34 results about "Fractional differential" patented technology

The invention discloses a texture force touch sensing method based on single image fractional order processing. The method includes the steps that a fractional differential algorithm is utilized, the order gamma of the fractional differential algorithm is adjusted dynamically, an enhanced texture image is obtained, original image texture information is extracted from the enhanced texture image, and multi-scale analysis of the detail texture and the edge contour of different rates of gray level changes is achieved from the point of image airspace gray level change features; force touch rendering is carried out on the virtual surface texture, and tangential force frictional damping coefficients of a force touch sensing model are controlled on the basis of gradient vector distribution of the enhanced texture image; obtained resultant force is calculated and fed back to an operator through a hand controller. By the utilization of sensibility of fractional differential to detail information, the sense of reality of texture force touch sensing is enhanced by manually selecting orders, extracting texture information in a two-dimensional image signal from different scales and converting the texture information into frictional damping control.

The invention discloses a method for removing cosmic rays in a charge-coupled device (CCD) astronomic image. The method comprises the following steps of: (1) performing sub-sampling on an original image I to obtain a sub-sampled image; (2) improving a Laplacian operator, amplifying the sub-sampled image, performing convolution operation on the amplified sub-sampled image and the improved Laplacian operator, removing a negative value, and recovering an original size to obtain a Laplacian image L'; (3) identifying cosmic rays in the Laplacian image L'; (4) removing the cosmic rays in the Laplacian image L'; and (5) performing image edge enhancement processing on the image in which the cosmic rays are removed by utilizing a fractional differential normalized Tiansi operator. According to the method, the identification rate of the cosmic rays is greatly increased, a part influenced by the cosmic rays in a star can be maximally retained, and the processing quality of the image is improved.

The invention provides a fractional differential-based multi-feature combined sparse representation tracking method. The method includes the following steps: in a frame of particle filtering, first, performing partitioning processing on a target image region, dividing the target region into 9 related and unequal subblocks according to the features of the target region, extracting the gray scale feature and HOG feature of each subblock, combining the two features to perform sparse representation on a target subblock, and also performing the same feature extraction and sparse representation on 8 adjacent regions around the target; then, adopting a nucleating accelerated neighbor gradient algorithm to jointly solve sparse coefficients of 9 candidate particles; and finally, regarding target blocks in different positions as different categories, utilizing a block of the same category as a candidate particle block and a representation coefficient in a dictionary to reconstruct the block, and building a likelihood function according to a reconstruction error to determine an optimal candidate particle, thereby realizing accurate tracking of a main target and 8 auxiliary targets.

The invention discloses a corner detection method based on a non-causal fractional gradient operator, which belongs to the technical field of image processing. According to the invention, non-causal fractional gradient operation of a gray-scale image to be detected is realized through the combination of cause-effect and anti-causal fractional integral and cause-effect and anti-causal fractional differential. The method comprises the steps that an image is read to generate a gray scale matrix f (x, y); non-causal fractional gradients Dx and Dy of f (x, y) in x and y directions are calculated; the product DxDy of the gradient direction is calculated; Gaussian kernel is used to filter DxDy; the corner point intensity is calculated; and non-maximum suppression is carried out to acquire an accurate image corner. According to the invention, the novel algorithm based on non-causal fractional gradient is used to carry out gradient and corner energy operation; the corner detection precision is improved; the method is suitable for the computer vision fields of image registration and matching, image fusion, target recognition and the like.

The invention provides a fuzzy image super-resolution reconstruction method based on fractional differential. The method comprises the steps of (1) carrying out Gauss fuzzy and down sampling processing on an original image set in order, and obtaining an input image sequence, (2) selecting one arbitrary frame of input image from the input image sequence to carry out bicubic interpolation amplification, and obtaining an original reference frame, (3) using an adaptive fractional differential algorithm to carrying out image enhancement processing on each frame of input image in the input image sequence and the original reference frame, (4) calculating a motion matrix between each frame of input image and the original reference frame through SIFT matching, and searching a point in each frame of input image corresponding to a point in the original reference frame, and (5) calculating a residual error between the point in the original reference frame and the corresponding point in each frame of input image, through residual error inverse iterative projection correction, and the pixel value of the point in the original reference frame is constantly adjusted until a preset condition is satisfied.

The invention discloses a fractional order cell neural network adaptive synchronization control and circuit design method. A fractional differential algorithm is selected, and the definition of a cell neural network equation is combined, thereby constructing a three-dimensional cell neural network system. The method comprises the steps: designing a drive-response system with a known drive system nonlinear parameter and an unknown response system nonlinear parameter based on the above system, constructing a new adaptive synchronizing controller and a parameter adaptive adjustment rate, and achieving the synchronization of the drive and response systems in numerical simulation; designing the circuit diagrams of the drive and response systems of the fractional order cell neural network, and achieving the circuit simulation of the controller and the adaptive adjustment rate. A simulation result indicates that the circuit simulation and the numerical simulation have the similar synchronous phase diagram, and the method verifies the accuracy of system theoretical analysis and the actual physical realizability. Therefore, the method is practical in the field of engineering.

