718 results about "Gaussian function" patented technology

A method converts a resolution of video signals, the method including: calculating up-sampling and down-sampling ratios based on a resolution of an input video signal and a desired resolution of an output video signal; calculating a number of filter tabs by multiplying the up-sampling and down-sampling ratios by a number of side lobes; calculating first filter coefficients of a same number of the filter tabs by multiplying a window function by a sinc function; calculating final filter coefficients by subtracting a result of a multiplication of a Gaussian function by a window function from the first filter coefficients, and then normalizing the final filter coefficients; and performing filtering in vertical and horizontal directions based on the final filter coefficients by modifying a sampling rate of an input video signal depending on the up-sampling and down-sampling ratios, to obtain clear video images.

The invention provides a method for the outline automatic extraction of a level set medical ultrasonic image area based on an edge and a statistical characteristic, aiming at the characteristics of low contrast degree and small signal-to-noise ratio of medical ultrasonic images and designing a method for the outline extraction of the level set area based on the edge and the statistical characteristic. The method comprises three steps of: 1, carrying out area outline coarse extraction, completing image inverse extraction and self-adapting Gaussian function background suppression, applying otsu image automatic threshold algorithm to convert the image into a binary image, and carrying out image degreasing interference operation and closed area outline extraction work; 2, adopting a selectivity aeolotropy medical ultrasonic image smoothing algorithm to pre-treat an original image; and 3, carrying out the precise extraction of the level set image area based on the edge and the statistical characteristic. Experimental results indicate that compared with the prior method, the method of the invention can obtain a more precise partition result.

The invention provides a human falling detection method based on machine vision. Under the conditions that all detected objects are people in indoor environment and the activities of all people can be captured by a depth camera, the method comprises the following steps of: (1) acquiring an image by using the depth camera; (2) subtracting a background from the image which is acquired, and extracting a foreground from the image; (3) acquiring a foreground contour by using a binary image contour extraction algorithm; (4) performing convolution computation on the foreground contour and Gaussian functions in different scales, mapping a contour image to a curvature scale space (CSS) to form a CSS image, extracting peak points of the CSS image, and thus obtaining a video word bag based on curvature scale characteristics by the peak points in different scales; and (5) training an extreme learning machine classifier by using the video word bag which is obtained. By adoption of the method, under the condition of few training samples, an accurate falling detection result can be obtained within a short training time.

A device for analyzing a physiological signal, such as an electrocardiogram or electrogram, that was previously collected, filtered, sampled and digitized. The device memorizes the digitized signal and analyzes it by decomposing the signal into a plurality of N parameterized bump functions, where each bump function is a continuous function defined by three successive intervals, respectively, a first monotonic parameterized function, an affine function, and a second monotonic parameterized function, with one of the monotonic parameterized functions being increasing and the other decreasing. The parameterized functions are preferably half-Gaussian functions, and the affine function preferably has a null slope. Each N bump function is classified by recognizing at least one parameter characteristic of each wave, and allotting a standardized label, selected among a plurality of predetermined labels, according to one or to more of the characteristic parameters thus recognized.

A feature tracker for tracking a target includes an imaging sensor for imaging the target and a Kalman filter for generating predicted position, velocity and acceleration of the imaging sensor with respect to the target. The Kalman filter includes a state vector estimate of the position, velocity and acceleration of the imaging sensor, and a model for characterizing the target. The model characterizes the target by using at least one bivariate Gaussian function for the target. The Kalman filter includes a Jacobian matrix defined as a partial derivative of the model with respect to the state vector estimate. The Kalman filter includes a gain matrix generated from the Jacobian matrix.

The invention discloses a power system fault diagnosis method comprehensively using electricity amount and timing sequence information. Firstly, power-off areas before and after a fault are compared and analyzed to determine the fault area, and a suspicious element set is formed; secondly, a weighting fuzzy timing sequence Petri net model is built, and SCADA information, electricity amount information of a WAMS and the timing sequence characteristics included in the information are integrated to form the alarm information criterion, place delay restraints and the electricity amount criterion; a Gaussian function is adopted, the confidence coefficient of alarm information is obtained in combination with timing sequence reasoning, the initial confidence coefficient of the Petri net model is calculated through array calculation, and the model is solved; finally, backward reasoning is carried out according to the fault probability of an element, and protection and judgment of maloperation and motion refusal of a disconnector are carried out. The power system fault diagnosis method is high in fault-tolerant capability, can handle the phenomena of protection and maloperation / motion refusal of the disconnector and the phenomenon that an alarm is lost or wrong, and improves the accuracy and reliability of the fault diagnosis result.

