307 results about "Double threshold" patented technology

A detection method of double-threshold constant false alarm movement object on double-base synthetic aperture radar includes finalizing distance compression of various pulse-sampling, dividing orientation time to be multiple CPI, carrying out FFT in each CPI to enter orientation frequency domain, comparing data amplitude of frequency channel at external of clutter region with the first threshold and outputting point trace, making coagulation on point trace, making Hough transform in point trace domain and making decision of the second threshold.

The invention relates to a terminal detection method for a voice recognition system. The method combines energy double-threshold algorithm with judgment by a model classifier, namely, a detection-verification strategy, to detect terminals of voice signals. In the detection process, energy is initially judged through energy double-threshold algorithm; in the verification process, firstly, a segmental zero-passage rate is judged and then the model classifier is adopted for further verification; the detection-verification strategy includes the following steps: model training, initial energy judging, zero-passage rate judging, model classifier judging, voice terminal determination and voice segment confirmation. The method can effectively and accurately locate the terminals of the voice; meanwhile, the method is easy to realize on line and is applicable to voice real time recognition systems.

The invention discloses an improved image edge detection method. The improved image edge detection method comprise steps of S1, performing smoothing processing on an image and using an improved media filter to suppress noise, S2, obtaining a difference between a horizontal direction and a perpendicular direction through first-order-partial derivatives of directions of x,y,45 degrees and 135 degrees and thus obtaining a gradient amplitude and a gradient direction, S3, performing non-maximum value inhibition on the gradient amplitude, S4, using a gradient histogram to solve a high threshold and a low threshold and then using a double-threshold algorithm to perform edge detection on the image, and S5, performing sharpening processing and connecting the edges to obtain a final edge image. The improved image edge detection method use weighted media filtering to replace gauss filtering, uses the partial derivatives of four directions, uses the gradient histogram to determine the high threshold and the low threshold, reduces detection errors, improves detection accuracy, and makes the counter of the edge image more clear and continuity better.

The invention discloses a news video scene generating method which comprises the following steps: firstly sampling a news video sequence according to a preset interval; calculating the similarity of color histograms of two adjacent video image frames according to the color histogram of a sampled video image; determining a shot boundary sequence according to interframe similarity, the preset high threshold value, the preset low threshold value and the preset lowest threshold value of length of a dissolve shot of the whole news video sequence; then judging with a flashlight; selecting a first frame of each shot as a shot key frame and outputting a boundary sequence of the scene according to the similarity among shot key frames and a corner distribution figure in a subtitle area. With the adoption of the technical scheme, the problem that partitioning algorithm of the double threshold value shot is higher in complexity and the partitioning result is affected by the flashlight can be solved, and continuous shots with same subtitle and the similar visions can be merged to a same video scene when the video scene is generated.

The invention relates to a single frame picture test device for automobile queue length at crossroad and a working method thereof. The single frame picture test device comprises an illumination intensity detection control circuit, an image collection and processing device, and a queue length output interface. The working method comprises: detecting environment illumination intensity, and automatically using a flash lamp to collect a single frame traffic image under low illumination intensity; processing binarization on the traffic image via double threshold value method; detecting lane edges via Hough transformation to define a lane region; detecting boundary to obtain the boundary image of automobiles; projecting the edge information of automobiles to the center line of the lane region; scanning the white points on the center line to determine queue tail; calculating the queue length in the image, calculating actual automobile queue length, and outputting the actual automobile queue length via an output interface. The invention overcomes the defects of video flow detection which is affected by weather, illumination, camera vibration and the like, having the advantages of simple calculation, high operation speed and high reliability.

Systems and methods are provided for detecting edges in an image. In an example method, the image is smoothed using a filter. A gradient magnitude and angle are determined for each pixel of the image. A non-maximum suppression is performed on the resulting image data. A double threshold with an upper and a lower threshold are applied to the resulting image data to determine the edges in the image. The upper and the lower thresholds are calculated automatically using a distribution of intensity values of the image. In example systems, an image is acquired by a camera, or other image acquisition units. An image processing unit is configured to detect the edges in the image using an adaptive threshold edge detection method.

