242results about How to "Improve computing speed" patented technology

The invention discloses a power equipment state evaluation method based on data mining and principal component analysis (PCA). The method comprises the following steps: determining a parameter to be evaluated of power equipment; mining and analyzing the determined parameter to be evaluated of the power equipment, establishing a comprehensive evaluation module for the operating state of the power equipment by using a PCA method; selecting modeling data, solving model parameters, determining an early warning value, and performing real-time dynamic evaluation. The method establishes a power equipment key parameter system by using dimensionality reduction technology, reduces the computation space and time, improves operation rate and efficiency; and can achieve differentiated evaluation on different individuals of the same kind of equipment in the case of lack of historical failure sample data of the power equipment.

The invention discloses a public transport vehicle passenger flow statistical method and system. Concretely, two cameras are respectively installed at the front and rear doors of a bus. The release lines of the front and rear doors of the bus and a fitting function of the camera height and the head size are determined before processing images acquired by the cameras. After a door opening signal is received, preprocessing, head detection, head tracking and head movement track analysis are performed on the images acquired by the cameras in turn, and finally passenger behaviors are analyzed according to head movement tracks, the number of bidirectional passenger flow of the corresponding doors is acquired and the number of bidirectional passenger flow of the whole bus is acquired through statistics of the number of bidirectional passenger flow of the front and rear doors of the bus. After a door closing signal is received, processing of the images acquired by the cameras is stopped, and the problem of influence on the statistical result caused by abrupt change of illumination generated by movement of the bus can be reduced. The method has advantages of high statistical accuracy so that reliable supporting can be provided for intelligent scheduling of the bus.

The invention discloses a locomotive semi-physical simulation device, system and method. The device comprises a real-time simulation unit, a computer and an electrical signal conversion unit. The computer converts a control instruction for simulating an upper-layer network control system into a bus communication protocol, and then sends the bus communication protocol to a communication conversion unit of the electrical signal conversion unit. The communication conversion unit sends the control instruction to an external physical controller, the physical controller outputs a control signal to a controlled object model according to the control instruction, the control signal is sent to a real-time simulation unit via the electrical signal conversion unit, the real-time simulation unit calculates a output result of a state signal of the controlled object model according to the control signal, the output result is sent to the physical controller via the electrical signal conversion unit, and the physical controller sends the state signal of the controlled object model back to the computer via the communication conversion unit. The technical problems that simulation integrity and precision of an existing system are not high, function verification cannot be accurately performed, the modification period is long, and efficiency and performability are low are solved.

The invention discloses a method for real-time correcting error of a multi-channel high-speed parallel alternative acquisition system, which comprises the following steps: in correction, inputting direct current correction signals of different voltages, acquiring a quantization value of each ADC and performing correction point by point to obtain errors point by point and form an error matrix; in measurement, inputting a signal to be measured and performing sampling at time alternating, acquiring quantization values and searching a table in the error matrix, deducting corresponding error value from the quantization values respectively to obtain corrected actual quantization values; and then, rearranging the corrected quantization values according a time phase relationship; converting the rearranged and corrected quantization values into acquisition data for output; and performing low-pass filter; and filtering time-base deviation error spectral line so as to finish the correction of a time error. Compared with the prior art, the method has the advantages that: the deviation and a gain error can be corrected at the same time; and a table searching operation is a corresponding relationship operation of which the process is simple and the calculation amount is small. A low-pass digital filter is very easy to be implemented in a DSP processor and the operation rate is fast so that the efficiency of the time error correction is improved greatly.

The invention relates to a data processing method and device, an electronic device and a readable storage medium, and the method comprises: carrying out the division of a processed target neural network, and enabling the target neural network to comprise at least one sub-network. With the adoption of the method, the compilation and operation of the sub-network can be carried out at least. The dataprocessing method provided by the embodiment of the invention only needs one input operation and one output operation, and is more efficient and the operation rate is improved compared with separatecompilation operation, required multiple input operations and multiple output operations on a plurality of network layers in the sub-network.

