212 results about "Morphology Method" patented technology

The invention discloses a parallax optimization algorithm-based binocular stereo vision automatic measurement method. The method comprises the steps of 1, obtaining a corrected binocular view; 2, matching by using a stereo matching algorithm and taking a left view as a base map to obtain a preliminary disparity map; 3, for the corrected left view, enabling a target object area to be a colorized master map and other non-target areas to be wholly black; 4, acquiring a complete disparity map of the target object area according to the target object area; 5, for the complete disparity map, obtaining a three-dimensional point cloud according to a projection model; 6, performing coordinate reprojection on the three-dimensional point cloud to compound a coordinate related pixel map; 7, using a morphology method to automatically measure the length and width of a target object. By adopting the method, a binocular measuring operation process is simplified, the influence of specular reflection, foreshortening, perspective distortion, low textures and repeated textures on a smooth surface is reduced, automatic and intelligent measuring is realized, the application range of binocular measuring is widened, and technical support is provided for subsequent robot binocular vision.

The invention discloses a high-speed weak target flight path detection method of an image field, relating to the field of moving object tracking, and mainly solving the problem of extracting a moving object flight path and a target kinematic parameter from data with a low signal-to-noise ratio. The detection process is as follows: firstly, obtaining a time-Doppler image by mixing and accumulating target multiframe data; carrying out edge extracting on the basis of phase equalization model; carrying out skeletonization and binary image de-noising treatment on the edge detection result with a morphology method to realize edge center positioning as well as noise inhibition; and finally, using a least square method to detect the target flight path and estimate a target parameter. The invention can accurately estimate the advantages of a curve flight path parameter, and can be used for the data processing field for monitoring systems, such as radars and the like to realize high-speed weak target detection and tracking.

The invention discloses a high-speed rail catenary geometric parameter detection non-contact compensation and Kalman filtering correction method. The method mainly comprises the steps that firstly, a video camera is triggered to collect video images at equal intervals through an encoder on a wheel set; the regions where target light spots appear in the images are predicted according to prediction strategies; the positions of the target light spots in the images are located according to the centroid method and the image morphology method; the angle of roll vibration is detected through an angle sensor, and compensation is performed on the vibration through coordinate transformation; contact wire height and pull-off values are figured out according to mapping transformation of the light spots from the position of an image coordinate system to the position of a world coordinate system; finally, detecting values are corrected through the Kalman filter equations. According to the method, the defects that the system is low in detecting accuracy, poor in real-time processing performance and the like are effectively overcome, the processing efficiency of the system is improved, and the requirements of high-speed rail catenary on-line detection for real-time performance and accuracy are well satisfied.

The invention relates to a single-pointer meter reading method based on machine vision. The method comprises the steps of collecting a template map and modeling and performing feature detection on a template map dial area, a step of performing feature detection on a meter image to be the recognized, completing the dial area positioning in the meter image to be the recognized by using the feature matching technology and calibrating a dial area to be recognized as a template map state, a step of processing the calibrated dial area map, a step of obtaining a pointer area by using a pointer-basedfeature search method, a step of carrying out shadow removal on the pointer area, a step of obtaining a pointer single pixel boundary by using a morphology method, completing adaptive pointer boundarylinear detection, and obtaining a pointer straight line by an angular bisector, a step of completing pointer direction judgment by using a projection method, and completing meter reading by using anangular method. According to the method, the information needed by pointer-type meter recognition can be reduced, the method can be adapted to the conditions of pointer breakage and pointer and text stickiness in an image, the method is not interfered by a pointer shadow, and the method has high accuracy and stability.

The invention discloses a method for detecting diseases of crop leaves. The method comprises the following steps: acquiring a leaf image of a crop to be detected, uploading the leaf image to an on-line detection platform with a disease image automatic identification function and a professional diagnosis system function, performing scab image partitioning and identification on the leaf of the crop to be detected, outputting a detection result, and giving a control suggestion, wherein the scab image partitioning is as followings: converting an original image from a red, green and blue (RGB) model space to a horizontal situation index (HSI) space, respectively extracting an H component image and an I component image in the HIS space, and performing dynamic threshold value partitioning on the H component image by using a maximum between-cluster variance method to preliminarily obtain a scab region image; superimposing the I component image on the partitioning result of the H component image to eliminate misjudgment caused by a background region on the scab partitioning, thus obtaining a binary image only comprising the scab region; and performing subsequent treatment on the partitioning result by using a morphological method, and finally obtaining a complete scab image of the leaf of the crop to be detected.

The invention provides a vehicle intelligent rear viewing method in the vehicle intelligent technical field, comprising the following: step one is to acquire a scenery image from the posterior lateral of the vehicle by two needle cameras arranged on a left rear view mirror and a right rear view mirror of the vehicle respectively and a needle camera arranged on a rear deflector door of a vehicle body; step two is to perform a pretreatment to the acquired scenery image; step three is to perform an edge detection for the pretreated image based on a method of mathematical morphology of multi-structural elements; step four is to form a sampling round region and perform a polar-log grid sampling to an edge image occurring in the sampling round region; step five is to perform an image data conversion to the edge image received from the polar-log grid sampling; step six is to perform a recognition of a motion state to the converted image data; and step seven is to perform a decision operation and output an execution command according to the recognition result of the motion state. The vehicle intelligent rear viewing method improves the safety technical level of the vehicle and reduces the occurrence probability of road traffic accidents.

