168 results about "Morphological filter" patented technology

The present invention is a distribution network cable-line mixed fault circuit selection method by using HHT detection technology. The invention uses HHT singularity detection technology to calibrate fault occurring time accurately, and implements circuit selection according to a principle that, when a fault occurs transient fault zero-sequence current component of the fault circuit and the regular circuit is opposite in direction. The specific method is using HHT to proceed EMD decomposition to the sampling signal of before and after 1/4 period each circuit fault zero-sequence current that is morphological filter pretreated, obtaining highest frequency IMF component of each circuit fault zero-sequence current and proceeding a first-order backward difference treatment to the IMF component, comparing the polar mutation of the fault zero-sequence current IMF component at fault moment, and then forming a circuit selection criterion. Analysis and simulation of the principle shows that, using fault information of 1/4 period after the fault to select circuit can provide an accurate and reliable circuit selection result and avoid effects of paraphrase and CT saturation on circuit selection accrracy.

The invention discloses a hydroturbine fault diagnosis method and a hydroturbine fault diagnosis device based on acoustic emission technology. The method applies the acoustic emission technology to the fault diagnosis of a hydroturbine set, obtains acoustic emission signals on a hydroturbine draft tube and guide vanes of a watermill chamber through an acoustic emission sensor, eliminates noise by adopting morphological filtering technology, adopts the pulse repetition rate to analyze the intensity of the periodicity of the acoustic emission signals, combines spectrum analysis and waveform nearness analysis to identify common rub impact, cracks as well as cavitation and cavitation erosion states, and applies wavelet packet technology for the decomposition and reconstruction of the signals to calculate the acoustic intensity of acoustic emission and perform the trend analysis of parameters of the acoustic intensity along with the working condition such as active power, water head and guide vane opening, further to comprehensively know the operation state and performances of a hydroturbine generator set and guide the operation of a hydroturbine.

The invention discloses an electrocardiosignal denoising method based on morphology and EMD (empirical mode decomposition) wavelet threshold value. The electrocardiosignal denoising method comprises the following steps: firstly, the mathematical morphology filter method is adopted to perform primary filtering on an electrocardiosignal to filter baseline drift in the electrocardiosignal, then, the EMD method is adopted to decompose the obtained electrocardiosignal and obtain a noise dominant component and a useful signal dominant component through classification, the threshold value denoising method is adopted to perform secondary filtering on the electrocardiosignal, and finally, the EMD method is adopted to reconstruct the electrocardiosignal to obtain the noise-filtered electrocardiosignal. The invention has the following remarkable effects: the method is simple and easy to implement; the morphological filter method, the EMD method and the threshold de-noising method are combined in an organic manner; compared with the traditional denoising method, the electrocardiosignal denoising method can comprehensively and effectively remove electrocardiosignal noise, keeps the useful information in the electrocardiosignal, and provides more valuable reference for change of cardiac functions and diagnosis of cardiac diseases.

The invention discloses an intelligent power grid high voltage insulation damage monitoring system and method based on ultraviolet imagery. Image acquisition is conducted on a high voltage insulation device through an acquisition module via an ultraviolet imager, and an RGB color image of corona discharge and arc discharge on the surface of the device is obtained; the RGB color image is converted into a grayscale image through a pre-processing module; a threshold segmentation method is adopted by a threshold segmentation sub-module, a threshold is set to convert the grayscale image into a binary image, and a discharging area in the image is divided; mathematical morphology filtering processing is conducted on the binary image through a morphological filter sub-module, and scatters are removed; an edge detecting sub-module conducts edge detection on the filtered image, edge pixel point position information is stored, colors of edge pixel points in the original image are changed, and a dividing effect image is obtained. The intelligent power grid high voltage insulation damage monitoring method based on ultraviolet imagery has the advantages of being efficient, simple, visual, imagery, safe and convenient, and not influencing normal operation of the device. With combination of the image division technology, the discharging area is divided, and accurate detection of high voltage insulation damage is achieved.

