150results about How to "Meet the requirements of real-time processing" patented technology

The invention discloses a multi-surveillance-source flying target parallel track processing method. The method includes the steps: multi-surveillance-source data receiving; multi-surveillance-source data analysis; radar data processing; ADS-B (automatic dependent surveillance-broadcast) data processing; multi-surveillance-source data fusion. Surveillance of quality of data accessing to radar is realized by monitoring and analyzing quality of radar signals. In addition, real-time receive processing of the radar data is realized by means of multithreading, high safety, high reliability and high usability of a data processing system can be further guaranteed, and accuracy and quickness in track processing of flying targets in different data types from different surveillance sources can be realized.

The method comprises: reading out a digital image; saving the color and gray values of each pixel into an allocated memory; dividing the inputted image into a light illumination image and a reflection image; respectively making the treatment for both images; after treatment, said two images are combined into one image that is outputted to the output device. By the invention, the digital photos can get a high dynamical range (from the most brightness to the most darkness) according to the partial image information obtained at different light condition.

The invention discloses a dual-aperture photoelectric imaging system-based unmanned aerial vehicle detection and recognition system. According to the dual-aperture photoelectric imaging system-based unmanned aerial vehicle detection and recognition system, a wide-angle camera and a long-focus camera are adopted as an unmanned aerial vehicle detection device and recognition device respectively for performing monitoring; and a monitoring image sequence is acquired, a target detection algorithm is adopted to detect an unmanned aerial vehicle suspected target, a pattern recognition algorithm is adopted to recognize the suspected target, and after the target is confirmed, the track is tracked, interfered and controlled. Photoelectric sensors are adopted as the unmanned aerial vehicle detection device and recognition device, and therefore, the dual-aperture photoelectric imaging system-based unmanned aerial vehicle detection and recognition system has the advantages of high reliability, low cost and the like; the suspected target is searched in a large space region by means of a wide-angle imaging system, and the target is confirmed and tracked by means of a long-focus imaging system which is a two-axis turntable, and therefore, requirements for high detection rate and high accuracy can be satisfied, the reliability of the system can be greatly improved, and therefore, the system can bring outstanding economic benefits and has high practical value.

The invention discloses a traffic flow data recovery method based on space-time correlation. The method includes a front-stage traffic data conversion and abnormal data screening method and a follow-up traffic flow data recovery method. The data screening method relates to a threshold value screening method, a zero data screening method and a quality screening method according to the abnormal condition of actual traffic flow data. According to time correlation and space correlation of traffic flow data, by the combination of a time sequence method and a multiple linear regression method, namely the data recovery method based on temporal correlation and spatial correlation, the comprehensive traffic flow data recovery method based on space-time correlation is designed. The method is simple and quick, the real-time processing requirement can be met, and an acquired result is high in accuracy.

The invention discloses a detection method for remnants in the complex environment. The detection method comprises the following steps: comparing prospect targets obtained on the basis of both a partial updated Gaussian mixture background modeling method and a modified three-frame difference method; in combination with the YCbCr color space based shadow elimination method and the connected domain analysis, dividing to obtain temporary static briquettes within a scene; carrying out the centroid distance judging method on each frame of an image, and counting the rest time of each briquette; marking the static briquettes reaching to the temporal threshold as the remnants after eliminating the possibility of pedestrian residence through the HOG (Histograms of Oriented Gradients) pedestrian detection algorithm, and eliminating the possibility of pedestrian residence through the FAST (Features from Accelerated Segment Test) feature point detection algorithm. The detection method can improve detection accuracy of the remnants, lowers calculation complexity, can be better suitable for the complex environment crowded and frequent in shielding, and enhances the anti-interference capability.

