean square root height Sq values measured by virtue of a machine can be objectively assisted.

The invention discloses a deep learning face recognition method based on multi-feature fusion. First, two-dimensional gabor transformation (2D gabor) is performed on the orl face database to be tested, and the face database with gabor features is extracted; due to the size of the picture It is: 92×112, the size here is relatively large, and the bilinear interpolation method is used to reduce the image to 32×32; then the original orl face database is fused with the face database of gabor features; finally, the depth is used The stacked self-encoding method in the study is used to encode, and the weight parameters are calculated by softmax regression to predict the recognition accuracy. After the present invention integrates multiple features, the premise is that the accuracy rate of this feature alone can not be lower than 80%, the accuracy rate will be improved, and the algorithm will be more stable, that is, after random initialization, the recognition accuracy rate will basically remain unchanged.

This specification provides a processing method, device and server for phased seismic data. Among them, based on the above method, the acquired reflection seismic data of the target area are decomposed by Gabor transform-based sparse constrained inversion spectrum to obtain the corresponding time-frequency amplitude spectrum data and time-frequency phase spectrum data; and then in the frequency domain On the basis of the preset phase error limit, the time-frequency amplitude spectrum data and the time-frequency phase spectrum data, perform phase-by-phase reconstruction to obtain the data body after phase-by-phase reconstruction; The phase-reconstructed data volume is inversely processed by Gabor transform-based sparse constrained inversion spectral decomposition to obtain phase-separated seismic data that meet the requirements. Therefore, phase-separated seismic data containing specific phase component information can be obtained that is more accurate and has high seismic interpretation value, and then the above-mentioned phase-separated seismic data can be used to more precisely describe the reservoir characteristics of the target area.