The invention relates to a dependable computing unit based on a homemade TCM and an operation method thereof. The dependable computing unit comprises a mechanical structure layer, a platform hardware layer, a firmware layer, an operation system layer and a dependable software layer; the mechanical structure layer comprises a case, a heat dissipating system and a power source; the firmware layer comprises firmware; the operation system layer comprises an operation system; the platform hardware layer comprises a backboard, a computing unit module and a dependable code module; the dependable software layer comprises a dependable application module and a dependable computing supporting module. The dependable computing unit is complete in function, high in algorithm computing efficiency and large in concurrence work capacity; serving as the dependable computing unit, the unit can provide high-performance data encryption / decryption service for an information safety transmission system and serve as main code equipment and core member of a host data safety storage system, an identity authentication system and a symmetric and asymmetrical secret key management system, and has wide system application potentiality.

The invention relates to a dual-camera-based motion estimation method of a light stream detection device for an aircraft. First, the light stream detection device for the aircraft, which consists of a forward-looking camera and a down-looking camera, is used for collecting forward-looking images and down-looking images in real time, and the light stream values, which are represented by a first formula shown in the description and a second formula shown in the description, of corresponding multiple characteristic points between the current frames and the last frames of the forward-looking images and the down-looking images are calculated respectively; secondly, the angular speed of the aircraft is calculated by utilizing the distribution characteristics of the translational light streams of the forward-looking images and the decoupling thoughts of the light streams, and an attitude angle is updated; thirdly, the translational light streams of the down-looking images are obtained by utilizing the obtained angular speed and the second formula shown in the description. By utilizing the obtained attitude angle, each characteristic point is deeply represented to be the function of the terrain clearance of the aircraft; a novel translational motion estimation equation is established; the speed v of the aircraft is solved by using a UKF (unscented Kalman filter). Finally, the six-degree-of-freedom motion estimation of the aircraft is gained. The dual-camera-based motion estimation method of the light stream detection device for the aircraft is applicable to the navigation of indoor, low-altitude or deep-space aircraft such as a mars exploration aircraft. The forward-looking camera to which the dual-camera-based motion estimation method of the light stream detection device for the aircraft relates can be also used for completing the calculation of an attack angle and a sideslip angle and other navigation tasks of evading an obstruction, and the like.

The invention relates to a multi-source combined positioning method based on adaptive weighted mixing Kalman filtering. The method performs combined positioning via GNSS / RFID / IMU. When a GNSS signal loses lock, the method may perform weighted adaptive Kalman filtering on IMU and RFID positioning information by searching a weight database and outputs accurate positioning. The multi-source combined positioning method based on adaptive weighted mixing Kalman filtering increases GNSS positioning precision by using an expanded Kalman filtering algorithm, improves blind area positioning precision by using a multi-sensor combined positioning algorithm, overcomes influences due to the shielding of high buildings, and reduces weight computational difficulty and increases arithmetic speed by establishing a weight database.

The invention discloses a fault early warning and life prediction method of escalator equipment. The method calculates the health status of an escalator system according to each module status of the escalator, the escalator health status is compared with the escalator abnormality time fitting function, the escalator operation time is obtained, and the remaining life of the equipment can be calculated according to a failure threshold of the escalator corresponding to the escalator operation time. According to fault early warning and life prediction method of escalator equipment, all kinds of algorithms adopted are designed based on equipment index analysis, the escalator index in algorithms is changed to other equipment indexes, the real time index data of the corresponding detection equipment is collected, so that the health evaluation status and the remaining life of the detection equipment. Therefore, the algorithm system is suitable for the prediction of each key equipment, and hasthe commonality.

The invention discloses a data processing system based on a separated Hopkinson bar dynamical mechanical property test device. The data processing system is characterized in that voltage-time data points obtained by testing the separated Hopkinson bar dynamical mechanical property are first read, then the voltage-time data points are drawn into a voltage-time curve, the tested voltage-time curve is transformed to a needed engineering stress-strain curve and a real stress-strain curve through the human-computer interaction according to a given test parameter or by automatically inputting the parameter and a calculation formula, and the data can be examined, smoothened, modified, intercepted and outputted at any time. By adopting the data processing system, the dynamically compressed test data, the cutting data and the tensile mechanical property test data can be respectively processed; based on the humanized interface of the windows operating system, the intelligentization of the separated Hopkinson bar dynamical mechanical property data processing can be realized, the data is more flexible to process, the operation rate and convenience in operation can be improved, and the practicability is strong.