A method converts a resolution of video signals, the method including: calculating up-sampling and down-sampling ratios based on a resolution of an input video signal and a desired resolution of an output video signal; calculating a number of filter tabs by multiplying the up-sampling and down-sampling ratios by a number of side lobes; calculating first filter coefficients of a same number of the filter tabs by multiplying a window function by a sinc function; calculating final filter coefficients by subtracting a result of a multiplication of a Gaussian function by a window function from the first filter coefficients, and then normalizing the final filter coefficients; and performing filtering in vertical and horizontal directions based on the final filter coefficients by modifying a sampling rate of an input video signal depending on the up-sampling and down-sampling ratios, to obtain clear video images.

The invention discloses a visual saliency and multiple characteristics-based pedestrian re-recognition method. The method comprises: quickly detecting a pedestrian target in a video; partitioning a pedestrian region by virtue of a saliency detection algorithm and extracting a spatial histogram of a target region; partitioning a human body into three parts: a head, a trunk and legs by virtue of a SCA method and extracting color, position, shape and texture characteristics of the trunk and the legs; calculating the similarity of the spatial histogram by adopting an improved JSD similarity measurement criterion for primary recognition of the target; and calculating the similarity of a regional characteristic set by virtue of a Gaussian function to obtain a final recognition result. The method provided by the invention can be used for realizing long-term tracking and monitoring of special pedestrians in a multi-camera network in different background environments and under different camera settings. The method is used for realizing intelligentized processing of monitoring vidoes and immediately responding to unusual conditions in the videos, and moreover, a lot of manpower and material resources can be saved.

The invention relates to an NaI (TI) scintillation detector gamma energy spectrum high-resolution inversion analysis process and method based on a gauss response matrix. The analysis process comprises the steps of spectral line pretreatment, peak searching and peak boundary treatment, resolution ratio ruling, background subtraction, gauss response matrix generation and inversion analysis. According to the feature of an NaI (TI) scintillation detector and the physical property of the spectrum forming process, response, in the detector, of different energy gamma photons corresponds to different full widths at half maximum of a photo peak, and the photo peak is approximate to a gauss function in shape. The parameters of the full widths at half maximum of a spectral line are extracted, the background is subtracted from the full widths at half maximum adaptively, the universal gauss response matrix between a radioactive source and a gamma spectrum is constructed, and finally the response matrix is used for inversion analysis of other gamma instrument spectrums measured by the NaI (TI) scintillation detector. The result analyzed through the method is an energy point corresponding to the measured spectral line under the response matrix or is approximate to the solution of a physical spectral line in theory, and the ability of the method to analyze the spectral line is obviously improved.

A method for determining a location of an object in a three-dimensional localization field created by a localization system includes the following steps: providing a catheter having known spacing between electrodes; providing a lookup table of data correlating locations of an object within the localization field with measurements made by the localization system; placing the catheter into the localization field; using the localization system to determine the location of the electrodes based on the lookup table; calculating an observed distance between electrodes; comparing the observed distance to the known electrode spacing; and adjusting the lookup table to more accurately measure the spacing of the electrodes. A Kernel function, such as the derivative of a Gaussian function, may be used to update the lookup table.

The invention belongs to the field of intelligent traffic, and relates to an urban road vehicle running speed forecasting method based on road network characteristics. The urban road vehicle running speed forecasting method can be applied to forecasting the vehicle running speed on an urban road during a period of time, a model is improved on the basis of a k-nearest neighbor nonparametric regression method, relations between road sections in a road network are considered, and a matched time series state vector is enlarged into a multi-dimensional space-time state matrix. A sample database is built through collected historical data, real-time data are collected for serving as a template to be matched with a sample, and the vehicle speed of a next time series of a target road section in the obtained neighbor sample serves as the forecasted vehicle speed. The gaussian function is used in the model two times for setting weights for the state matrix and forecasting results in an integrated mode so that the forecasting accuracy can be improved. The model provided in the urban road vehicle running speed forecasting method has the advantages that the data of the small road network with the road section to be detected as a center serve as the state matrix; compared with a prior forecasting model which only gives consideration to data of a current forecasted road section, the model provided in the urban road vehicle running speed forecasting method is higher in accuracy of multi-step forecasting; in addition, the method can offset real-time data or can be used for forecasting under the condition that a road section speed detector breaks down.

