80 results about "Adaptive smoothing" patented technology

An adaptive demosaicing method is disclosed that interpolates images based on color edge detection and neighborhood voting. The adaptive demosaicing algorithm uses a voting scheme to determine the direction of interpolation at each missing luminance pixel location. Each color plane votes either horizontal or vertical based on a comparison between the vertical and horizontal components of the degree of change (i.e., gradient, Laplacian or other measure of the degree of change) in that color plane. Votes are counted from the neighborhood pixels as well as from measurements taken at the pixel location itself. Once the luminance plane is fully interpolated, the chrominance planes are filled in by a simple bilinear or median interpolation of difference chrominance values. Enhancements to the adaptive demosaicing algorithm permit adaptive smoothing and sharpening.

The invention relates to a wind power output adaptive smoothing method based on energy storage battery charge state feedback, which comprises the following steps of: A, reading data; B, filtering wind power output based on a smoothing controller module, and calculating a smoothing target value of the wind power output; C, adjusting a time constant of the smoothing controller module in the step B and a power command value of an energy storage battery in real time based on an adaptive controller module; and D, outputting data of the smoothing target value of the wind power output which is calculated in the step B and the power command value of the energy storage battery which is calculated in the step C. Due to an industrial personal computer and a communication platform, the functions of smoothing the wind power output, monitoring system on chip (SOC) of the energy storage battery on line, adaptively correcting the time constant of a filter, adaptively correcting the power value of the energy storage battery and the power value of a wind power smoothing target, and the like are realized; therefore, the wind power smoothing effect and energy storage battery management are conveniently and effectively controlled in a wind storage hybrid power generation system.

The subject invention pertains to a method of image intensity correction. The subject invention can utilize extrapolation for image intensity correction. The use of extrapolation can reduce the artifacts during intensity correction as compared to traditional methods of intensity correction. The extrapolation can be combined with, for example, homomorphic filtering methods, parametric estimation techniques, wavelet based method, and/or Gaussian smooth method, in order to reduce the artifacts generated by these methods and improve the quality of correction. The implementation of image extrapolation in accordance with a specific embodiment can utilize closest point method. The subject method can also use adaptive smoothing for image intensity correction. In an embodiment, the use of gradient weighted smoothing method can reduce, or eliminate, over-smoothing of bright spot regions. In a specific embodiment, the subject method can utilize gradient weighted partial differential equation (PDE) smoothing.

The invention discloses a method for calculating the trust values of wireless Mesh network nodes. The method comprises direct trust value calculation, indirect trust value calculation and comprehensive trust value calculation, wherein the direct trust value calculation is carried out for acquiring the interaction times of different time slices among the nodes and establishing a time sequence according to the obtained data at first, and then predicating the interaction times of the next time slice among the nodes by virtue of three times of an exponential smoothing method, and taking the relative errors of the interaction times predicated values and the actual values as the direct trust values of the nodes; the calculation formula of indirect trust values is obtained in a multi-path trust recommendation mode; comprehensive trust values are obtained by virtue of integrated computation for the direct trust values and the indirect trust values. The invention provides a method for calculating the trust values of the nodes, according to the specific condition of a network, adaptive smoothing factor alpha, credibility threshold value Phi, the value of a direct trust value weight beta are selected, the time attenuation characteristic and objectivity of the trust values are guaranteed, the credibility of the nodes is objectively and accurately described, the computation complexity is low, and the method is suitable for a wireless Mesh network.

The subject invention pertains to a method of image intensity correction. The subject invention can utilize extrapolation for image intensity correction. The use of extrapolation can reduce the artifacts during intensity correction as compared to traditional methods of intensity correction. The extrapolation can be combined with, for example, homomorphic filtering methods, parametric estimation techniques, wavelet based method, and/or Gaussian smooth method, in order to reduce the artifacts generated by these methods and improve the quality of correction. The implementation of image extrapolation in accordance with a specific embodiment can utilize closest point method. The subject method can also use adaptive smoothing for image intensity correction. In an embodiment, the use of gradient weighted smoothing method can reduce, or eliminate, over-smoothing of bright spot regions. In a specific embodiment, the subject method can utilize gradient weighted partial differential equation (PDE) smoothing.