The invention discloses an H.264 compression bit stream oriented video watermark method. The method comprises the following steps of: generating an authentication code according to a predictive mode of a 4*4 brightness block in an I film; and embedding the authentication code in the film-grade bit stream by modulating variable-length code words of some motion sub-blocks of a B film and a P film, wherein the modulation is performed based on a mapping rule between the variable-length code words and the bits to be embedded, and the mapped variable-length code words is extremely similar to the original variable-length code words. Because in the method, the video bit stream is unnecessarily pre-processed, and comparison and replacement operations are performed by selecting the proper variable-length code words only from the H.264 / AVC compression bit stream in the watermark embedding process without a huge number of operations, so that the method has the advantages of low computing complexity, high safety and high practicability, and can better meet the requirements of the real-time video processing.

In the system, the input module is used for collecting the inputted digital image; the collected digital images is inputted into the image dissection module; the image dissection module dissects image into a light illumination image L and a reflection image R, both are respectively inputted into the light illumination processing module and the reflection processing module; the light illumination processing module makes non-linear correction for the light illumination image L and output an corrected light illumination image L'; the reflection module makes the partial noise reduction process for the reflection image R corresponding to the dark area of the inputted image and outputs a noise-reduced reflection image R'; the image combining and outputting module combines L' and R' and then displays the combined image on the output device.

The invention relates to a scanned document image error correction method. The method comprises the following steps: converting into a gray-scale image; performing filtering; extracting an edge-point binary map; by means of projection analysis, scanning an inclination angle of a scanned document image; and according to Q, calculating boundary of a text area, accordingly, calculating an offset amount, and by means of translation operation, enabling the text area to be centered. According to the invention, through detecting inclination of the input scanned document image, inclination of an original document image is rectified, and a document image with better visual quality can be obtained.

The invention relates to a method, a system and a device for synthesizing an image, belonging to the technical field of image processing. The method comprises the steps of: acquiring exposed images oftwo different exposure times of a target photographic subject, analyzing pixel point data of the same position in the two exposed images to obtain an image fused weight value, and fusing the two exposed images by using the weight value to obtain a composite image, wherein the composite image is synthesized by images of different exposure times, and the pixels at the same position are capable of capturing more image details, thereby improving the dynamic range of the composite image. At the same time, only two images with different exposure times are needed in the process of synthesizing the images, the amount of image calculation is reduced compared with the traditional technology, by which the synthesis process uses multiple images, which can meet the real-time requirements of image processing.

The invention provides a radar target tracking method based on measurement conversion and relates to the field of the radar target tracking. The radar target tracking method mainly comprises three parts, namely the first part of obtaining a mean value and a covariance matrix of measurement conversion errors based on measurement values, the second part of obtaining the mean value and the covariance matrix of the measurement conversion errors based on predicted values in a spherical coordinate system, and obtaining parameters involved in the mean value and the covariance matrix by virtue of nonlinear transformation and Jacobian transformation matrix and by use of a predicted state and a prediction error covariance matrix which are estimated by a filter under a rectangular coordinate system, and the third part of selecting the mean value and the covariance matrix both relatively higher in accuracy from the mean values and the covariance matrices obtained in the first two parts as the final mean value and the final covariance matrix of the measurement conversion errors, carrying out a recursive filtering algorithm and realizing target tracking. In the target tracking, the method is high in tracking accuracy and fixed in operand, capable of better meeting the requirements of real-time processing and high in reliability.

The invention discloses a fence vibration intrusion positioning and mode recognition method based on a distributed optical fiber system. The method comprises a step of arranging distributed optical fibers on a fence, obtaining a vibration signal of the fence and storing vibration data, a step of accumulating the vibration data of all detection points on the fence into a time-space two-dimensionalmatrix A(x, t), filtering the space-time two-dimensional matrix by utilizing a Sobel operator, counting the times that the position of each detection point is larger than a set threshold value M in atime period after filtering, taking the detection point as a suspicious invasion point if the times that the position of each detection point exceeds the set threshold value M is larger than a set threshold value N and storing original vibration signals of all suspicious invasion points, a step of obtaining wavelet time-frequency graphs of the original vibration signals of all the suspicious invasion points, and a step of inputting the wavelet time-frequency graphs after the suspicious intrusion points are scaled into a convolutional neural network pre-trained by utilizing known event data inadvance. According to the method, the position coordinates and the event type of an intrusion event can be accurately identified, and meanwhile, the requirement of relatively good real-time performance is met.