The invention relates to an adhesion hemocyte image segmentation method based on the improved fractional differential and graph theory.The method comprises the steps of pretreating a hemocyte image by combining morphological denoising with the improved quasi-circular mask operator fractional differential algorithm aiming at the phenomenon that the hemocyte image is fuzzy and low in contrast ratio, wherein by the adoption of the improved fractional differential algorithm, cell edge details are reserved while staining contamination and grain noise of the hemocyte image are filtered out; then conducting preliminary segmentation on the pretreated image with the watershed algorithm, and mapping an over-segmentation area into a node; finally conducting resegmentation on the cell image obtained in the second step with the improved graph theory minimum spanning tree (MST) algorithm.By the adoption of the method, segmentation precision of adherent cells in the cell image can be improved.

The invention provides a seismic horizon tracking preprocessing method based on a fractional derivative, which belongs to geophysical exploration field for petroleum and natural gas. The seismic horizon tracking preprocessing method comprises the following steps: (1) selecting a central pixel point of a seismic image, and then determining an order of a fractional differential; (2) selecting a filtering direction of an isotropy diffusion; (3) performing same-phase axis continuous enhancement on a central pixel point; (4) repeating the steps (1) to (3) on a next central pixel point until all pixel points are processed; (5) performing isotropy diffusion filtering processing; and (6) adjusting vertical resolution of the seismic image, and accordingly seismic horizon tracking preprocessing is finished. Through the method of the invention, transverse-direction continuity and vertical-direction resolution of a lineups can be controlled simultaneously. The preprocessing method for automatic tracking of seismic horizon has advantages of: simple process, high calculating efficiency and obvious effect.

The invention provides a fractional-order observability analysis method for a pulsar navigation system. The method comprises a preparation stage and a system observability analysis stage; the preparation stage comprises establishing a spacecraft orbit kinetic model and a pulsar navigation model which are demanded by navigation filtering; and the system observability analysis stage comprises working out the fractional differential of time by a measurement module, obtaining a measurement matrix of fractional order, constructing an observability analysis matrix, rearranging the condition number, calculating the condition number of exponential weighting, so as to obtain the observability analysis result. The observability analysis result obtained according to the technical scheme of the invention is relatively accurate, past measurement information is fully utilized, influence of orbit elements on navigation precision can be embodied, calculation is simple and the method is convenient to achieve, system resource is saved and analysis results can be efficiently obtained.

The invention discloses an optimal-order image enhancement method based on a fractional differential image enhancement algorithm, and the method comprises: calculating the gradient, information entropy, image brightness, human eye feeling brightness, and human eye contrast sensitivity functions of an image, so as to judge the characteristics of the image; performing normalization calculation on the gradient, the information entropy, the image brightness, the human eye feeling brightness and the human eye contrast sensitivity function of the image to obtain a value S; numerical values S representing image texture information, brightness and human eye visual perception are put into the logarithm compression curve to be compressed, and a corresponding optimal fractional order is obtained; Andusing the optimal fractional order in the fractional order differential algorithm of the image to realize self-adaptive image enhancement. Normalized local statistical information capable of representing image characteristics is constructed, a function relation between the orders and the normalized local statistical information is established, the normalized local information is used as an independent variable, a fractional order differential algorithm of the optimal order is obtained according to a logarithm function, and image enhancement is carried out.

The invention belongs to the field of image processing and target tracking, in particular to a target tracking method fusing edge features. The R-L fractional differential edge detection operator andLaplacian edge detection operator are fused to construct a hybrid edge detection operator, which can detect the edge feature information of the target template and scene image. Based on the two histogram models of target chromaticity and edge feature, an adaptive fused backward probability projection map is established to improve the adaptability of the tracking method to uncertain environmental change information. The invention can be applied to a real-time monitoring system.

The invention discloses a novel WENO-format high-precision fractional derivative approximation method. The method comprises the steps: decomposing a Caputo fractional derivative in a fractional differential equation into a classical second derivative and a weak singular integral in a Cartesian coordinate system, discretizing the classical second derivative by using a novel WENO format, and solvingthe weak singular integral by using Gauss-Jacobi quadra-ture; for a time derivative in an equation, using a three-order TVD Runge-Kutta discrete formula to discretize a semi-discrete finite difference format into a space-time full-discrete finite difference format, wherein the space-time full-discrete finite difference format is an iterative formula about a time layer, and an initial state valueis known; and according to the space-time full-discrete finite difference format, solving an approximate value of a next time layer through an iterative formula, and sequentially acquiring a numericalsimulation value in a calculation region at the end moment. The method can achieve six-order precision in the smooth area of a solution, and can keep the property of basically no oscillation in a discontinuous strong interruption area.