The invention puts forward an indoor moving target positioning method based on trajectory smoothing, comprising the following steps: (1) a mobile UWB (Ultra Wideband) node acquires the distance between the node and each anchor node through polling; (2) the distance measured by each anchor node at a moment is corrected by an unscented Kalman filter; (3) the position coordinate of a moving target is acquired through a trilateration positioning approach based on the filtered UWB measured value at the moment; (4) the positioning result is corrected by the unscented Kalman filter; and (5) the filtered positioning result is smoothed by an adaptive smoothing algorithm so as to reach the position estimated value of the moving target at the current moment. According to the invention, the measured values and estimated values at the current moment and at a previous moment are fully utilized, an unscented Kalman filtering mode is adopted, so the influence of environment, device and other factors on the measurement result is reduced, and the positioning accuracy of the trilateration approach is improved; and the positioning result is filtered secondarily and smoothed adaptively, and the positioning accuracy is further improved.

Systems and methods of computer aided engineering analysis using hybrid approach of finite element method (FEM) and adaptive smoothed particle hydrodynamics (SPH) are described. According to one aspect, a computer-aided engineering analysis is performed to simulate an impact event between structures. A FEM grid model is created to represent the structures using a plurality of solid elements which represents geometry and material properties. Once a contact between two structures resulted into a material or structural failure according to predefined material constitutive equation, solid elements representing the failed portion of the structure are removed. Each failed solid element is then replaced by a plurality of particles to be analyzed using the SPH analysis. The particles replacing the failed element inherit all of the states and properties of the failed element, such as location, mass, velocity, acceleration, etc. The replacement is conducted according to the principles of mass, momentum and energy conservation.

The invention provides an image enhancement and abstraction method based on anisotropic filtering. The method comprises the following steps: calculating the smooth continuous characteristic-preserved edge tangential flow field of an input image by use of a structure tensor smoothing technology; performing adaptive smoothing on the image along the direction of the edge tangential flow; performing unsharp image enhancement based on a Gaussian difference mask along the gradient direction; and performing improved bilateral filtering based on characteristic flow or improved Kuwahara filtering based on characteristic flow to obtain a characteristic-enhanced abstract image. The method provided by the invention can obviously improve the generation quality of the existing image abstraction method; and the obtained abstract image has obviously-enhanced boundary characteristics, clear image quality and distinct layers.

The invention discloses a spatial non-cooperative target pose measurement method based on stereoscopic vision. The method does not need any auxiliary measurement tool and a geometric size of a target, and directly recognizes the inherent characteristics of a non-cooperative target to realize the measurement of target position attitude parameters. The method utilizes an image pre-processing frame based on adaptive smoothing filtering and Wallis filtering to solve the problem of image degradation caused by a complex space environment so that the accuracy of non-cooperative target recognition and feature extraction is improved. A feature matching algorithm based on spatial geometric constraint is adopted so that the epipolar constraint in the plane is extended to the space, and the three-dimensional information is obtained directly when the feature matching is completed so that the complexity of the image processing algorithm is reduced.

The invention discloses an improved image edge detection method. The method comprises the following steps of S1, using adaptive smooth filtering and smoothly inputting an image; S2, using the first order gradient template of a Sobel operator for reference and extending to the first order gradient templates in four directions of a horizontal direction, a vertical direction, a 45 degree and a 135 degree, carrying out convolution on the filtered image and acquiring first order gradient components at the four directions, and acquiring gradient amplitudes and gradient angles; and S3, using an Otsualgorithm to calculate a threshold, and combining a connection analysis method to determine the final edge of the image. In the invention, a traditional Canny edge detection algorithm is improved; firstly, the adaptive smooth filtering is used to replace Gaussian filtering in an original algorithm; then, the templates of the four directions of the horizontal direction, the vertical direction, the45 degree and the 135 degree are used to calculate a image gradient, noise sensitiveness is reduced and edge positioning precision is increased; after a non-maximal suppression step, the Otsu algorithm is introduced, and according to image gray level information, high and low thresholds are adaptively generated.

A method and system for spatially localized, noise-adaptive smoothing of filtered back-projection (FBP) nuclear images includes smoothing a FBP image on a pixel-by-pixel basis using a shift-invariant kernel such as a Gaussian kernel. The width of the kernel may be varied on a pixel basis, such that the smoothed image contains a constant SNR over the entire image.