The invention discloses a method for generating a video navigation system automatically, which comprises: cutting a video into a plurality of pictures by using a double-threshold picture cutting algorithm; extracting the key frames of the pictures by using a leading clustering algorithm; clustering the pictures by using the leading clustering algorithm to form a picture group; and creating a scene by using the picture group. The method improves the structuralization degree of the videos greatly, makes a hierarchical directory of the videos, and facilitates the video content capture and browsing of users. The method can make a DVD directory conveniently and provide support for video retrieval technology. Today, when videos have become part of people's life, the method has great application value and prospect.

The invention discloses a double threshold cooperative sensing method in a cognitive wireless network, comprising the following steps: 1. recovering a signal through local compressed sensing, and recovering the whole broadband frequency spectrum in the invention through the compressed sensing, thus a low-speed A / D commutator can be used, so as to lower the hardware requirement; 2. determining a sub frequency band, being capable of obtaining a frequency spectrum edge point of the recovered broadband frequency spectrum signal through the compressed sensing through a wavelet edge detection, and forming a plurality of sub frequency bands by segmenting the frequency spectrum; and 3. cooperative sensing: carrying out double threshold energy detection on all sub frequency bands by each cognitiveuser, transmitting the detection result to a fusion center for judgment to obtain the existing condition of the main user of the whole frequency spectrum, and self-adaptively determining the positionof the spectrum hole in the frequency band. Through the steps, the cognitive network can carry out sampling sensing on the main user signals under the condition of low nyquist frequency, and can self-adaptively determine the position of the spectrum hole in the frequency band.

The invention discloses a frame synchronization method applicable to a burst communication system. A multi-path segmented coherence relevance mode with different segmentation lengths is adopted in an initial signal channel access stage, the maximum relevance value of each branch path is adopted for carrying out double-threshold value (relative threshold value and absolute value) judgment, the synchronous confirmation is carried out from the position with the relevance value exceeding the threshold value, and the influence of severe environment factors in the initial signal channel access stage is overcome through the setting of proper double threshold values and conforming windows; and a full coherence relevance mode is adopted in a stable communication stage, the prior double threshold values and conforming window width are set according to the signal channel information recorded in the conforming windows in the initial signal channel access stage, and the method and the device have higher adaptability through different frame synchronization methods adopted in different communication stages.

The invention provides a water supply pipeline leakage detecting and positioning method which is characterized in that a signal collected by a sensor is subjected to enhancement through spectral subtraction, the frequency spectrum variance of the signal after enhancement is calculated, a double-threshold method is adopted for judgment, that is, if the frequency spectrum variance is within the threshold range, which indicates that leakage occurs, the leakage point is positioned, a BP neural network is utilized to form a filter, a water leakage signal is separated from noise and subjected to generalized correlation analysis, time delay estimation is carried out according to the weight function with a good signal-to-noise ratio selectivity, three time delays of the sensor are acquired, and a water leakage positioning model is utilized for calculation to acquire water leakage point position information. According to the water supply pipeline leakage detecting and positioning method, the spectral subtraction method is adopted for signal enhancement, the double-threshold method is utilized for leakage judgment, if the frequency spectrum variance is within the threshold range, which indicates that leakage occurs, and therefore the accuracy is higher; besides, the non-leakage estimation method is adopted for estimation of the noise spectrum, the signal-to-noise ratio is increased more obviously, meanwhile the BP neural network is adopted for filtering, the generalized correlation time delay estimation accuracy is improved, and the water leakage point positioning accuracy is effectively improved.

The invention relates to an improved double-threshold cooperative spectrum sensing method, which comprises the following steps that: a cognitive user in a cognitive radio system senses a local spectrum, compares and classifies sensed main user energy values, if the energy values are beyond two thresholds, can directly perform local judgment to obtain a hard judgment result whether a main user exists and send the result to a fusion center, if the energy values are between the two thresholds, the cognitive user needs to send the sensed main user energy values and signal to noise ratios to the fusion center, and the fusion center calculates a weight factor of each cognitive user according to the received signal to noise ratios and performs fusion judgment according to the received energy values to obtain a soft judgment result whether the main user exists; and the fusion center comprehensively processes the hard judgment result and the soft judgment result, and makes a final judgment whether the main user exists by using an 'or' rule. According to the method, the advantage of double-threshold cooperative spectrum sensing can be fully realized; and the spectrum sensing performance is improved.