The invention belongs to the radar signal processing field, and discloses a missile-borne SAR imaging system architecture design based on a single-chip FPGA. The single-chip FPGA comprises a processing module PS and a programmable logic module PL; the PS module is provided with a double-core ARM and a DDR storage device; the PL module is internally provided with an FPGA module, a RAM module and a DMA module; the FPGA module is used for obtaining an intermediate frequency digital echo signal, performing digital down-conversion and range direction pulse compression and storing the result in the RAM module. The core 0 of the ARM successively performs Doppler center estimation, distance moving correction, distance bending correction and Doppler frequency modulation estimation on the distance pulse compression image. The FPGA module performs motion error compensation, azimuth nonlinear scaling and azimuth pulse compression on the image after distance direction correction. The core 1 of the ARM performs multi-view processing and quantification processing on the azimuth pulse compression image to obtain an SAR image data.

The invention discloses a parallel random sampling consensus (RANSAC) method. An assuming stage and a verification stage in the parallel RANSAC method are performed parallelly. Specifically, the method comprises the steps of parallelly acquiring m groups of random samples ai(i=1,2,...,m) from a data set A with N data numbers in the assuming stage and parallelly outputting m estimated models Mi(i=1,2,...,m) according to the m groups of random samples ai(i=1,2,...,m); adopting the m estimated models Mi(i=1,2,...,m) to parallelly vote the same point in the verification stage and gradually and directly judging whether each point in the data set A is an inside point or not. The invention further discloses a parallel RANSAC device. By adopting the parallel RANSAC method and the parallel RANSAC device, the performance and data throughout amount of an RANSAC algorithm can be improved, and the demand for high-speed real-time processing can be met.

The invention discloses a water falling detection and rescue control system based on deep learning, belonging to the field of computer vision, and the method comprises the steps: building an underwater personnel image data set; using a LabelImg tool to mark the position of a person in water and the category of the person in water in the original image, and storing the marking information generatedby each image in an xml file format for network training; using a YOLOv2 deep learning target detection framework i for training a data set, making clustering analysis on the established data set before network training, and dividing the data set into a training set, a verification set and a test set according to the proportion of 6: 2: 2. The rescue control system comprises a monitoring video input unit, a drowning person detection unit and an alarm rescue unit. The response efficiency of water surface rescue is effectively improved, and the survival probability of people falling into wateris improved.

The invention provides an intelligent slag removing method of molten iron based on machine vision, which comprises the following steps: collecting image information of a target area in real time; setting a first region of interest according to the image information collected in real time for judging whether the slag scraping work is being carried out; when the slag scavenging work is in progress,continuously performing image recognition on the first region of interest until the end of the slag scavenging work is detected; setting a second region of interest according to the image informationcollected in real time when the slag scraping is not carried out; determining a region to be stripped according to the second region of interest; carrying out slag scraping on the area to be scraped.According to the invention, the area with the most slag content on the surface of molten iron can be effectively and accurately measured and the signal is returned to the automation equipment for subsequent slag scraping, and the efficiency and accuracy of slag scraping can be significantly improved. The method has the advantages of easy realization, small calculation amount and strong stability,and can also meet the requirements of real-time processing and ensure the accuracy of slag scraping.

The invention provides a method for face deformation in a video stream, and the method comprises the steps: carrying out the face deformation of each frame in a plurality of frames of images in the video stream, wherein the method of the face deformation of any frame of image comprises the steps: determining a plurality of initial points on a face in a current frame of image through employing a preset first algorithm; according to the initial point and / or the key point in the related frame image, adjusting the position of the initial point in the current frame image through smoothing processing, the initial point subjected to smoothing processing serving as the key point; adopting a preset second algorithm, carrying out triangulation according to the key points, and obtaining multiple triangles with the key points as vertexes; and according to the type of the face deformation to be performed, moving at least part of the key points by adopting a corresponding moving rule, and on the premise that the texture in the triangle is not changed, calculating the pixel value of each pixel point in the triangle with the moving vertex. The invention further provides a device for face deformation in the video stream, electronic equipment and a computer readable medium.