The invention provides a space-time consistence and feature center EMD adaptive video-stable joint optimization method. According to the method, spatial structure consistence matrix estimation-based technologies such as significance protection, inspection elimination, adaptive smoothness, cutting area reduction and video complementation are utilized for jitter videos by taking video jitter prevention as a target on the basis of decomposing noise signals by utilizing an EMD method, so that the stability, universality, correctness and adaptiveness of video enhancement are improved and the videointegrity is improved.

The present invention discloses a remote-sensing image road network extraction technology based on the combined filtering. A complete road substantial structure mathematic measurement model is built through combination of the human vision cognitive rules according to the aperiodicity, the anisotropy and the local directivity of the substantial image structure formed in the human cognition; under the guidance of the road substantial structure mathematic measurement model, a self-adaptive smooth filtering function is built through combination of the steam line directivity, and the self-adaptive smoothness of the road remote sensing image is realized through combination of the pixel point space distance Gauss function; and under the guidance of the road substantial structure measurement model, a self-adaptive impact filtering function is defined through combination of the maximin value filtering, and the impact enhance intensities of the function generated in different areas are different so as to realize the self-adaptive impact filtering enhancement, retain the substantial road structure while smoothing the non-road structure area and realize the preprocessing of the remote sensing image in the road extraction process.

The invention relates to the technical field of digital image processing, and aims at avoiding a staircase effect and a block effect by conducting improvement on a traditional anisotropism filtering method. According to an anisotropism filtering method based on a self-adaptive averaging factor, in the image filtering process, edge fragmentary information is protected by reducing the smoothing degree of noise and marginal areas. Therefore, according to the technical scheme, the anisotropism filtering method based on the self-adaptive averaging factor comprises the steps that pretreatment is conducted on a noise image by adopting a Gaussian filter, the pretreatment formula comprises the step that an improved anisotropic filtering is utilized, the size of the value of a parameter K is determined according to differences of gradient values of each diffusion pixel to a central pixel, that is to say, a self-adaptive equation is utilized to replace the value of an original fixed parameter K, the value of the K of the improved anisotropic filtering is made to reduce on the noise and marginal areas, and the smoothing degree of the improved anisotropic filtering is reduced; the value of the K of the improved anisotropic filtering is increased on the smooth and flat areas, and the smoothing degree of the improved anisotropic filtering is increased. The anisotropism filtering method based on the self-adaptive averaging factor is mainly applied to digital image processing.

The invention discloses a self-adaptive sliding method of a displacement field of a digital image relevant method based on a kernel function. The method comprises the steps of adopting a relevant function p<*> based on the kernel function: FORMULA, and using the self-adaptive sliding method to process a displacement obtained by the digital image relevant method based on the kernel function. According to the self-adaptive sliding method of the displacement field of the digital image relevant method based on the kernel function provided by the invention, a new relevant function p<*> based on the kernel function can be defined; furthermore, with view to the influence on the image noise, the method has better measurement precision in comparison to a traditional method; moreover, the self-adaptive sliding method is used for punishing the roughness of the surface of a noise displacement field based on a least square regression punishing technology, so as to realize an aim of effectively eliminating the noise in the displacement field.

The invention provides a composite insulator hydrophobicity level intelligent determination method. The composite insulator hydrophobicity level intelligent determination method comprises the following steps: preprocessing an image taken by an unmanned aerial vehicle through adaptive smoothing filtering; performing contour extraction on the preprocessed image to obtain the outer contour of water droplets distributed on the umbrella skirt of the composite insulator; using a Bayesian decision-making algorithm combined with an artificial intelligence neural network algorithm to compare an hydrophobic detection image obtained by the contour extraction in step 2 with a standard hydrophobic image, so as to intelligently judge which one of the hydrophobicity level images is most consistent with the hydrophobic detection image, thereby judging the hydrophobicity level of the current hydrophobic detection image. The invention combines the artificial intelligence determination method to detect the hydrophobicity of the composite insulator in operation, which not only improves the detection efficiency, reduces the labor cost, but also greatly improves the working conditions of the line operators, ensures the safety of personnel, avoids the economic loss caused by power failure of the detection methods in the prior art, and has greater economic benefits and good social benefits.