The invention provides a method for extracting a video summary. The method comprises the following steps: calculating HSV histograms of all frame images in a video segment to be extracted; calculating the similarity of two adjacent frame images; detecting the shot transition through an adaptive local double threshold method, and cutting the video where the shot transition exists to finally form a shot set; organizing the similar shots into a clustered set by using a clustering method based on the adaptive threshold; and taking a shot cluster to perform key frame extraction; repeating the operation of S6 until all shot clusters are subjected to key frame extraction; and combining the obtained key frames according to the time sequence to form a final video summary. According to the method provided by the invention, the L value that is closer to human perception is taken as a feature, an optimal maximum value point is obtained through the Euclidean distance of adjacent frame differences, and thus the optimal key frame number and relatively reasonable initial clustering center can be determined to a great extent, and the convergence rate can be increased.

The invention discloses a voice sensitive information detecting and filtering method based on unspecified people. The method is capable of detecting and filtering real-time voices and voice files. The method includes the steps: carrying out end point detection for an original voice through an improved double threshold end point detection algorithm, extracting the Mel frequency inverted spectral coefficient characteristic of the voice, training an appropriate voice characteristic template through a self-learning time warping algorithm, and storing the template in a database; and then performing end point detection for the original voice through the improved double threshold end point detection algorithm, extracting the MFCC characteristic, comparing the extracted voice characteristic with the template in the sensitive word characteristic template database by combining rough matching with fine matching, detecting sensitive words input to the voice, and filtering the detected sensitive words.

The invention discloses a continuous and stable tracking method of a weak moving target in a dynamic background. The method includes acquiring the video data, and processing each frame of image in the following steps of acquiring the position coordinate of the moving target to be tracked in the current frame of image and determining the target tracking frame according to the position; establishing a spatial context model of the current frame of image for the area in the target tracking frame with a Bayesian framework; performing convolution calculation by means of the spatial context model of the current frame of image and the next frame of image to obtain a confidence map of the position of the moving target to be tracked in the next frame of image, the position with the greatest confidence is the position of the moving target to be tracked in the next frame of image; based on the double threshold moving target crisis determination, determining that when the moving target to be tracked is not shielded or lost, outputting the moving target position in the next frame of image when the tracking of the current frame of image is completed; otherwise, updating the target tracking frame and re-checking. The method realizes the continuous and stable tracking of the target under the condition of background interference and shielding.

The invention provides an online AOI (Automatic Optical Inspection) image retrieval method based on double-threshold binaryzation. The online AOI image retrieval method comprises the following steps: S1, acquiring a position image of a standard PCB (Printed Circuit Board); S2, carrying out grey processing on the position image to acquire a grey template image; S3, carrying out double-threshold binaryzation treatment on the grey template image to acquire a template image; S4, acquiring a position image of a PCB to be detected, and acquiring a search region image corresponding to the template image according to the double thresholds set in the step S3; and S5, matching the search region image with the template image to search out a position point which is has the biggest matching degree with the template image. The double-threshold binaryzation treatment is adopted to reduce a grey level of the position image, so that the target area of an electronic component is more protruded; moreover, calculation can be effectively reduced, and calculation speed can be improved, so that a rate of missing report and a misstatement rate are reduced when the AOI software identifies an image.

The invention relates to a motion detection method based on edge detection and frame difference. The method comprises the following steps of: 1, acquiring an image sequence and determining images of a reference frame and a current frame; 2, performing Gaussian filtering on the images of the reference frame and the current frame; 3, extracting the edge information of the reference frame and the current frame after the filtering; 4, acquiring converted images of the reference frame and the current frame; 5, performing the frame difference on the converted images of the reference frame and the current frame, and determining a motion region by using a double threshold value method; and 6, performing image post-processing on the motion region, and determining a motion object. The method has the advantages that: due to the adoption of the thinking of frame difference, the method is simple and practical, the complex computations of an optical flow method and hybrid Gaussian are avoided, computing time is only 1 / 3 of that of classical hybrid Gaussian, and requirements on the real-time performance of intelligent monitoring are met; incomplete frame difference detection results are improvedto a certain extent by the combination of the edge detection and the frame difference and a series of additional processing; and compared with the hybrid Gaussian serving as a mainstream method, the method is difficult to influence by illumination and external interference.