The invention discloses a strap-down inertial navigation transfer alignment algorithm parallel implementation method. A primary binding module, a globe related parameter calculating module, a sub inertial navigation system navigation calculating module, a filter parameter calculating module, a Kalman filter module and an alignment output module are adopted in the method; the primary binding module receives data information of a first frame main inertial navigation system, and the data information is subjected to compensating calculation and then used as an initial value of initial alignment navigation calculation of a sub inertial navigation system; the globe related parameter calculating module, the sub inertial navigation system navigation calculating module, the filter parameter calculating module and the Kalman filter module form a fine alignment process of transfer alignment, and the fine alignment process is circularly executed till reaching the set cycle times; and the alignment output module performs one-time correction on the attitude information of the sub inertial navigation system after the fine alignment process is finished, and outputs attitude, speed and position initial values required by navigation calculation of the sub inertial navigation system. By the method, the calculation velocity of the strap-down inertial navigation transfer alignment algorithm is quickened, and the alignment precision of transfer alignment is improved.

The invention discloses field programmable gate array (FPGA) implementation equipment and an FPGA implementation method for self-adaptive clutter suppression of external radiation source radar. The method comprises the following steps of inputting four paths of channelized data to be processed into a finite impulse response (FIR) filter module through a first in first out (FIFO) memory inside a self-adaptive clutter suppression module of an FPGA chip; inputting two paths of data, channelized by an auxiliary antenna, into a step length calculation module through the FIFO memory, and calculating the step length through the step length calculation module; simultaneously inputting the output of the step length calculation module and two paths of output of the FIR filter module into a weight updating module; inputting an updated weight into the FIR filter module; and performing synchronous parallel output on clutter suppression results through in-phase / quadrature (I / Q) channels. The self-adaptive clutter suppression is controlled by five global clocks with the same rate and different phases. The self-adaptive clutter suppression of the external radiation source radar can be better realized. The problem of difficulty in meeting of a requirement on real-time performance caused by large conventional clutter suppression equipment amount is solved. The equipment and the method are high in processing efficiency, high in calculation speed and low in equipment complexity, and are used for implementing the self-adaptive clutter suppression of the external radiation source radar.

The invention relates to the field of coding of images of field camera shooting equipment, and provides a video coding method allowing a human face to be recognized. The video coding method comprises the steps that signals collected by an image sensor are converted into YUV data; the YUV data are transmitted to a first cache and a second cache respectively, the YUV data fed into the first cache are first data with an original resolution ratio, and the YUV data fed into the second cache are second data with a compressed resolution ratio; a human face detecting algorithm is executed on the second data, and regional information of a macro block meeting human face characteristics is obtained; the first data are coded, and the coding rate of a macro block of a corresponding region in the first data is improved according to the regional information. The invention further provides a video coding device allowing the human face to be recognized. The video coding method and device allowing the human face to be recognized have the advantages that due to the that that the resolution ratio of the second data is low, a higher operating rate is achieved when the human detecting algorithm is executed, and operation consumed time is shortened significantly.

The invention discloses an automatic grain boundary extraction method for a steel grain. The method comprises the steps that 1 grain image preprocessing, wherein gray scale conversion, median filter denoising and binarization processing are carried out; 2 grain feature point extraction and feature distance calculation are carried out, wherein distance transforming, scale space generating, Gaussian difference scale space constructing, scale space feature point seeking and feature distance calculating are carried out; and 3 automatic grain boundary extraction is carried out, wherein initial contour drawing, grain boundary level set evolution extracting and final grain boundary determining are carried out. The results are as follows: (1) the distance between the two layers is calculated; Grain boundaries. A sift algorithm is used to acquire an image feature point after distance transformation and calculate the corresponding feature distance, and the initial contour is constructed to improve the evolution efficiency and precision of a level set algorithm. The requirements of high precision grain boundary extraction and grain measurement are effectively met. The defects of low precision and poor effect of grain boundary extraction and grain measurement in the prior art are overcome. The method is easy to understand and has the advantages of high applicability and high accuracy.

The invention discloses a fixed subarray space-based millimeter wave beam forming method based on a depth complex network, which simplifies a radio frequency pre-coding matrix and a radio frequency merging matrix through a fixed subarray structure, greatly reduces the requirements of a phase shifter, reduces the hardware dependence and reduces the failure rate compared with the prior art. By constructing the neural network which inputs the real part and the imaginary part of the complex number respectively and is fully connected with the lower-layer neurons, compared with a traditional methodin which the real part and the imaginary part need to be separated into two networks for processing, computing resources are saved; through the design of weight and offset parameter shared imaginary part neuron and real part neuron node expressions, half of network parameters are reduced, the system complexity is greatly reduced, hardware resources are saved, the operation rate is increased, and the real-time performance is improved.