The invention relates to a failure line selection self-adapting method for a mixed line of a distribution network line-cable by utilizing the relative energy of a characteristic frequency band. The method is as follows: when the instant value of the zero sequence value of a bus is out of limit, a failure line section device instantly starts and records waves; a morphological filter is used for carrying out filter pre-treatment on the transient zero-sequence currents of temporary state of each line; a wavelet packet is applied for decomposing, the characteristic frequency bands of the transient zero-sequence currents of temporary state of each line are confirmed, the component of a stable state and the information of other frequency bands are eliminated and the line selection margin is improved according to the view of energy maximizing; the sizes of the relative energies in the front 1/4 period and the last 1/4 period in the back period of the failure under the characteristic frequency bands are compared according to the principle that the capacitance and current of the failure line under the temporary state attenuates fast in the characteristic frequency bands, thus forming the criterion for failure line selection. Principle analysis and imitation show that the relative energy of the first 1/4 period and the last 1/4 period in the back period of the failure is utilized for realizing the line selection, thus amplifying the temporary characteristics of the failure, avoiding the effect of an intermittent corner on the accuracy of the line section caused by the saturation of CT. The method is accurate and reliable for line selection.

A method for the detection of target structures shown in digital medical images, the method of comprising: (1) generating a three dimensional (3D) volumetric data set of a patient region within which the target structure resides from a plurality of segmented medical image slices; (2) grouping contiguous structures that are depicted in the 3D volumetric data set to create corresponding grouped structure data sets; (3) assigning each grouped structure data set to one of a plurality of detection algorithms, each detection algorithm being configured to detect a different type of target structure; and (4) processing each grouped structure data set according to its assigned detection algorithm to thereby detect whether any target structures are present in the medical images. Preferably, the target structures are pulmonary nodules, and a specialized detection algorithm is applied to image data classified as a candidate for depicting perivascular nodules. To segment perivascular nodule candidates from surrounding vessels, the image data is preferably correlated with a plurality of 3D morphological filters.

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

The invention provides a discharge ultraviolet imaging quantization parameter extraction method of high-voltage electric equipment. Firstly, by means of digital image grey level transformation, a threshold division algorithm, a morphological filter algorithm and a small region image elimination algorithm, a discharge region image in an ultraviolet image of the high-voltage electric equipment is segmented, then a boundary point of each region is extracted by means of a multi-region boundary tracking algorithm, and on the basis of the preceding operation, one or more geometrical parameters of a light spot are calculated and used for carrying out quantitative analysis on discharge strength of the high-voltage electric equipment. According to the method, adverse influences of gain of an ultraviolet imager and observation distances on analysis results can be eliminated, quantitative analysis can be carried out on discharge strength of a plurality of discharge points, and accuracy of analysis results is greatly improved. Besides, due to the fact that the parameter comprises position information of the discharge points, and conditions are created for accurate positioning of discharge defects.

Digital images that are produced by an image forming device may be processed using an edge enhancement technique to reduce the effects of halftone color depth reductions. For each element in the original image, certain detail elements are classified by examining the magnitude of pixel intensity gradients between elements of interest in a first window applied at each element and other elements in the first window. If a first predetermined condition is satisfied, those elements locations are stored. After halftoning, a morphological filter may be applied to the same element locations in the halftone image to enhance the halftone image.

The invention discloses a double-layer morphological filter based electrocardiosignal preprocessing method. The method is characterized by including the method: firstly, utilizing a first-layer morphological filter constructed by triangular structure elements to process an original electrocardiosignal f0 so as to obtain a primary filter signal f1; secondly, processing the primary filter signal f1 by the aid of a second-layer morphological filter constructed by linear structural elements to obtain a secondary filter signal f2, and thirdly, comparing the primary filter signal f1 obtained in the first step to the secondary filter signal f2 obtained in the second step to acquire a difference so as to acquire a preprocessed electrocardiosignal f3. The method has the remarkable advantages that electrocardiosignals are processed by the aid of the double-layer morphological filters, on the basis of retaining useful characteristics of the electrocardiosignals, baseline drift in the electrocardiosignals is acquired accurately and is removed effectively by making difference, and accuracy rate of R wave detection during follow-up processing is improved beneficially.

The invention discloses a power quality disturbance identification method based on online weighted SVM training. The method comprises: according to different characteristics of common power quality disturbance signals, a mathematical model of the disturbance signals is established; on the basis of the mathematical morphology, a generalized morphological filter of an adaptive LMS algorithm is constructed to carry out denoising processing on a power quality disturbance signal; wavelet transform is carried out to extract energy distribution of different frequency bands of the power quality disturbance signal; a fractal dimension of the power quality disturbance signal is extracted based on a fractal theory and the energy distribution and the fractal dimension form a quantification characteristic index of the disturbance signal; and according to time of power quality disturbance occurrence, disturbance data are assigned with different weights, online training and testing are realized, and identification classification is carried out on power quality disturbance.

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.

