297 results about "Morphological filtering" patented technology

The invention relates to a self-adaptive background modeling and moving target detecting method which comprises the following steps: establishing an initial background, extracting a moving target binary mask, updating a self-adaptive background and detecting a moving target: firstly, determining the pixel gray value of the initial background by using an inter-frame difference method; successively, carrying out binarization treatment on a difference image by using a self-adaptive threshold valve when a difference image is obtained by a current frame image minus a background image and post-treating of morphological filtering and the like to obtain the moving target binary mask; then, carrying out dynamic update on the gray value of a pixel which is not corresponding to the moving target binary mask in the background image by adopting an region-based background updating method; and finally, carrying out logic 'and' operation by using the moving target binary mask and the current frame input image to detect the moving target. The invention can effectively establish the reliable initial background and carry out real-time dynamic update on the background to solve the problem that the detection accuracy of the moving target is influenced by background disturbance and illumination change.

The invention discloses a change detection method based on a high-resolution remote sensing image. The method is characterized by using a superpixel segmentation and synthesis algorithm to segment a multi-temporal remote sensing image after carrying out necessary ortho-rectification, image registration, histogram matching and other preprocessing, using a superpixel as a unit to carry out local characteristic calculating and sample selection, and realizing the automatic annotation of a changing area possessing an obvious tendency or an unchanged area in the image; and then, taking an annotationresult as a sample to train twinborn convolutional neural networks, classifying image changing conditions, carrying out noise reduction, morphological filtering and other postprocessing, and acquiring a final changing detection result. An experiment shows that in a high-score number-two satellite remote sensing image data set, each index in the method is better than each index in a traditional change detection algorithm; a Kappa coefficient is averagely increased by 0.3; and an average overall error rate is less than 3.5%. The detection result has a high practical value.

A fingerprint recognition method includes iterative gamma correction that compensates moisture effect, feature extraction operations, directional morphological filtering that effectively links broken ridges and breaks smeared ridges, adaptive image alignment by local minutia matching, global matching by relaxed rigid transform, and statistical matching with Gaussian weighting functions.

The invention discloses a feature analysis-based video flame detecting method. For a video image acquired by a monitoring camera in a fixed visual field. the method comprises the following steps of: firstly, performing binary cutting of the image by maximum between-cluster variance method and acquiring a foreground region; secondly, removing noise points by morphological filtering, and counting a plurality of features of the foreground region, such as colour illumination, geometry, diffusion property, irregularity, sparkling frequency and the like; and finally, comprehensively judging the calculated features of the foreground region to judge whether the video image has flame or not. The binary cutting of the image by maximum between-cluster variance method can reduce the influence of a large amount of disturbances. At the same time, in order to overcome the defect of single or few discriminating features of the currently usually used flame detecting method, the method extracts the plurality of features of the foreground region acquired by the maximum between-cluster variance method to make a comprehensive judgment, so the method realizes quick and accurate judgment that whether the video image has flame or not and greatly reduces false alarm rate.

The invention discloses a building area extraction method based on LiDAR data, which belongs to the field of extraction of building through the LiDAR data and includes early-age processing of the LiDAR data, resampling of original LiDAR data, reverse iteration mathematical morphological filtering and separation of building and dense trees. The building area extraction method directly processes three-dimensional (3D) point cloud data instead of converting the point cloud into depth images, avoids information loss and added calculation quantity in the conversion process, simultaneously utilizesdifferent windows to conduct mathematical morphological filtering operation through reverse gradual iteration, basically removes effects of terrain fluctuation on the building in the mathematical morphological filtering and obtains high extraction accuracy. The building area extraction method can quickly and accurately extract LiDAR points belonging to the building area from a large range of LiDAR points and is capable of providing reliable data support for urban three-dimensional modeling.