Statistical timing analysis methods for circuits are described which compensate for circuit elements having correlated timing delays with a high degree of computational efficiency. An extended canonical timing model is used to represent each delay element along a circuit path, wherein the model bears information regarding any correlations that each element has to any other elements in the circuit (and / or to any external global factors, e.g., global temperature variations over the circuit, etc.). The model can be represented in a vectorized format which allows enhancement of computational efficiency, wherein the coefficients of the vectors allow an objective measure of element correlation (and wherein the vectors can be “pruned” by dropping insignificant coefficients to further enhance computational efficiency). A decomposition procedure can be used to decompose correlated elements into uncorrelated elements to allow delays to me more easily propagated through the timing diagram representing the circuit. Finally, a bounded approximation for the output of the MAX operator is described which provides a safely conservative approximation regardless of the linearity of the MAX output.

The invention discloses a face emotion recognition method and device based on deep learning, equipment and a medium. The method comprises the following steps of: inputting a to-be-identified image into a preset deep learning model, performing channel data extraction on the to-be-identified image by using an input layer in the deep learning model; obtaining face image data of the to-be-identified image; carrying out depth convolution calculation on the face image data by using the first convolution core of the convolution layer to obtain feature data, carrying out point convolution calculationon the feature data by using the second convolution core of the convolution layer to obtain face features of the to-be-identified image, and finally, carrying out classification regression on the facefeatures by using a full connection layer to obtain the emotional state of the to-be-identified image. According to the embodiment of the invention, the standard convolution operation in the traditional deep learning algorithm is decomposed into the calculation of one deep convolution and the calculation of one point convolution, so that the calculation amount of the deep learning model is reduced, and the operation rate of the emotion recognition model is improved.

The invention provides a three-dimensional face and eyeball movement modeling and capturing method and system, and the method comprises the steps: firstly extracting the position information of a feature point of a face in a shot scene image, and obtaining the information through employing a convolutional neural network method; after sparse facial feature points are obtained, a multilinear model of a human face serving as a prior condition of the model, the positions of the feature points detected on the image serving as observation results, and designing a maximum posteriori framework to optimize and solve the geometrical shape and posture of the human face in the image; aiming at the movement of a three-dimensional eyeball, using sparse two-dimensional feature points for obtaining an eyearea image block through cutout, achieving alignment of the image block through position information of the feature points, further marking the areas of an iris and a pupil, and reconstructing the eyeball movement in real time through an analysis method based on synthesis; in combination with the expression parameters obtained in the previous step, combining facial expression motion with eyeballmotion to obtain a complete facial expression animation.

A transient state polarization radar waveform acquisition method and a radar signal transmission method based thereon relate to a method for designing constant module phase encoding enabling a transient state polarization radar to meet good related performance. The methods provided herein aim to solve the problems of high side lobe of the transient state polarization radar waveform and the existing influence of reflected signals of other distant units received by the radar to the reflected signals of a target distant unit. In this invention, firstly, a shift matrix is utilized to establish a function related to a waveform vector; a strong scattering body and a position of a target in a radar scene are ensured and a distant side lobe section needed to be suppressed is estimated so as to construct a waveform optimization objective function; a time-domain optimization formula is transformed into a frequency-domain optimization formula; lastly, self-correlation and cross-correlation requirements of two waveforms are ensured and the waveforms are optimized; and the two waveforms are used as radar signals for transmitting and receiving. According to the invention, the related side lobe can be suppressed; the distant side lobe interference between various scattering bodies distributed along the distant direction is reduced; and the reflected signals of the other distant units are suppressed, thus improving the signal ratio.

The invention discloses a conveying belt longitudinal tearing online detecting method based on line laser images, belongs to the field of nondestructive detection, and solves the speed and accuracy problems of conveying belt longitudinal tearing online detection. Based on the characteristics of the line laser images in longitudinal tearing of a conveying belt, a laser stripe skeleton, a domain differential determining break point position and a second derivative determining fluctuation abnormal position are extracted by a maximum method to detect and mark a conveying belt longitudinal tearing fault area. The method can quickly and accurately detect the conveying belt longitudinal tearing fault online, and is excellent in application value in industrial application.