The invention provides an electrocardiosignal preprocessing method based on morphological multiresolution decomposition, which comprises the following steps that (1) a linear structural element is applied to drifting of a correction baseline of a morphological filter; (2) a lifting method is applied to constructing of morphological multiresolution to decompose an electrocardiosignal to a second layer and a third layer; and (3) a component with less QRS (Quantum Resonance Spectrometer) complex wave distortion in signal components of the second layer and the third layer is selected as a preprocessed electrocardiosignal. According to the electrocardiosignal preprocessing method, the linear structural element is applied to drifting of the correction baseline, so that waveform distortion is reduced to a greater degree. A morphological multiresolution decomposition method is applied to filtering of high-frequency noise such as electromyography disturbance, the performance is superior to that of the preprocessing method of wavelet transform and morphological filtering, and the method is simple and easy to realize. The lifting method is adopted to construct multilayer morphological decomposition, because prediction and update operators which are suitable for the electrocardiosignal are selected, the denoising performance is further optimized, and the distortion of characteristic waves of the electrocardiosignal is reduced.

The present invention proposes a depth image repair algorithm, which combines morphological filtering and bilateral filtering algorithm to process the depth image, and realizes the filling of holes in the depth image through several basic algorithms in morphology. The image is then smoothed using bilateral filtering. Different types of noise signals contained in the target object are eliminated by using different structural elements. The steps of the algorithm are as follows: Step 1: Establish the dilation, erosion, opening operation, and closing operation formulas of the morphological algorithm; Step 2: Establish a morphological filter; Step 3: Select structural elements; Step 4: Establish bilateral filtering criteria.

The invention belongs to the field of biomedical science, and aims to provide a high metastatic potential hepatoma cell line capable of steady autophagy indication, and an establishment method and an application method thereof. Lentivirus expression vectors containing EGFP (enhanced green fluorescent protein)-LC3 (light chain 3) autophagy reporter genes are constructed and used to infect hepatoma cells with high metastatic potential, so that host cells can express the EGFP-LC3 steadily and efficiently and indicate autophagy changes of the hepatoma cells steadily. To apply the cell line, metastasis research models can be established in vitro and in vivo for the observation on autophagy changes during metastasis, and the cell autophagy changes can be analyzed rapidly and quantitatively by adopting an image analysis method based on Top-hat operators and morphological filter. The high metastatic potential hepatoma cell line has the characteristics that autophagy indication is conducted steadily, reliably and sensitively in a real-time manner, the metastasis occurs definitely, the autophagy is quantified rapidly and accurately, the operation is simple and convenient, and the like, and can greatly facilitate correlation study on autophagy and metastasis.

The invention discloses a moving object detection method based on entropy. Firstly, data of scene graphs are read in, graying processing is carried out on the data, then monitor areas of a scene are marked, difference operation is carried out on every two adjacent frames of gray level images of each monitor area, an optimal threshold of division of the images is determined based on the entropy of the difference operation result, then morphological filter is carried out on the divided images, connected areas are detected, and the detected connected areas are marked; finally, the area of each connected area is calculated, the connected area with the largest area is marked as a moving object, and the moving object is output. Multiple image processing basic methods are applied comprehensively, the technologies in the current computer vision and mode identification fields are introduced into the intelligent monitor field of transmission lines, moving detection of large machines in the transmission line scene is achieved through the related vision information in the transmission line scene, and therefore influence on the transmission line by external force damage is prevented.

The invention discloses a visible foreign matter and bubble classification recognition detection method for medical large volume injection (250ml), and the method comprises the following steps: 1), continuously obtaining a plurality of images of large volume injection in detection; 2), carrying out the image pre-processing: filtering the images based on Top-Hat morphological filter; 3), carrying out image segmentation: carrying out the segmenting of the filtered images through employing an interframe differential method based on the maximum information entropy; 4), carrying out extraction of defected edges: extracting the edges of the visible foreign matters and bubbles through employing an SUSAN algorithm; 5), carrying out an image feature extraction algorithm: extracting the shape, gray and movement feature parameters for describing foreign matters and bubbles through the research and analysis of the features of defects; 6), carrying out the defect classification and recognition: achieving the recognition and classification of foreign matters and bubbles through employing an IDS-ELM algorithm. The invention achieves the recognition and classification of foreign matters and bubbles, can precisely classify and recognize various types of defects, and enables the products with different types of defects to be removed and placed in different defective product regions.