The invention discloses a video smoke detection method based on a mixed Gaussian model and morphological characteristics. The method comprises the following steps of: acquiring a foreground motion area by adopting a mixed Gaussian model algorithm aiming at a video image acquired by a supervisory camera of a fixed view field; then removing noise points by adopting morphological filtering; counting the morphological characteristics of the motion area, such as the diffusion velocity, the geometry, the ambiguity, the irregularity, and the like; and finally, carrying out a synthetic judgment on the counted characteristics of the motion area so as to judge whether smoke exists in the video image or not. The self-adaptation mixed Gaussian model (i.e. each pixel is modeled by utilizing mixed Gaussian distribution) can be used for reliably eliminating the influences of interferences of illumination changes, messy background motions, ripples, leaf wobbles, rain, snow, and the like. The invention realizes the quick and accurate identification on whether the video image has smoke or not through the synthetic judgment of the detected various morphological characteristics of the foreground motion area and also greatly reduces the false alarm rate.

A method for background replacement. The method takes an input image of one or more frames of video, or a still image, and performs an initial classification of the pixels (14) as foreground or background pixels. The classification is refined (16) using one of several techniques, including anisotropic diffusion or morphological filtering. After the refined classification is completed, a feathering process (18) is used to overlay the foreground pixels from the original image on the pixels of the new background, resulting in a new output image (20).

The invention provides a cigarette smoke detection method based on video monitoring. The method is divided into a classifier training phase and a cigarette smoke detection phase. In the classifier training phase, geometrical characteristics and Hog characteristics of all prepared positive and negative sample sets are extracted, a support vector machine is used for training the extracted characteristic vectors, and a classifier is generated; and in the cigarette smoke detection phase, firstly, a Gaussian mixture model is utilized to obtain a foreground motion region from a video image obtained by a monitoring camera, then noisy points and cavities are removed by means of morphological filtering, projection histograms in X and Y directions are counted according to the characteristics of cigarette smoke, a precise interested region is obtained, finally characteristics of the interested region are extracted and combined into characteristic vectors, the characteristic vectors are input into the classifier, and whether the interested region has cigarette smoke is judged. The method provided by the invention has the advantages that the interference resistance is high, the detection sensitivity is high, the false alarm rate is low, the cigarette smoke can be recognized in realized, the position of the cigarette smoke is determined, and the cigarette control work of indoor public places is facilitated.

The invention provides a micro seismic event recognition method based on morphological filtering. The method comprises the steps of conducting spectral analysis on micro seismic data; rectifying a demodulator used for receiving micro seismic signals; selecting structural elements, combining opening operation and closing operation to form a morphological noise filter, and conducting the morphological filtering on the micro seismic data going through spectral analysis; calculating the energy ratio of a long-time window and a short-time window according to the data going through the morphological filtering, and comparing the energy ratio and triggering threshold to identify whether the demodulator has micro seismic events; and conducting microseism pickup according to the micro seismic events. According to the method, the signal to noise ratio of the data can be improved through preprocessing and further filtering treatment; the micro seismic events can be detected quickly and accurately by utilization of the energy ratio method, the fluctuation type of the micro seismic events can be identified through polarization analysis, and first arrival can be picked up accurately by utilization of self-adaptation threshold and the long-time and short-time window energy ratio method.

The present invention discloses a morphological filtering-based electrical imaging reservoir fracture and cave body quantitative characterization method and system. The method includes the following steps that: 1) noise suppression and blank band encryption interpolation are performed on original electric imaging logging data to obtain full-coverage rate electrical imaging logging data; 2) structural elements of appropriate shapes and scales are selected, and morphological filtering is performed on the obtained electrical imaging logging data in the step 1), so that fracture and cave pore distribution of different scales can be determined; and 3) the proportion of the porosity of fractures and the proportion of the porosity of corroded caves are calculated according to the fracture and cave pore distribution of different scales, so that a fracture and cave porosity spectrum which can indicate pore throat size and porosity distribution relationships can be obtained. According to the method of the invention, a mathematical morphological filtering method is introduced into imaging logging data processing; based on the filtering and anomaly recognition technologies of conductivity signals of different scales, the signal-to-noise ratio and identifiability of fracture and cave abnormal signals are improved; and exact information such as intuitive fracture and cave shapes and scale distribution can be provided for the processing and interpretation of electrical imaging logging data.