A high-voltage line foreign matter invasion target detection method includes the steps that an adjacent-frame image is extracted from a video monitoring terminal or a video, the extracted adjacent-frame image is preprocessed, the movement characteristic of the adjacent-frame image is extracted, foreign matter or an invasion target is detected, the foreign matter invasion target is marked through a morphological method, the foreign matter or the invasion target is tracked, whether a power transmission line is affected or not is judged, and if yes, an alarm is given. Through the method, the foreign matter invasion target is fast and accurately detected, a high-voltage line is monitored, and an actual application requirement can be met.

The invention relates to an automatic recognition method for a woven fabric structure based on gradient direction characteristics and Fuzzy C-Means Algorithm (FCM). The automatic recognition method comprises the following steps of: firstly preprocessing a woven fabric brightness image by adopting an image morphology method, then correcting deflection existing in interweaving of warp yarns and weft yarns by utilizing gray projection, simultaneously segmenting a fabric image into a plurality of weave points, extracting gradient direction histogram characteristics on each weave point, classifying the weave points by using an improved FCM, and finally carrying out statistics on classifying results according to the periodicity of the fabric structure and correcting error checking points, thus outputting a correct weave chart. The automatic recognition method can overcome the influence brought by uneven illumination, and difference in thickness and color of yarns by utilizing gradient direction information of the weave points and combining with the FCM method, can achieve recognition of basic weaves (a plain weave, a twill weave and a satin weave) of the woven fabric, and also has a good recognition effect on derivative weaves (a plain derivative weave, a twill derivative weave and a satin derivative weave) in small decorative pattern derivative weaves.

The invention discloses a moving target detection method based on a deep optical flow and a morphological method, which includes the following steps: (1) collecting video data, marking sample videos,randomly dividing the sample videos into a training set and a testing set, carrying out mean calculation on the processed training set and the processed testing set to form a training set mean file and a testing set mean file, and completing the preprocessing of the training set and the testing set; (2) constructing a fully convolutional neural network architecture composed of a coding part and adecoding part, and carrying out training by using the training set and the testing set through an adaptive learning rate adjustment algorithm to get trained model parameters; (3) inputting image dataneeding detection to a trained fully convolutional neural network to get a corresponding deep optical flow graph; (4) processing the deep optical flow graph through an Otsu threshold adaptive segmentation method; and (5) morphologically processing the data after threshold segmentation, removing outliers and slots, and finally obtaining a detected moving target area.

The invention relates to a method for measuring the size of a target object of sparse point cloud. The method comprises the following technical steps of: converting a point cloud coordinate system into an image pixel coordinate system; Projecting the point cloud data on an XY plane to obtain a two-dimensional point cloud; Carrying out edge detection on the matched image by using a Canny operator;De-noising and refining the edge contour line by adopting an image morphology method; Carrying out traversal search on the point cloud image, and carrying out pixel point coordinate matching on the point cloud image and the binary contour image; And extracting the point cloud coordinates which are matched and matched to form a new point cloud, and carrying out linear fitting and size measurement on the new point cloud. According to the invention, through overlapping the point cloud image and the contour image after stereo matching, the point cloud on the contour line is extracted, the linear fitting algorithm is used to carry out linear fitting on the extracted new point cloud coordinate, and the size measurement is further carried out.

The invention provides a method for leukocyte automatic identification based on a relevant vector machine. According to the method, hue information of blood microscopic image characteristics is utilized and coarse segmentation of a hue image is accomplished according to a gray level image segmentation method based on the relevant vector machine; all leukocytes are detected with the assistance of an FCNN; a threshold value is determined through the clustering methodology and fine segmentation is conducted on a partial image containing one single leukocyte with the combination of the threshold value segmentation method and a binary morphology method; on the basis of the partial images obtained in the last step, the representative leukocyte characteristics are extracted, wherein the leukocyte characteristics comprise 47 characteristics in three types of forms, colors and textures; the leukocytes are identified and classified through the supported vector machine. The method has the advantages that the identification effect is ideal, stability is high and robustness is good. Valuable information is provided for diagnosis conducted by a doctor and quantitative analytical investigation is rapidly and accurately conducted on the cells.

A quick and effective segment method for a human iris image in an image process technology includes: 1, determining a binary threshold value based on a gray projection method then to carry out binary quantization to the original images, 2, eliminating noises of the binary images with the morphologic method, carrying out edge test to the image after eliminating noises by Sobel operator, 4, determining the parameter of the pupil to segment the pupil with the theory of string crossing and determining a circle by three points, 5, determining sphere around the iris based on the parameter of the pupil, 6, reducing area of the iris pro rata and carrying out the edge test in the vertical direction with an improved Canny operator, 7, further eliminating noise information of the pupil region and part of the eyelashes and eyelids, 8, carrying out location of a circle to the reduced edge image by the Hough transformation, 9, calculating the parameter of the original iris outer circle from the parameter of the reduced circle.