The invention discloses a full-waveform laser radar echo data gaussian decomposition method based on a genetic algorithm. The method is mainly formed by five steps including echo denoising, initial wave crest extracting, initial parameter estimating, waveform fitting and precision judging; when the precision of initial fitting meets the requirements, second fitting is not carried out, when the precision of the initial fitting does not meet the requirements, iteration fitting is carried out, and in each iteration, the fitting precision is improved by a method of increasing a gaussian component until a stopping condition is met; a gaussian function is used for fitting echo data of a full-waveform laser radar; and a gaussian filter method is adopted to filter noise of the echo data of the full-wave form laser radar, the number of initial gaussian components is confirmed by extracting a peak value point, an initial parameter range is estimated, fitting and precision adjusting are carried out on the echo data of the full-waveform laser through the genetic algorithm, an echo gaussian decomposition software system based on the genetic algorithm is manufactured, and echo decomposition operation is simplified.

The invention provides an infrared image and visible image fusion method. The method comprises the following steps of: firstly decomposing an infrared image and a visible image by adoption of non-subsampled double-tree contourlet transformation NSDTCT so as to obtain a low-frequency sub-band and a high-frequency sub-band; constructing a fused sub-band coefficient; in low frequency, calculating local area energy, and replacing a low-frequency sub-band coefficient in fuzzy logic (constructed by a fuzzy membership and a Gaussian function) by the local area energy to calculate a weight coefficient, so as to calculate a fused low-frequency sub-band coefficient; in high frequency, taking pixel definition and significance as fusion rules externally input by a pulse granted cortex model SCM so asto calculate a fused high-frequency sub-band coefficient; and finally obtaining a fused image via inverse NSDTCT. Compared with the traditional image fusion method, the method subjectively and objectively obtains relatively ideal effects in infrared image and visible image fusion, and provides a new thought for infrared image and visible image fusion.

In a face recognition apparatus adapted to recognize an input face image on the basis of one or more registered face images, similarity between an input face image and a registered face image is determined based on a Gabor jet calculated by performing Gabor filtering using Gabor filters defined by a Gaussian function representing a window and sine and cosine functions representing a frequency response for each of predetermined frequency values and for each of predetermined angles of the response function. Values of the filter window are calculated in advance based on the Gaussian function and stored in a filter window data ROM. Values of the response function are calculated in advance for respective angles based on the sine and cosine functions and stored in sine data ROMs and cosine data ROMs. In the Gabor filtering process, coefficients of the Gabor filters are determined from values read from these ROMs.

The invention discloses a face identification method, which comprises the following steps of: obtaining a classifier model by carrying out random dimension reduction on testing samples and training samples, generating a training set and a testing set of the similarity learning, selecting a regular parameter and a Gaussian kernel function of a support vector machine and inputting the training set of the similarity learning into the regular parameter and the Gaussian kernel function; then inputting the testing set of the similarity learning into the classifier model to obtain classification results; obtaining a sum of the classification results, using the quotient of the sum and the sample number of a certain type of samples as a value of the similarity probability of the type, obtaining the maximum value and outputting the maximum value to obtain the value of the similarity probability so as to obtain the most accurate face identification result. By dimension reduction of the samples, the complexity of the samples is reduced, so that an algorithm of learning the similarity between face images on the basis of the SVM (Support Vector Machine) is rapid; and moreover, by an algorithm of carrying out processing on each type, the face identification rate is correspondingly improved.

The invention discloses an image quality evaluation method based on a visual characteristic. The method is characterized by: firstly, converting original images into a xyY space or a LCH space, processing a component Y or a component C so as to obtain a group of test images; then, finding at least one JND (just noticeable difference) critical image; calculating differences of brightness (BR), color saturation (CS) and Gaussian function variance parameters in the original images and the JND critical image, wherein average values of the differences are the JND values of the BR, CS and contour rendering (CR) of the original images respectively; and then, carrying out mean calculation on the JND values of the different original images and determining the final JND values; taking the JND as a unit to change different attributes of the image at different degrees respectively; using a visual perception test to test the subjective image quality; then, using a regression analysis to establish a relation model of the image quality and the image attributes; and researching influences of the different attributes of the images according to a variance analysis. By using the method of the invention, influence on image quality which can be perceived by human eyes when improving the display parameter can be quantitatively evaluated.