The invention discloses a moving platform moving object detection method and system based on an infrared image, and belongs to the field of machine vision. The method includes the following steps: extracting SURF feature points of an infrared sequence image, and matching the feature points; calculating an affine matrix and obtaining a registered image; correcting a pixel gray value of the registered image through linear regression, conducting inter-frame differentiation, and obtaining a binary image of a suspected moving object; conducting morphological filtering; and conducting statistics histogram correction on edge pixels of the suspected object through regional gradients, and obtaining a current frame detection result graph. The invention also provides a moving platform moving object detection system based on an infrared image. According to the technical scheme, the method exhibits great real-time performance and robustness for infrared sequence image moving object detection in a moving imaging platform, and is suitable for an infrared moving sequence image with uneven brightness changes.

The invention discloses an electrocardiosignal denoising method based on morphological filtering and lifting wavelet transformation. Firstly, three times of decomposing are conducted on electrocardiosignals f based on the lifting wavelet theory, three layers of high-frequency coefficients and three layers of low-frequency coefficients are obtained, a lifting threshold denoising method is used for processing the high-frequency coefficients, the high-frequency coefficients on the bottom layer and the low-frequency coefficients are reconstructed twice, the reconstructed low-frequency coefficients can be obtained, morphological filtering is conducted on the reconstructed low-frequency coefficients, signal reconstruction is conducted according to the processed reconstructed low-frequency coefficients and the processed high-frequency coefficients on the highest layer, and denoised electrocardiosignals f' are obtained. The electrocardiosignal denoising method has the advantages that the method is simple and easy to achieve, the morphological algorithm and the lifting wavelet transformation algorithm are organically combined, compared with a traditional wavelet algorithm, the high-frequency electrocardio-noise and the low-frequency electrocardio-noise can be removed at the same time, the quality of the denoised signals is improved, computing is easy, the occupied space is small, and the electrocardiosignal denoising method can be achieved on hardware more easily.

The invention belongs to the technical field of video retrieval, relates to related image processing knowledge, and in particular relates to a video text detection method. The video text detection method is characterized by extracting video subtitles from a video to be detected and being used for recognition and video retrieval. The video text detection method comprises the steps of: firstly enhancing the text boundary of an input image by utilizing a gradient amplitude map (GAM); secondly, filtering partial background interference by using morphological filtering in two directions and enhancing the contrast of text and background; thirdly, detecting the saliency map of video text by using a maximally stable extremal region (MSER) detector, and acquiring the optimal segmentation of the text by utilizing Graph Cuts; and finally, connecting the texts to a text row by utilizing the geometrical distribution feature of the texts, and removing non-text regions by using multiframe confirmation and a certain starting education method. The detection method has the effects and benefits that the sensitive technical difficulties of blur text boundary, low contrast and complicated background and the like in text detection are overcome, and the detection results can be directly used for character recognition.

The invention provides a synthetic aperture radar target identification method based on diagonal subclass judgment analysis, which mainly solves the problem that the prior synthetic aperture radar has poor target identification performance. The method comprises the following processes: the self-adapting threshold segmentation, the morphological filtering, the geometric clustering operation and the pretreatment of image enhancement are carried out for an original image; the optimal subclass division to each target after pretreatment is carried out by adopting a two-dimension rapid global K-means clustering algorithm; the diagonal subclass judgment analysis or the diagonal subclass judgment analysis and two-dimension subclass judgment analysis are used for finding out an optimal projection matrix; training and testing images after pretreatment are projected towards the projection matrix to obtain characteristic matrixes of the training and testing images; the Euclidean distance between a testing target and the characteristic matrix of each training target is calculated, and the category attribute of the testing target is determined by adopting a nearest neighbor rule. Simulation experiments show that the invention has the advantages of good effect of inhibiting background clutter, high quality of the target image and low characteristic dimensionality and can be used in a remote sensing system.

The invention relates to a low-power consumption portable electrocardiograph monitor and a control method thereof, which belongs to the field of medical appliances. The portable electrocardiograph monitor comprises an electrocardiosignal collection unit, an electrocardiosignal analysis and storage unit and an upper computer. The control method comprises the following steps of: step 1: carrying out digital filtering to collected signals by using a wavelet entropy optimal threshold denoising method; step 2: carrying out rejection of baseline drift to the signals processed by step 1 by using a morphological filtering method; and step 3: carrying out disease classification to the signals processed by step 2 by using a support vector machine algorithm. The invention has the advantage of high integrity, and has the characteristics of portability, compactness and economy.