An iris identifying-method based on wavelet transform zero cross presentation includes carrying out binary state treatment on original image based on certain threshold to raise rough positioning precision of pupil, making double-threshold treatment based on external edge rough positioning of iris and carrying out intermediate value filtering to pick up general part of iris effectively, making quick and accurate rough positioning, accurately positioning external edge of iris by utilizing rough positioning result and using wavelet filter to carry out iris coding to effectively pick up information.

The present invention relates to the analytic technology of footprint automatic morphology, in particular to an extraction method of human body bare footprint morphology characteristics. The present invention reduces the number of errors caused by experience extent and subjective mistakes in the artificial analysis to the greater extent, provides a rapid, accurate and effective analyzing mean and method for the analysis and examination of footprint image and ensures the footprint analysis and examination is quantitative, automatic and intellectualized. The technical proposal of the present invention obtains the barefoot image of the target through a scanner and then inputs into the computer for pretreatment including double threshold values segmentation based on the combination of two-peak method and great law, binary processing, morphological filter wave, edge smoothing, etc.; afterwards, the profile tracking extraction and the footprint characteristic value calculation are automatically carried out for the footprint image under the preset coordinate system. The present invention is mainly applied in the field of security and criminal investigation, disease recovery estimation and sports science.

The invention discloses a Bayesian algorithm-based content filtering method. Content filtering is performed for text information in a 3rd generation mobile communication core network, text classification is performed by using a double threshold-based Bayesian algorithm, C1 is set to be normal information, C2 is set to be junk information, a classifier estimates the probability that a characteristic vector X which represents a data sample belongs to each class Ci, and a Bayesian formula for the estimation is that: P(Ci / X) = P(X / Ci) P(Ci) / P(X), wherein i is more than or equal to 1 and less than or equal to 2, the maximum value of a posterior probability is called the maximum posterior probability, for an error (a reference source is not found) of each class, the error (a reference source is not found) only needs to be calculated, a characteristic vector X of an unknown sample is assigned to the Ci class of the error (a reference source is not found) with the minimum risk value. Characteristic selection is performed by adopting document frequency (DF), and classification is performed by using minimum risk-based double threshold Bayesian decision. In a time division-synchronous code division multiple access (TD-SCDMA) mobile internet content monitoring system, the algorithm has higher controllability and can realize real-time high-efficiency classification of mass text information.

The invention provides a novel double-threshold cooperative spectrum sensing algorithm based on energy detection, and belongs to the technical field of spectrum sensing of a wireless system. The method comprises the steps that: 1, a center unit or base station and M cooperative cognitive users are considered to participate in cooperative spectrum sensing, and the center unit is responsible for managing a cognitive radio system and all of the M cooperative cognitive users; and 2, each cooperative cognitive user participating in cooperative spectrum sensing firstly checks the energy of authorized user signals received by itself and judges whether an energy statistical magnitude Ti is less than a threshold [lambda]0 or larger than or equal to a threshold [lambda]1, if the energy statistical magnitude Ti is less than the threshold [lambda]0, a sensed authorization channel is considered to be idle at present, and a value 0 is awarded to the sensed authorization channel, and if the energy statistical magnitude Ti is larger than or equal to the threshold [lambda]1, authorized users are using the current frequency range. According to the invention, a K-order fusion strategy and an EGC soft fusion strategy are effectively combined, the cooperative spectrum sensing efficiency is effectively improved, and the system cost is saved; in addition, compared with single-user self-adaptive double-threshold energy detection, the double-threshold cooperative spectrum sensing algorithm detection is based on multiple fusion rules.