The present invention provides a fluctuation wind speed prediction method based on an extreme learning machine. The method comprises the following steps of: step 1: using an ARMA model to perform simulation to generate fluctuation wind speed samples of vertical spatial points, and dividing the fluctuation wind speed sample of each spatial point into two parts of a training set and a test set; step 2: giving training samples, setting a Gaussian kernel function as a kernel function, and calculating a kernel function matrix K of the training samples; step 3: establishing a limit learning machine algorithm model of the kernel function; and step 4: comparing test samples with results of prediction of fluctuation wind speed by KELM, calculating a mean absolute error, a root-mean-square error and a correlated coefficient of a predicted wind speed and an actual wind speed, and evaluating the effectiveness of the method. The invention provides a prediction method for a complete wind speed time-history curve needed for wind resistance design, thereby reducing a great deal of time costs.

The invention discloses a laser frequency spectrum retrieval method and a measurement calibration method for a molecular scattering anemometry laser radar and solves the problem that the existing anemometry Rayleigh laser radar is difficult in laser spectrum calibration. The retrieval method includes: allowing the molecular scattering light to perform convolution operation through an etalon transmittance function Ni(v) and an etalon transmittance function Fei(v), allowing the operation result to perform convolution operation with Rayleigh-Brillouin spectrum FR-B(v) to obtain the laser spectrum Fl(v), and fitting with the Gaussian function to obtain the laser spectrum function. The measurement calibration method and system is provided with an etalon controller for changing the cavity length of an etalon, the laser beams entering the etalon scan the cavity length of the etalon to obtain the etalon and molecular scattering light transmittance curve. By the method, accuracy, reliability and precision of wind retrieval are increased.

The invention provides a scene understanding method based on multi-task learning. The method includes: multi-task learning having unstable homoscedasticity, multi-task likelihood function, and scene understanding model. The method includes the following steps: first executing weighted linear sum on the loss of each individual task, learning the optimal task weight, inducting a multi-task loss function, defining a probability model, defining the probability as the gaussian function of the average values that are output by the model, eventually modeling pixel-level learning regression and classified output, comprising semantic division, incidence division and depth regression. According to the invention, the scene understanding model can learn multi-task weight, is more advantageous than models that independently train each task in that the method herein reduces computing amount, increases learning efficiency and prediction precision and can be real-timely operated.

The invention discloses a method for identifying and predicting a bus passenger flow influence factor based on geographically and temporally weighted regression (GTWR). The method comprises steps of: extracting a traffic zone hour bus passenger flow and calculating built environment density; 2, constructing a space-time three-dimensional coordinate system according to the time of a passenger flow observation point and latitude and longitude to calculate space-time distance, and reckoning a spatial regression weight matrix according to a Gaussian function and the distance; 3, calculating a relation between a passenger flow volume and land utilization under different space-time conditions based on the GTWR; and 4, obtaining a change of a relevant parameter to a coefficient according to the calculation to perform visualization processing in time and space, and analyzing an inherent law. The method takes account of the influence of a time factor on the bus passenger flow and the built environment relation, can deeply excavate an internal relation between the passenger flow and the land utilization, accurately predicts the bus passenger flow, and provides the scientific theoretical guidance for the bus line planning and operation management.

The invention relates to a method for detecting a modulation transfer function (MTF) of a hyperspectral remote sensing system based on an edge image. The method takes the influence of an image noise into consideration and comprises the following steps of: selecting edge areas in the horizontal direction and the vertical direction by calculating the modulation degrees of the edges of each wave band of a hyperspectral remote sensing image; performing differential operation on the edge areas to obtain linear spread functions; performing Gaussian function least squares fitting on the linear spread functions to obtain standard deviations; figuring the linear spread functions in the vertical direction and the horizontal direction out by using a median of the standard deviations of the linear spread functions in the vertical direction and the horizontal direction; performing discrete Fourier transform to obtain MTF curves in the two directions; and establishing a two-dimensional MTF matrix of the system by multiplying. The method can select suitable edge areas in the situation of a plurality of wave bands of the hyperspectral remote sensing image, and realize the extraction of the MTF ofthe system when the signal-to-noise ratio of the image is not high.

The invention discloses an unsupervised face recognition method based on a fast density clustering algorithm. The unsupervised face recognition method comprises the steps: firstly extracting pixel matrixes of face images; by utilizing structure similarity, computing similarity of the images; by utilizing the Gaussian function, computing the density of image objects; by utilizing the computed density of the image objects, computing the local density of one image object, and the minimum distance between the image object and another image object with the larger local density; with the combination of density-distance distribution of the image objects, fitting a density and distance function relationship by regression analysis, and automatically determining a cluster center by residual analysis; training and recognizing clustering results by utilizing a classifier. The unsupervised face recognition method based on the fast density clustering algorithm, which is provided by the invention, does not need to predict any classification information of the face images and has stronger recognition capability.