The invention provides a residual echo estimating method and device, and belongs to the technical field of voice signal processing. The method comprises that a residual error signal and an aligned far-end signal are obtained; a power spectrum of the residual error signal, a first power spectrum of the aligned far-end signal and a coherent function of the aligned far-end signal are obtained; a self-adaptive smoothing parameter is calculated and obtained; on the basis of the self-adaptive smoothing parameter and a preset rule, a second power spectrum of the aligned far-end signal is obtained; and according to the coherent function of the second power spectrum of the aligned far-end signal, an estimated value of residual echo is obtained. Thus, the residual echo can be estimated accurately.

The invention discloses an adaptive smooth filtering method. The method comprises the steps of acquiring a pixel point of an image to be denoised; and judging whether the pixel point is an obvious noise point or not; if the pixel point is an obvious noise point, acquiring a similar point set of the obvious noise point, and then estimating a pixel value of the obvious noise point; and adjusting a pixel value of the pixel point if the pixel point is not an obvious noise point. The adaptive smooth filtering method disclosed by the invention starts from an angle that self-similarity exists in the image texture, a filtering operation is carried out on an irregular non-singular point, and the image can be enabled to carry out region smoothing under the condition that the loss in edge and minutiae is reduced as much as possible.

The invention relates to a method and apparatus for rapidly measuring temperature. The method includes the steps of obtaining sampling data at a sampling time through a temperature sensor, obtaining first data at the sampling time, obtaining second data at the sampling time, obtaining third data at the sampling time, and conducting adaptive smooth processing on the third data at the sampling time to obtain the final data of the sampling time. Adaptive low-pass filtering, frequency expansion dynamic acceleration and adaptive smooth processing are conducted for sampling data output by the sensor, thereby substantially improving the sensitivity of temperature measurement.

A method and device for predicting the execution time of manufacturing cloud services. When predicting the service execution time of each candidate cloud service, first determine the characteristic mode of the service execution time of the candidate cloud service, and then according to the set characteristic mode and prediction For the corresponding relationship of the model, use the determined predictive model corresponding to the feature pattern to calculate the predicted value of the service execution time of the candidate cloud service; wherein, the predictive model adopts an exponential smoothing algorithm with adaptive smoothing coefficients for prediction. The present invention improves the traditional exponential smoothing prediction method; proposes an identification method for the characteristic pattern of manufacturing cloud service execution time, and predicts the execution time of manufacturing cloud service on this basis, which enhances the adaptability of the prediction method and improves the prediction Accuracy can provide important data support for service combination, resource optimization configuration and management in cloud manufacturing environment.

The invention discloses a flight path self-adaptive smoothing method based on time sequence QAR parameters. The method specifically comprises the steps that S1, extracting the QAR parameters needed for judging smoothing of an aircraft path; S3, extracting a ground stage, a first flight stage and a second flight stage of an aircraft route segment according to the values of the cleaned QAR parameters; S4, performing smooth noise reduction processing on the ground stage and the first flight stage by adopting an S-G filtering smoothing algorithm; carrying out data smoothing processing on the second flight stage by adopting a moving average smoothing algorithm; S5, identifying and extracting a turning interval in the ground stage; and S6, carrying out smoothing processing on the turning interval by adopting n-order Bezier curve interpolation. The method provided by the invention is high in accuracy, simple in principle, good in stability and suitable for large-scale popularization, and different methods are adopted for smoothing according to the track characteristics of different stages.

The invention provides an illumination compensation method based on a bionic spiral and an image enhancement method. The image enhancement method comprises: S1, a to-be-processed original image is obtained; S2, a brightness layer and a color layer of the image are separated, wherein the color layer is own attribute reservation and the brightness layer includes illumination information for separateprocessing; S3, an illumination analysis of a bionic spiral is carried out on the brightness layer and a new brightness layer is obtained based on an adaptive smoothing algorithm; and S4, the new brightness layer is combined with the original color layer to obtain an enhanced image. With the method provided by the invention, the brightness of the bright part of the image is reduced, the brightensof the shadow part is enhanced, and the brightness f the whole image becomes uniform, so that the details of the extremely high brightness part and the extremely low brightness part are represented;and the operation efficiency is improved.