The invention discloses a computer aided detection method for microcalcification in mammograms, which comprises the following steps of: performing grayscale correction and transformation on a mammogram image to obtain an image after grayscale correction; acquiring microcalcification enhanced mammogram image by adopting dual-structure element-based background superposition method, and simultaneously acquiring another background suppressed mammogram image by adopting a Top-hat transformation method in morphology; segmenting the two images by adopting double thresholds to acquire a preliminary microcalcification image, and performing postprocessing to form a coarse microcalcification detection image; and extracting characteristics from coarsely detected microcalcification destination area, classifying by using a support vector machine, removing false microcalcification destination areas, marking the rest microcalcification into the mammogram image for the reading of doctors. The method can free the doctors from trivial mammogram reading and classifying work, and assists the doctors in better understanding and judging the images so as to reduce error diagnosis and missed diagnosis and fulfill the aim of improving diagnostic accuracy.

The invention provides a wearable portable device and method for analyzing eye movement. A flexible wearable electrode frame is used for acquiring a two-channel electro-oculogram (EOG) signal; after preprocessing, EOG in the vertical direction adopts a suggested blink processing algorithm based on wavelet transform-double-threshold processing to perform linear interpolation compensation on a blink signal, and conscious single-blink and double-blink components are separated; after median filtering is performed on horizontal and blink-removal EOG, digital filtering is performed through parameters obtained through a method based on self-adaptive filtering in training mode, two-dimensional position information of a target gazing by the human eye is decoded, and information of speed and accelerated speed of the object is obtained through a derivation module; all parameter information can be displayed through a screen or wirelessly transmitted to an external interactive device to perform coding control; and a user obtains a control result through visual feedback and performs next eye movement control so as to achieve man-machine interaction.

The invention relates to an airborne meteorological radar ground clutter suppression method based on double threshold control. The airborne meteorological radar ground clutter suppression method comprises the following steps of: 1) reading inertial navigation parameters and a radio altitude value from an airborne navigation equipment; 2) compensating a clutter frequency spectrum center nearby a zero doppler frequency by utilizing prior information to obtain a clutter region and a spectrum width evaluation initial value; 3) calculating an echo power of each distance unit; 4) evaluating a clutter spectrum center and a spectrum width by utilizing a polar coordinate centroid method; 5) setting a control factor according to the echo powers; 6) calculating a spectrum width threshold and an amplitude control threshold by utilizing the control factor; 7) calculating the number of doppler channels (exceeding the thresholds) in the clutter region of each distance unit; and 8) utilizing the number of the ultralimit channels to design a self-adaptive notch filter, so that the aim that an airborne meteorological radar ground clutter can be realized. The airborne meteorological radar ground clutter suppression method is suitable for airborne meteorological radar ground clutter suppression in a wind shear mode and is simple; and with the adoption of the airborne meteorological radar ground clutter suppression method, the clutter suppression effect is good, and influences on target signal albefaction during the clutter suppression and influences of a residual clutter on a wind shear signal can be reduced.

The invention discloses a sliding window auto-regression residual error model-based non-intrusive load switching action monitoring method. The method comprises the following steps of: obtaining current of a bus through NILM equipment such as an intelligent electric meter, and carrying out wavelet filtering and denoising on the current; and obtaining a current period maximum value sequence, obtaining a next current maximum residual error by using an AR regression model, and judging a load state according to a size relationship between the residual error and a threshold value. According to the method, double threshold values U and L are used for dividing corresponding events of load switching; the load switching action monitoring range is enlarged, and load switching actions with relativelylow power can be detected, so that missing detection, caused by power change, of load switching actions with small power is avoided; other electricity parameters of non-intrusive measurement equipmentdo not need to be obtained, and only current parameters of the bus are required, so that the requirement for non-intrusive performance is simplified; and through the category division of loads, the complexity of a whole NILM recognition algorithm can be simplified.

The invention provides an image edge detecting method based on Bayer RGB, the method comprises the following steps: providing Bayer RGB images; taking target pixel points as centers, generating matrices of Bayer RGB pixel points in a 3*3 neighborhood, applying detection operators for the gradient operation for various pixel points in the matrices respectively and obtaining gradient amplitudes and gradient angles of various pixel points, and then whether edge points are determined or not through comparing determination of double-threshold values and the gradient of adjacent pixel points. The invention avoids calculating grayness of pixel points, which is suitable for direct edge detection for original images obtained by a CMOS image sensor. The detecting method has the characteristics of good signal to noise ratio, accurate edge positioning and good effect of restraining false edges.