The invention provides an automatic focusing method and apparatus based on an interested area, and belongs to the technical field of imaging. The method comprises the following steps: obtaining a target image that has been divided into blocks; separately obtaining the definition of the blocks; obtaining a normalized central coordinate and a normalized size of the interested area on the target image; separately calculating coefficients of full width at half maximum of a horizontal direction and a vertical direction according to the normalized size; separately calculating the weight values of the blocks by using a two-dimensional dispersed Gaussian function according to the normalized central coordinate and the coefficients of full width at half maximum; calculating the normalized overall definition of the target image according to the weight values and the definition of the blocks; and performing focusing according to the normalized overall definition. By adoption of the automatic focusing method and apparatus provided by the invention, an interested area template can be automatically calculated, thereby the occupation of the storage space by the storage of ROI template data.

The invention discloses an iris image blurring determination method and device. The method comprises steps of using radial symmetry transform to position pupils in an iris image; capturing an area of interest in the iris image, wherein the area of interest comprises a pupil boundary and pupils and irises at two sides of the pupil boundary; performing Gaussian fitting of the gradient value of the area of interest in the pupil radius direction, so as to obtain a blurring grade which is a divergence parameter of a Gaussian function obtained by fitting; and determining whether the blurring grade is larger than a blurring grade threshold or not, and if so, determining the iris image as a blurring one. Compared with the prior art, the method provided by the invention is higher in determination accuracy and faster in operation speed, and improves the iris identification performance.

The invention discloses a pedestrian detection method based on layered kernel sparse representation. The method is characterized by comprising steps as follows: preprocessing acquired traffic videos to obtain required positive and negative samples, acquiring multi-scale eigenvectors through layered sub-block division, and constructing two kinds of dictionary matrixes; preprocessing to-be-detected pedestrian images to obtain test samples, and performing pedestrian feature extraction with a method adopted in the dictionary construction process to form eigenvectors of the test samples; performing kernel sparse decomposition on the eigenvectors of the test samples with histogram cross kernel functions, adopting Gaussian functions for weighting in an iteration solution process, and reconstructing errors to realize pedestrian detection. With the adoption of the method, the higher detection performance can be realized, the pedestrian detection accuracy under the condition of partial shielding is effectively improved, and the robustness of a pedestrian detection system for appearance changes is enhanced.

A method of analyzing data from a mass spectrometer for a data dependent acquisition is described. In an embodiment of this method, mass spectral scans are taken of a sample eluted from a liquid chromatography column. An extracted ion chromatogram (XIC) is then created for each m / z data point of the mass spectral scans and the XIC for each m / z data point are correlated to a model function, such as a monotonically increasing function, or the first half of a gaussian function, to obtain a XIC correlation value. A weighting function is then applied to the XIC correlation value to obtain a current weighted intensity. The current weighted intensity for each m / z point is used to reconstruct a weighted mass spectrum, which is then used to make a real-time decision for the data dependent acquisition. In an embodiment, the data dependent acquisition is the performance of tandem mass spectrometry. Embodiments of the invention also describe a sample processing apparatus for the data dependent acquisition and a computer readable medium that provides instructions to the sample processing apparatus to perform the method described above.

A real-time algorithm for rendering an inhomogeneous scattering medium such as fog is described. An input media animation is represented as a sequence of density fields, each of which is decomposed into a weighted sum of a set of radial basis functions (RBFs) such as Gaussians. The algorithm computes airlight and surface reflectance of the inhomogeneous scattering medium. Several approximations are taken which lead to analytical solutions of quantities such as an optical depth integrations and single scattering integrations, and a reduced number of integrations that need to be calculated. The resultant algorithm is able to render inhomogeneous media including their shadowing and scattering effects in real time. The algorithm may be adopted for a variety of light sources including point lights and environmental lights.

Point source responses of pinhole apertures in a non-uniform grid mask used to spatially calibrate a gamma camera can be modeled as a two-dimensional Gaussian function with a set of seven parameters. The Gaussian parameters can be measured using a surface-fitting algorithm that seeks minimum error in the least squares sense. The process is repeated for data from each pinhole location and the data are added together to generate a complete model of the flood image from the mask, which then can be used in a peak detection process for clinical images.