The invention belongs to the technical field of marine coral biological research, and particularly relates to a coral distribution and health condition evaluation method based on deep clustering analysis. Acquired video images are spliced into a large image through an improved image splicing algorithm, target recognition detection is performed on warm water coral through a deep learning algorithm,then image segmentation is performed on a recognition target through a k-means segmentation algorithm, and finally coral distribution and health conditions are researched. According to the method provided by the invention, the image splicing efficiency is improved; smooth seamless splicing of the coral images is realized; the coral in the image is rapidly detected by using the YOLOV3, the coral coverage rate is improved by calculating the coral area through image segmentation, so that the overall calculation rate of coral distribution and health conditions is greatly improved, and a good effect is achieved for researching the coral reef distribution condition and health conditions.

Described herein is a method for recognising a human head in a source image. The method comprises detecting a contour of at least part of a human body in the source image, calculating a depth of the human body in the source image. From the source image, a major radius size and a minor radius size of an ellipse corresponding to a human head at the depth is calculated, and, for at least several of a set of pixels of the detected contour, generating in an accumulator array at least one segment of an ellipse centred on the position of the contour pixel and having the major and minor radius sizes. Positions of local intensity maxima in the accumulator array are selected as corresponding to positions of the human head candidates in the source image.

The invention discloses a rehabilitation training motion state monitoring method and system fusing electrocardiogram and myoelectricity characteristics. The method comprises the following steps: obtaining an electrocardiogram signal and a myoelectricity signal and performing characteristic extraction respectively; performing characteristic fusion based on a random fusion coefficient vector to obtain a fusion characteristic vector; learning a multi-classification motion state recognition model based on a support vector machine by adopting the fusion characteristic vector; performing iterative optimization on the fusion coefficient vector by adopting a particle swarm algorithm to obtain an optimal fusion coefficient vector and a multi-classification motion state recognition offline model; and performing motion state recognition based on the fusion characteristic vector of the electrocardiogram signal and the myoelectricty signal collected in real time. According to the invention, electrocardiogram and myoelectricity signals are fused for motion state detection, so accuracy and reliability are greatly improved, and real-time monitoring of the motion state of rehabilitation training ofa patient is achieved; the motion state can be used as the basis for real-time adjustment of rehabilitation training task intensity and a control strategy, the rehabilitation training effect is enhanced, and secondary injuries caused by excessive training are avoided.

The invention discloses an FPGA-based phase generation carrier demodulation system, and relates to the technical field of optical fiber phase demodulation. The PGC demodulation system uses an FPGA asa signal processor, and can improve the operation rate and demodulation speed of the system. The PGC demodulation system comprises a PGC demodulation module, a data caching module and an asynchronouscommunication module which are connected in sequence. The input to-be-demodulated data is directly input into the data cache module for storage. The PGC demodulation module adopts an improved PGC demodulation algorithm to demodulate the data to be demodulated. A disturbance removing unit is introduced into the PGC demodulation module. The division operation is carried out in the disturbance removal unit to eliminate signals containing alternating current items and modulation depths. Therefore, the output item only has the tangent value of the phase signal. The dependence of the demodulation result on the modulation depth introduced by an external carrier is reduced. The uncertainty of the demodulation result caused by unstable factors such as a light source can also be avoided. The accuracy and the stability of system demodulation are improved.

The invention discloses a complex network theory-based water route vulnerability research method. The complex network theory-based water route vulnerability research method comprises an implementationscheme and a research method, wherein the research method comprises a complex network theory and numerical calculation; and the complex network theory comprises a basic conception and feature parameters. According to the method, a ship route network model is established; and MATLAB software is utilized to calculate feature parameters of a complex network, wherein feature parameters comprise degree distribution, a clustering coefficient, a relative size of a maximal connected subgraph, an average efficiency of the network and a tightness index. The numerical calculation pays attention to the stability of calculation, is capable of controlling the growth momentums of errors and ensuring the stability of the calculation process, and pays attention to high calculation speed and high calculation precision, so that the numerical calculation is capable of correctly judging the property of the network and has reliability for the researches.