The invention provides a method for detecting and processing a narrow-band interference of a DS (Direct Sequence) / FH (Frequency Hopping) hybrid spread spectrum communication system. According to the technical scheme, the method comprises the following steps: 1) calculating a frequency domain signal; 2) solving an initial threshold; 3) solving high and low thresholds; and 4) calculating a signal after being subjected to interference rejection. According to the technical scheme provided by the invention, the method belongs to a quick double-threshold algorithm. Besides, according to the method provided by the invention, the calculating speed for solving double thresholds is increased and the amount of the calculation is reduced, so that the consumption of resources is reduced and the practicability of the double-threshold algorithm is boosted.

A double-threshold mechanism is used for implementing the follow-me function of a mobile robot. Initially, a user comes to a mobile robot and turns on its follow-me function. Then, the mobile robot is in the follow-me mode or can simply be described as following the user. When the user moves away from the robot, the robot determines whether the distance between itself and the user exceeds a first distance threshold. If so, the robot starts moving to follow the user. Otherwise, the robot stays put. While following the user's movement, the robot continues to monitor the distance between itself and the user. When the robot determines that the distance between them is less than a second distance threshold—because the user has slowed down or stopped, for example—the robot stops moving. The second distance threshold is lower than the first distance threshold.

The invention discloses a Canny edge detection algorithm based on a local threshold, and the algorithm mainly comprises the following steps: 1), carrying out the smooth filtering of an image, and removing noise through Gaussian filtering; 2) calculating an image gradient amplitude and direction based on the smoothed image; 3) performing non-maximum suppression calculation on the gradient directionto obtain an edge point, if the amplitude of the gradient of the current pixel point is greater than the gradient amplitude of two adjacent pixel points in the positive and negative directions of thegradient, regarding the point as the edge point, and marking the corresponding position of the point as 1, otherwise, suppressing the point as a non-edge point, and marking the corresponding positionof the point as 0; 4) obtaining a global proportion value; 5) calculating a local high-low threshold value matrix through an acceleration algorithm according to the global proportion value, and 6) detecting edges according to a double-threshold value matrix to obtain a final edge image. The Canny algorithm can detect the local significant edges in the image in a self-adaptive mode and has good acceleration calculation performance.

The invention relates to a threshold detection method for weak satellite signal acquisition. The signal comprises pseudo range measuring codes. The method comprises the following steps: A, carrying out correlation integration on every possible satellite for N milliseconds, and carrying out M-times difference accumulation on the obtained result; B, determining whether a satellite signal exists or not according to the preset upper limit and lower limit of threshold; C, if the detected signal-to-noise ratio is greater than the upper limit of the threshold, then judging that a strong signal exists; D, if the detected signal-to-noise ratio is less than the lower limit of the threshold, then judging that no signal exists; E, if the detected signal-to-noise ratio is between the two thresholds, then carrying out near-far effect elimination algorithm on the detected strong signal, and detecting the processed data again; and F, and calculating the upper limit of the threshold after the strong signal is eliminated, and if the detected signal-to-noise ratio is greater than the threshold, then judging that a signal exists. The method considers the influence of near-far effect on weak satellite signal acquisition and introduces the concept of detection after the strong signal is eliminated. Compared with the traditional method, the invention reduces the false-alarm probability.

The invention discloses a double-threshold CRAR and trace point condensation method suitable for a continuous wave radar, belongs to a signal processing technology, and specifically relates to a continuous wave radar target constant false alarm detection and distance-speed two-dimensional trace point condensation method. Aiming at the characteristics of a relatively small wave beam grazing angle and relatively high resolution ratio of a continuous wave perimeter monitoring radar, a false alarm probability formula for ordered statistics of CRAR after Weibull clutter is subjected to linear detection and a corresponding clutter parameter estimation method are given; aiming at the problem that traditional OS-CRAR needs ranking operation and is relatively large in time consumption, a method fordouble-threshold CFAR is proposed, the number of times of statistical ranking is greatly reduced, and through addition of GO logic, running time is further reduced, and performance in clutter edges is improved; and joint realization of CRAR detection and trace point condensation is proposed, correct pairing of upper and lower frequency sweeping in subsequent processing of the continuous wave radar is guaranteed, and compared with traditional serial operation, computational complexity is greatly reduced.