The invention relates to a moving image processing device and a moving image processing method. The device and the method are based on an atmosphere model so that videos in foggy days are clear, the image visibility is improved, and in addition, the real-time processing requirements of the images can be preferably met. According to the moving image processing device, in the video defogging process, the video is divided into a core frame used as a main frame and an ordinary frame used as a sub frame, the core frame is calculated again to be used as a distance parameter t(X) and an unplugged point parameter A, the ordinary frame is not subjected to A calculation, and the A of the core frame is used, the t(X) of a region corresponding to the core frame is used for the background part of the ordinary frame, and the t(X) is calculated again for the foreground part of the ordinary frame. According to the device and the method, the application speed of the single-frame image defogging algorithm based on the atmosphere model in the defogging clarifying processing on the moving images such as videos and the like can be accelerated, a good defogging effect can be obtained, and in addition, the real-time performance of the moving images is ensured.

The invention discloses a non-adaptive airborne non-side-looking radar short-range clutter suppression method. A non-adaptive short-range clutter suppression method formed by space-time filtering and space-time matching is used for suppressing airborne radar short-range clutter. The method comprises the steps of firstly arranging received signals of all channels of radar into a column vector, then performing space-time filtering to the received signals to suppress clutter energy, and finally performing space-time two-dimensional matching to realize the detection of a target. The non-adaptive airborne non-side-looking radar short-range clutter suppression method overcomes the defects that a great number of training samples which satisfy independent and identical distribution conditions are needed when the traditional adaptive clutter suppression method is applied to airborne radar, the calculation amount is great and the performance during short-range clutter suppression is seriously decreased. Compared with the traditional adaptive clutter suppression method, the method provided by the invention has the advantages that since the method is a non-adaptive method, the method is not influenced by non-uniform samples, the calculation complexity is low, the clutter suppression can be conveniently and quickly realized and the detection of the target is more greatly facilitated.

The invention discloses a real time self-adapting processing method of anastigmatic imaging, which is a real time self-adapting processing method of anastigmatic imaging in a high-speed movement small-object imaging system. According to the method, small objects in high-speed movement can be automatically searched and found, FPGA and DSP are used as the real time on-line processing platform, an image reinforcement and inter-frame correlation identification algorism with low complexity is adopted, effect of fake objects are removed to obtain movement characteristics of the objects, so as to drive a high-precision two-dimensional piezoelectricity tilting mirror to complete self-adapting movement compensation, thereby realizing clear imaging of the high-speed movement small-objects.

The invention relates to the field of computer vision and machine learning, in particular to a method for detecting floating objects on water based on a YOLOv2 network. The method comprises the stepsof: 1, collecting data: 2, data enhancement: performing data enhancement of the data set A; 3, marking the picture: marking the floating object area on the water in the data set B with a rectangular frame, and 4, training the module: the purpose of dividing dataset B into three parts is to select the best weight model with the best generalization ability; 5, detecting the module: the trained weight model is used to detect the monitored river or lake video. The invention has the beneficial effect of replacing the traditional detection method based on artificiality, saving manpower and materialresources, and judging the pollution degree of a river or a lake. The invention randomly divides the data set into a training set, a test set and a verification set, and expands the sample through a data enhancement method to prevent the over-fitting problem caused by too few image samples.