This invention discloses a double point computation circuit used for elliptic curve code algorithmic chip. Its characteristics rely in: a control module used for separation of big integer K value, pre-computation and logical control, double point module and the dot piling module; the control module separates the big integer K value into m small integer Ki, and pre-computes the corresponding point Pi integrated to point P; then under the logical control of the control module, respectively carry out double point calculation in the corresponding m double point modules, send the calculated value to the dot-piling module to implement dot-piling computation, and output big integer K and the dot-piling computation value of the point P. This m circuits paralleled dot-piling computation circuit has apparent effect on the improvement of the performance, and m circuits paralleled computation performance is as m times as that of the mono-way serial double-point computation. Because the double-point computation takes up 95% time of the whole elliptic curve code algorithm, so the improvement multiple of the whole elliptic curve code algorithmic chip is m.

The invention discloses a real-time SAR imaging system and an imaging method based on a multi-core DSP. The imaging system comprises an A / D input module, an FPGA module, a DSP module and a D / A outputmodule. The A / D input module is used for sampling imaging echo signals. The FPGA module is used for receiving the imaging echo signals, carrying out preprocessing, and sending interruption to the DSPmodule after the preprocessing is completed. The DSP module is used for carrying out azimuth and distance focusing on the preprocessed imaging echo signals and acquiring an imaging result. The D / A output module is used for generating the modulation signal of a radio frequency transmitter. In the invention, through an EMIF asynchronous interface, high speed data transmission between the multi-coreDSP and FPGA is realized; the multi-core DSP parallelly processes data so that high speed data processing is realized; and problems that the data size of the SAR imaging system is large, a real-time requirement is high and so on are effectively solved.

The invention discloses an accelerometer combined LED indoor 3D positioning method. LED lights are used as emitters and send ID codes with their position information. A mobile terminal is used as a receiver. In this method, the receiving power intensity technology in triangulation method is used and at the same time, the confidence interval algorithm is combined to realize 3D positioning. While considering the rotation angles into the positioning method, the receiver with accelerometer is used to receive the emitted light from the LED lights and capture their receiving power. Through the angle information of the accelerometer, the normal directions of the receiver under different rotation angles can be obtained. A detailed calculation algorithm for incident angles is introduced so as to quantize the rotation angle of a handheld device as an incident angle finally. The Z coordinate of the receiver is introduced, and the positioning error is controlled in a set scope through the two-layer iteration. At the same time, the change of the Z coordinate of the receiver is not more than 5cm, so as to overcome the influence of the upper and lower jitter on the positioning error and provide higher positioning accuracy. The positioning accuracy can be increased from meter grade or decimeter grade to centimeter grade.

The invention discloses a method for obtaining space electromagnetic intensity data. The main problems that electronic equipment can not work normally, or target interference is high because in the prior art, calculation speed is low are solved. The method includes the steps that (1), the number and parameters of radar transmitters and communication transmitters are determined, and sampling frequencies and the sampling number of the radar transmitters and the communication transmitters are respectively calculated; (2), radar signals and communication signals are sampled respectively, and Fourier transformation is performed on the sampled signals to obtain a corresponding signal frequency spectrum; (3), an observation point is set; (4), a radar signal superposition frequency spectrum and a communication signal superposition frequency spectrum of the observation point are obtained through calculating the signal frequency spectrum; (5), according to the superposition frequency spectra, signal total power is calculated, so that electromagnetic intensity is obtained; (6), step3, step4 and step5 are repeated for a whole observation area, so that electromagnetic intensity data of the observation area are obtained. The calculation amount is reduced substantially, responding speed of the electronic equipment is increased, and the method can be used for analyzing the electromagnetic environment and avoiding mutual interference of kinds of electronic equipment.

The invention discloses a saliency detecting method applied to static image human segmentation. The method comprises the following steps of performing superpixel segmentation on a static image to be detected; performing face detection on the image after the superpixel segmentation to acquire a face area; performing skin color detection on the face area to acquire skin color information; performing color uniqueness calculation and color spatial distribution calculation according to the skin color information to acquire a color uniqueness value with the skin color information fused therein and a color spatial distribution value with the skin color information fused therein; performing saliency calculation according to the acquired color uniqueness value and the color spatial distribution value to acquire a saliency map used for static image human segmentation. Based on a conventional saliency detecting method, the method provided in the invention makes the skin color well detected by making the skin color information fused in the color uniqueness calculation and color spatial distribution calculation. The human skin color saliency is enhanced. Better and more accurate static image human segmentation effects are achieved. The method can be widely applied to the image processing field.