The invention relates to a LiDAR point cloud data morphological filtering method based on area prediction. The method includes the following steps: step one, building a filtering initial surface model; step two, removing gross error points of the initial surface model, and obtaining a filtering surface model; step three, performing partitioning on the filtering surface model; and step four, performing filtering processing. The method in the invention overcomes the defect in the prior art that morphological filtering cannot well realize filtering of complex scenes, and the problem that self-adaption is not strong which is caused by filtering processing using constant terrain parameters set manually, the partitioning principle and the advantage that using predicted terrain parameters of partitioned areas can adjust an altitude difference threshold value according to the up-and-down conditions of terrains are utilized, so that filtering on point cloud data can be performed in a self-adaption manner, and a relatively good filtering result can be obtained at last.

The invention discloses a segmentation method of a watershed texture image on the basis of morphological Haar wavelet gradient extraction, which relates to the technical filed of image processing and aims at solving the excessive segmentation problem of watershed texture image segmentation. The method not only can reduce excessive segmentation, but also is much better than other post processing methods solving the problem of excessive segmentation in arithmetic speed. The steps for realizing the method are as follows: (1) a texture gradient image TG is extracted by carrying out morphological Haar wavelet conversion to the original texture image; (2) after circumrotating by 90 degrees, the original image is used for extracting a texture gradient image RTG with a morphological Haar wavelet; (3) the texture gradient image RTG and the texture gradient image TG are subjected to circumrotation weighting to obtain a texture gradient image G; (4) morphological filtering is carried out to the texture gradient image G to obtain a texture gradient image OCG after being smoothed; and (5) marker-watershed segmentation is carried out to the texture gradient image OCG to obtain the final texture segmentation result. Compared with the image segmentation standards, the image segmentation result of the method is validated to basically meet standards.

The invention discloses a marine oil spill information identification and extraction method based on an SAR image. Noise and distortion of a spaceborne SAR image are eliminated through preprocessing. The universal processing is performed on the image for covering non-sea-surface areas. Then the image is divided by means of a two-dimensional maximum between-class variance method for obtaining an interested area. Based on the divided image, the mis-divided image is corrected by means of morphological filtering. Furthermore a selected characteristic parameter is extracted from an image dividing area. Finally according to priori knowledge, an image dividing result is classified by means of a similarly processed sample image set trained BP neural network, thereby differentiating sea surface oil spill and suspected oil spill and accurately extracting marine oil spill information.

The invention discloses an image processing method for quickly and automatically detecting soldered dot unsatisfied products of electric connectors. The image processing method comprises the following steps: 1) converting an original image into a gray image, determining a section center and the outer radius of an inner circle, and accordingly, extracting a square image within a section region; 2) binarizing the square image of the section region, separating out the section and the background and performing morphological filtering; 3) obtaining a contour line width in each direction of the binary value of the section, and obtaining the average width to determine the radius of a center circle; 4) converting an original connector enclosing rectangle RGB image into HSV, segmenting out an H-channel image and carrying out binarization by use of a color threshold of soldered dots; 5) performing morphological filtering on the H-channel binary image to obtain a soldered dot image; and 6) detecting the number of the soldered dots in the center circle of the section by traversing, and comparing the number of the soldered dots with a standard number of the soldered dots to judge whether the soldered dots are satisfied or not. The image processing method is capable of eliminating the subjective error caused by manual examination and improving the consistency of detection, and also capable of improving the efficiency and the accuracy and guaranteeing the safety of the personnel, and has the advantages of non-contact and the like.

The invention relates to a method for reconstructing a large-scale complex flyover 3D model by using airborne LiDAR data. The method includes the following steps: using inverse iteration mathematic morphological filtering and area information to extract flyover point cloud from airborne LiDAR data; in accordance with bridge floor connectivity, segmenting the flyover cloud point; adopting a central line vertical line scanning method to segment and extract a communicated bridge surface which has no bifurcation or intersection structures and has a width consistent width, acquiring a "constitutional unit"; an end place of the constitutional unit central line serving as a circular buffer area, detecting a shielding structure of the flyover through determination of point cloud height information within the buffer area; through steps of constitutional unit matching, 2D central line fitting and 3D curved surface fitting, acquiring a complete bridge surface 3D central line; and reconstructing a flyover 3D model in combination with bridge surface width information. Practice has proved that the method of the invention can effectively reconstruct the large-scale complex flyover 3D model, addresses shielding problem of the flyover point cloud, and the reconstructed 3D model has a higher correctness and completeness.