The invention provides a front car identification method based on monocular vision. The method includes the steps that (1), an original image is collected from a vehicle-mounted camera, the edge of the image is extracted according to a Canny edge extraction method, influence of noise points is eliminated through morphological filter, projection is carried out in the horizontal direction, and an area of interest of a front car is obtained according to projection characteristics; (2), a shadow area at the car bottom is extracted and judged according to the geometrical shape of the shadow at the car bottom, edge characteristics are overlaid, and a car area is judged; (3), graying, normalization and binary tree complex wavelet transformation are carried out on small color images of candidate car areas of different shapes, and characteristic vectors are obtained; (4), the number of dimensions of the characteristic vectors is decreased through a two-dimension independent component analysis algorithm, the characteristic vectors are fed into a support vector machine based on a radial basis function kernel to be classified, and it is judged that whether the candidate car areas are the car area. Cars on the road ahead are detected accurately, and real-time and reliable road condition information can be supplied for unmanned cars.

The invention relates to a method for diagnosing a commutation failure of an extra-high-voltage (EHV) DC power transmission system and belongs to the technical field of HVDC power transmission system fault diagnosis. The method comprises steps of: performing hierarchical reconstruction on an extracted DC current signal sequence; denoising the DC current signal sequence, filtering the DC current signal sequence with a morphological filter, and extracting the high-frequency components of the DC current signal sequence; performing Hilbert-Huang transformation on the high-frequency components, solving the instantaneous frequency maximum value f and the instantaneous frequency amplitude mean value A of an intrinsic mode function, setting a frequency threshold value f0 and a amplitude average threshold value A0, determining that the system is faulted if the f is more than or equal to f0 or that the system operates normally if not; and if the system is faulted, determining whether the A is more than or equal to A0, and determining that a line is subjected to a short-circuit fault if yes or that the system is subjected to commutation failure if not. The method may accurately determine the running state, the commutation failure, and the line short-circuit fault of the EHV DC power transmission system.

The invention provides an image information acquiring method applied to elevator safety monitoring, which can be convenient for later video analysis to quickly and effectively realize automatic warning of an abnormal elevator operation state and/or an abnormal activity in an elevator. The method comprises the following steps: images shot by cameras with fixed positions serve as background models; differential images are obtained through differences between present video frames and the background models by a background subtracting method, the binarization processing is performed for the differential images according to binarization formulas, the noise is removed through a morphological filter algorithm, and finally, the area communication analysis is performed for denoised results; edge profiles of the binarized images are extracted by Canny calculators; and human body standing images are modeled in an edge profile form, and are stored to compare with foreground profiles extracted in real time through a template matching algorithm so as to build basic machine vision.

The invention relates to an intelligent positioning method facing a microfluidic chip, the use of chip planar images in CCD camera acquisition microfluidic chip analysis system, using morphological filters and OPTA (one-pass thinning algorithm) refinement algorithm to denoise and refine the chip ichnography, being a skeleton-map of the chip ichnography; based on chip skeleton map, extracts relevant node on the network of micro-channels of microfluidic chip; According to the joint position of all micro-channels and relations between the two connection, generates adjacent table; According to the adjacent table on the microfluidic chip for intelligent tracking positioning, makes the adjacent table feedback amendments through the relevant feedback algorithm in accordance with the positioning results. It is based on the relevant image processing technology designed with a fully automated positioning, high precision, high speed and can track position, and other characteristics, it can improve the automaticed analysis for all microfluidic chip analysis system.

The invention discloses a recognition and location method for an apple on the tree based on a TOF camera. According to the technical scheme, the three-dimensional imaging technology of the TOF camera with the resolution of 1280 (h)*1024 (v) and the model of OI-VS-1000 is used to acquire the reflected light intensity image and depth image of the apple, and the image is pre-processed; a selective contrast enhancement algorithm is used to improve the depth image; a morphological filtering algorithm is used to filter out noise in the depth map; a target fruit region and a background region are divided by a regional splitting and merging algorithm; a Canny operator is used to extract the boundary; circle detection is carried out on a boundary image by shape location Hough transform in the image to acquire a fruit circle and the center position; and the location technology of the three-dimensional database of the TOF camera is combined to extract the three-dimensional coordinates of each fruit center to acquire the actual radius value of the fruit, so as to recognize and locate the apple.

The present invention discloses a target detection process that acquires imagery from a target; compares the acquired imagery with image metric data; applies criteria to eliminate false detections and reduce clutter; applies morphological operators on the acquired imagery; probability rank orders the target imagery; divides the imagery into a series of detected target windows; and displays the detected target windows. The imagery undergoes light target detection, dark target detection or both. A morphological operator isolates targets rom their background. Two concatenated morphological filter patterns are used to screen imagery data. Spatial discontinuities at the pixel level can be detected. The detected target window images are presented to a user in a mosaic format.