The invention discloses a load identification method based on an improved probabilistic neural network. The load identification method comprises steps of adopting a binary system to perform coding on a load type in an electricity usage network, establishing a coding library of load types, collecting an electric parameter of each load type, establishing a non-linear mapping relation between the electric parameter and the code of each load type, creating a probabilistic neural network, training the probabilistic neural network to obtain an error function, using the error function as a fitness function of a particle swarm algorithm, adopting the particle swarm algorithm to perform optimization on a smooth factor of the probabilistic neural network to obtain an optimal smooth factor, updating the probabilistic neural network according to the obtained optimal smooth factor to obtain an improved probabilistic neural network and performing recognition on the load type in the electricity usage network on the basis of the probabilistic neural network. The load identification method based on the improved probabilistic neural network can simultaneously identify multiple loads, improves speed of identifying a pernicious load under a multi-load mode, and can better realize recognition of restricted electric appliances and electricity usage control under a condition of multiple electric appliances.

The invention relates to the field of computer vision and machine learning, in particular to a method for detecting floating objects on water. The method comprises following steps of: 1, data acquisition; 2, data enhancement; Data enhancement is performed on a dataset A. 3, marking that picture; Marking the floating object area on the water in the data set B with a rectangular frame, and 4, training the module; The purpose of dividing dataset B into three parts is to select the best weight model with the best generalization ability. 5, detecting that module; The trained weight model is used todetect the monitored river or lake video. The invention has the beneficial effect of replacing the traditional detection method based on artificiality, saving manpower and material resources, and judging the pollution degree of a river or a lake. The invention randomly divides the data set into a training set, a test set and a verification set, and expands the sample through a data enhancement method to prevent the over-fitting problem caused by too few image samples.

The present invention relates to an electric spark clearance discharge condition detector which is in the special processing field and is based on wavelet transformation and a method thereof, wherein an analog optical coupler is connected with an electric spark machine tool through an inter-pole voltage signal outgoing cable. An analog-to-digital conversion module is connected at the back end of analog optical coupler. A DSP is connected at the back end of analog-to-digital conversion module. A CPLD is connected with the DSP, the USB interface and an SDRAM. A FLASH memory is connected with the DSP. The USB interface is connected with an upper machine through USB cable. The analog-to-digital conversion module samples the inter-pole voltage signal in electric spark machining and executes wavelet transformation procession to the signal in DSP. The wavelet coefficient comprising discharge condition information is obtained. The discharge condition of prior electric spark machining is obtained through the classifying decision and statistics summation of maximal value and minimum value of wavelet coefficient. The discharge condition of prior electric spark machining is transmitted to an upper machine. The electric spark clearance discharge condition detector of the invention has the advantages of excellent real-time property, excellent stability, excellent reliability, excellent accuracy and wide application sphere.

The invention discloses an abandoned object detection method. Brand-new environment self-adaptation double background models are built, and the background models are a front background model and a back background model respectively; independent update mechanisms are set for a first background image of the front background model and a second background image of the back background model; the first background image is updated according to a scene image; the second background image is updated according to the updated first background image; for a detected static target position, selective local updating is performed; the first and second background images are subjected to difference value operation; the influence of a shadow of the static target position in natural light on the detected static target position is eliminated by using a shadow inhibition technology; and the images are processed by a morphological processing algorithm, a contour extraction algorithm and the like to form an abandoned object region. The method has relatively high environmental adaptability, timeliness and robustness.

The invention relates to an IDL based method for three-dimensionally visualizing medical images. The method comprises the following steps of: converting DICOM data into three-dimensional visualized medical images through volume rendering algorithm, converting the medical images into a virtual reality data model through VRML interactive description, and controlling the displayed graphics state through a human-computer interface on a GUI (Graphical User Interface) and a control interface so as to form visualized virtual images, wherein the volume rendering algorithm comprises the following steps of: defining an object in an object graphics system; calling the object through an object-creating OBJ_NEW function; respectively calling a pattern object, a viewing object, viewing group objects and a scene object by utilizing ADD calling format; drawing the scene object in a window object by utilizing a window object DRAW method; and completely deleting the objects by utilizing the OBJ_DESTROY function after display is accomplished. The method has the advantages of meeting the basic requirements of visualization of the medical images, and being capable of carrying out corresponding operations, such as volume rendering, slicing, and the like.