The invention relates to a vehicle flow detection algorithm based on video image processing. The concrete algorithm comprises the following steps: a. continuous frame sequence images are inputted; b. image graying and smoothing are performed; c. a gray scale histogram and a gray scale mean value of each pixel point of an image frame sequence are counted; d. a background model and a threshold plane are established; e. frame inputting and background difference are performed, operation target regions are extracted, an OTSU is performed after the threshold plane is established, and a differential image threshold T is calculated; f. shadow detection based on color and gradient is performed, and binary and morphological filtering processing is performed after the threshold T is calculated; g. information of a moving vehicle of which shadow is eliminated is acquired; and h. vehicle flow detection based on coils is performed. The vehicle flow detection algorithm based on video image processing is simple in principle, convenient to calculate and more accurate in extraction of vehicle flow data so that a function of counting of vehicle flow predetermined by a system can be greatly realized via large amount of experiments and simulations.

The invention provides a method and device for diagnosing the failure state of a circuit breaker. The method comprises the following steps of obtaining a current signal characteristic value f0 (t10, i10 and S0) of an opening and closing coil in a circuit breaker predelivery test; obtaining a current signal, in the power-on process, of the opening and closing coil of a circuit breaker to be tested; performing morphological filtering processing on the current signal to obtain the de-noised current signal of the opening and closing coil of the circuit breaker to be tested; calculating a corresponding characteristic value f1(t11, i11 and S1) of the de-noised current signal; judging whether the value meets the formula shown in the specification; if the value meets the formula shown in the specification, further judging whether the value meets a formula shown in the specification; if the value does not meet the formula shown in the specification, judging that an iron core of the circuit breaker is normal and a power supply of the opening and closing coil fails; if the value does not meet the formula shown in the specification, further judging whether the value meets the formula that (S1-S0)/S0 is larger than theta; if the value meets the formula that (S1-S0)/S0 is larger than theta, judging that starting lagging failures happen to the iron core of the circuit breaker. According to the technical scheme, the diagnosing efficiency can be improved, and the diagnosing precision can be obviously improved. The requirement that the circuit breaker is diagnosed online on site can be well met.

The invention discloses an automatic measuring and morphological classification method for particles precipitated from steel, comprising the steps as follows: firstly, the electron micrographs of the target particles precipitated from steel are subjected to image binary segmentation so as to obtain the binary images of the particles; the binary images of the target particles are denoised by a morphological filtering method, a seed filling method is adopted to fill holes, and the particles to be separated are determined by the domain value determined by experience criterion and the separation of agglomerate particles is carried out; the particles after separation are subjected to region labeling; finally, the neural network morphological classification models of the target particles precipitated from steel are established; and results are displayed and output in the form of graph files. The method can obtain ideal measuring and classification effect without omission inspection and re-inspection; the measurement accuracy of particle size can reach plus or minus 2 microns, the particle size distribution anastomotic rate can be more than 91.7 percent, and the anastomotic rate of morphological classification can be more than 90.5 percent; the particle measuring and classification of one view field cost only a few minutes; and the method has excellent universality and can be used in all the particle measuring and classification works with complex backgrounds and morphologies in the material field and biological field.

In a water resource remote sensing survey, water body information is required to be extracted by using a middle-high-resolution image along with fine management of the water resource. However, since a high-resolution satellite image has fewer bands, the construction of an automatic water body extraction model is not facilitated, and further relatively fewer methods for effectively extracting water body information are available, and the error extraction rate and the extraction omission rate are higher. The invention constructs a new SPOT5 image-based automatic water body extraction method. According to the method, the polarization of the grey value of a water body is realized by constructing a calculation model capable of homogenously enhancing a water body index and multiple indexes based on remote sensing image background value research, and high-precision automatic extraction of the water body is realized on the basis by using improved automatic threshold selection algorithm, mathematical morphological filtering algorithm and thinning algorithm and the like. Experimental comparison shows that the influence of topographic shadowing, an inhabited place and the like can be eliminated to the greatest extent, a larger water body can be effectively extracted, and higher extraction precision on a finer water body is realized.