The invention discloses a fractional order edge detection method for a noise pollution image. The method is characterized by comprising the following steps: a noise pollution gray level image is denoised; the denoised image enters a two-dimensional fractional differential filter for edge extraction; the de-noising processing method comprises the following steps: carrying out convolution on a graylevel image and a fractional order integral filter or a Gaussian filter to obtain a filtered image; the edge extraction method comprises the following steps: carrying out convolution on a denoised image and two-dimensional fractional differential filter masks in the horizontal direction and the vertical direction, and solving a gradient image. The improved two-dimensional fractional order integralfilter and the improved two-dimensional fractional order differential filter are adopted, and when the improved two-dimensional fractional order integral filter and the improved two-dimensional fractional order differential filter are used for noise image edge detection at the same time, the characteristics of high robustness, edge positioning accuracy and edge enhancement on noise are achieved.

The invention relates to an NSCT domain infrared image enhancement method based on improved Retinex and a quantum flora algorithm, and the method comprises the steps: carrying out the multi-scale andmulti-direction decomposition of an infrared image through employing NSCT transformation, and obtaining a low-frequency sub-band and a high-frequency sub-band; then, adopting an improved Retinex algorithm to enhance the low-frequency sub-band coefficient; introducing a quantum updating strategy of the nonlinear self-adaptive revolving door into a flora algorithm for optimizing fractional differential parameters, and performing denoising and enhancement on a high-frequency sub-band by combining a Bayes Shrink threshold value; and finally, performing NSCT inverse transformation on the processedlow-frequency sub-band coefficient and high-frequency sub-band coefficient to obtain an enhanced image. According to the method, the contrast, definition and information entropy of the infrared imageare greatly improved, the details of the infrared image are enhanced, and a more favorable infrared enhanced image is provided for subsequent infrared image processing.

The invention provides a medical image enhancement method based on combination of a fuzzy set and fractional differential. The method comprises the following steps: decomposing an original medical image by using Haar wavelet transform to obtain a low-frequency sub-band component and a high-frequency sub-band component of the image; enhancing the extracted low-frequency sub-band components by adopting a fuzzy set method of an adaptive threshold to obtain enhanced low-frequency self-band components; constructing a mask by using fractional differential, and carrying out convolution operation on the mask and the high-frequency band component to obtain an enhanced high-frequency band component; and through wavelet reconstruction, performing combined reconstruction on the enhanced low-frequencyself-band component and the enhanced high-frequency band component, and recovering the enhanced image. Wavelet transform is utilized to decompose the image, different extracted frequency bands are enhanced by using different methods, the contrast of the enhanced image is higher, more texture details of the image are depicted, and edge contour details become clearer.

The invention discloses a multi-mode Lamb wave signal separation method based on a fractional differential. The method comprises the following steps that: (1) calculating the Fourier transform of a multi-mode signal; (2) calculating the fractional differential of an amplitude spectrum; (3) calculating the multinomial coefficient of the fractional order and the fractional differential maximum valueof the amplitude spectrum of a Gaussian model; (4) calculating an amplitude spectrum parameter; (5) on the basis of the Gaussian model, calculating the amplitude spectrum; (6) calculating a phase spectrum; (7) extracting a single-mode Lamb wave signal by the amplitude spectrum and the phase spectrum; and (8) removing a mixed signal of which the signal is extracted, and repeating the above steps to realize the separation of each mode. Compared with the prior art, the method can extract the single-mode characteristic parameter of a multi-mode mixed signal through the fractional differential soas to more favorably keep the detail characteristic of each mode signal, each-mode characteristic parameter can be effectively extracted, and each-mode amplitude spectrum reconstruction is realized soas to realize the separation of the multi-mode mixing signals.

The invention discloses a time-frequency overlapping multi-mode Lamb wave signal separation method, comprising the following steps: (1) frequency dispersion compensation of the multi-mode Lamb wave signal; (2) windowing the compensation signal; (3) Fourier transform of windowed signal; (4) calculating fractional derivative of amplitude spectrum; (5) calculating the fractional differential maximumof amplitude spectrum of Gaussian model and the polynomial coefficients of corresponding frequency and differential order; (6) calculating amplitude spectrum parameters; (7) calculating amplitude spectrum based on Gaussian model; (8) calculating and extracting non-dispersive Lamb wave signal by inverse Fourier transform; (9) recovery of Lamb wave signal by dispersion function; (10) the mixed signal after removing the extracted signal repeating the above steps to realize the separation of each pattern. Compared with the prior art, the invention can reduce the difficulty of the mixed signal through the frequency dispersion compensation, can effectively extract the characteristic parameters of each mode, and realize the reconstruction of the amplitude spectrum of each mode to realize the separation of the multi-mode mixed signal.