The invention discloses a method for measuring a target azimuth angle by using a vehicle-mounted radar and a vehicle-mounted radar. According to the method and the vehicle-mounted radar, spatial domain echo signals are obtained according to the received echo signals of a target; an original spectral peak position corresponding to the peak value of an original spatial spectrum obtained according tothe spatial domain echo signals is determined; a to-be-estimated spatial spectrum angle range interval is determined based on the original spectral peak position; a to-be-estimated spatial spectrum angle is set; a preset APES algorithm is adopted for the angle so as to obtain the estimation result of a target spatial spectrum; and the target azimuth angle of the target is estimated based on the estimated spectral peak position of the target spatial spectrum. According to the method, the to-be-estimated spatial spectrum angle range interval is obtained through fast Fourier transform, so that the value range of the original to-be-estimated spatial spectrum angle can be narrowed; a Q matrix in the preset APES algorithm is obtained only according to a sample correlation matrix, so that the Qmatrix does not transform along with the to-be-estimated spatial spectrum angle; and inversion operation needs to be performed on the Q matrix once, and therefore, the calculation of super-resolutionangle measurement can be realized in a short time.

The invention discloses a moving object classification method, which comprises the steps of extracting multiple groups of vehicle images through a traffic monitoring system, acquiring features of one group of the vehicle images by learning through adopting a sparse coding algorithm, performing secondary selection on the image features by adopting a feature selection algorithm, carrying out feature extraction and screening on the vehicle images extracted from a traffic management system according to the selected features, and finally regarding the vehicle features as input features of a classifier for classifying vehicles. The moving object classification method provides efficient solutions for a visual collision problem and a vehicle classification problem in the traffic video monitoring system respectively: with regard to the visual collision problem, feature vectors and area of gaps between the vehicles are defined, so that the visual collision problem is converted into a simple optimization problem to be solved; and with regard to the vehicle classification problem, the features of the images are obtained by learning through adopting the sparse coding algorithm, and the features are subjected to secondary selection.

The invention provides a color gamut expanding system comprising a signal input unit, a forward conversion unit, a color gamut expanding unit, an inverse conversion unit and a signal output unit; wherein, the signal input unit is used for inputting pixel color information; the forward conversion unit is used for converting the pixel color information into values of L, a and b in liner Lab color space; the color gamut expanding unit is used for receiving color gamut expanding coefficients, and expands the values of L, a and b provided by the forward conversion unit into new values of L, a and b in the linear Lab color space according to the color gamut expanding coefficients; the inverse conversion unit is used for converting expanded values of L, a and b into the pixel color information of a large color gamut; the signal output unit is used for outputting the color information of the large color gamut. The invention also provides a corresponding color gamut expanding method. The invention can realize continuous adjustment, has high calculation efficiency, can better avoid the phenomenon of hue deviation, and has higher precision and accuracy.

A method for interpolating a curve with a variable interpolation cycle on the basis of interpolation precision and control on maximum accelerations of various driving shafts of a machine tool includes operating steps of (1), approximating the curve to be interpolated by large quantities of small linear segments similar to the interpolation step length on the basis of the set interpolation precision so that approximation errors among the small linear segments and the curve to be interpolated meet requirements of the interpolation precision; and (2), determining the interpolation cycle of each small linear segment and the quantity of the interpolation cycles according to the programming speed, the maximum accelerations of the driving shafts of the machine tool and limitation of an interpolation cycle range of a numerical control system. The method has the advantages that data transmission quantity is reduced, the interpolation precision is guaranteed, the machining efficiency is improved, computational complexity of curve interpolation is reduced effectively, and ,machining errors caused by vibration of the machine tool when the speed of the machine tool is increased or decreased suddenly are reduced; interpolation can be carried out according to the set interpolation precision and the power performance of the machine tool, and the method is particularly suitable for precision machining; and backtracking processing is omitted, the computation speed is high, the performance is stable and reliable, and real-time machining requirements are met.