The invention discloses a method for predicting traffic flow extracted by an improved C-V model-based remote sensing image road network, which comprises the following steps of: (1) preprocessing an original remote sensing image; (2) selecting a seed point and segmenting a first road network sub-image; (3) extracting a road network area by an improved C-V model-based level set method; (4) extracting a road network central line by a morphological thinning method; (5) segmenting a next road network sub-image by using a sub-image position decision rule and automatically acquiring a road network initial curve in the road network sub-images by a threshold segmentation and morphological method; (6) vectorizing a road network; and (7) predicting the traffic flow. By integrating technology such as remote sensing, geographic information system (GIS), image identification, traffic planning and the like, an urban road network can be more accurately, efficiently and cheaply updated in real time, the traffic flow prediction cost is lower, the traffic flow prediction accuracy is higher and the traffic flow prediction period is shorter, so that decision makers can be effectively assisted in making traffic planning decisions.

The invention discloses a sequential level set-based method for automatically segmenting images, which comprises the steps: (1) inputting an image I to be segmented and automatically initializing a level set function in an image domain by using a morphological method; (2) evolving the level set function by using an iterative approach in a corresponding subdomain to obtain the level set function and a detected image border, and using the inside domain of the edge as a next subdomain; (3) converting an image edge detected in the subdomain into a binary image and storing the binary image; (4) judging whether the area of the next subdomain is zero, and executing a step (5) if the area of the next subdomain is zero, or returning to the step (1) if the area of the next subdomain is not zero; and (5) performing a 'combination' operation of the stored binary image, combining the image edges of all subdomains, and completing the image segmentation. The method has the advantages of detecting more image edges and automatically completing image segmentation, and can be used for segmenting natural images and medical images.

The invention relates to a cascade quick image defect segmentation method. The method includes: preprocessing an image to acquire global threshold information, and performing rough image segmentation based on a global adaptive threshold; acquiring fine image segmentation through priori knowledge and a level set method of local image information; eliminating evident error candidate defects and error alarms by means of size and shape analysis, and performing target separation on the basis of a parameterized mathematical morphology method. the problems such that non-uniformity of local and global thresholds leads to inaccurate image segmentation are solved by means of a stratified strategy; features of defects, such as shape and size, are introduced to a level set energy function; the priori knowledge of the defect shape is fully utilized, the problem of initial dependence is solved, and the method is well applicable to image defect segmentation having shape features; by introducing the cascade image processing architecture and discarding most non-defective images from the most front, unbalance of data distribution is greatly relieved, and defect segmentation speed and precision is greatly improved.

The invention discloses a greenhouse vegetable disease surveillance video key frame extracting method and extracting system. According to the extracting method and the extracting system, the visual significance is combined with an online clustering algorithm. Firstly, an X2 histogram method is utilized to measure interframe difference and remove the effects of video frame images with the similar characteristics on the algorithm calculation amount; then the video frame images are transferred to an HSV color space, a visual significance diagram is calculated through characteristics of greenhouse vegetable surveillance video, an H channel and an S channel to extract the significance areas in the video frame images, and scab information, possible to be lost, in the significance areas is repaired through a morphological method; finally, key frames can be extracted through the online clustering algorithm and a pixel frame average algorithm. The extracting method can effectively obtain disease information in greenhouse vegetable surveillance video to establish solid foundation for accurate identification on greenhouse vegetable diseases. If combined with image processing techniques and mode identifying techniques, the extracting method and the extracting system can make great contributions to greenhouse vegetable disease identification.

The invention, which relates to the computer vision field, provides a variable-length license plate character segmentation method based on hybrid tilt correction and a projection method. The method comprises the following steps that: horizontal license plate correction is carried out by using rotation invariance of a singular value of an image matrix and vertical license plate correction is carried out based on a Radon transform and horizontal shearing principle, thereby completing hybrid tilt correction of the license plate effectively and rapidly; various optimization pretreatment is carried out before license plate character segmentation, wherein image gray processing based on a weighted average method, elimination of license plate background non-character interference regions at the left side and the right side of the license plate based on license plate left-right boundary positioning, improved binary processing operation by combination of a global threshold value, a local threshold and an RGB color image, and binary image optimization based on a morphological method are carried out for optimization pretreatment; character image segmentation is carried out by using H-S connected domain analysis and projection methods; and then pseudo characters are eliminated by using a hue average statistical method of HSV space.

The invention discloses an X-ray, ultrasonic and infrared image fusion-based breast mass image detection method, and mainly solves the technical problems of simplex information source and inaccurate information due to a single breast mass image. The method is implemented by the steps of fusing a breast X-ray image, an ultrasonic image and an infrared breast image; preprocessing and denoising a fused breast image; segmenting out a breast region for three types of fused images of the denoised breast image by using a square grid coverage method; performing wavelet image enhancement on the breast image from which the breast region is segmented out, and remarkably distinguishing a mass from a background region; and detecting the mass in the breast region based on a Top-hat morphological method to assist in early diagnosis of doctors. According to the method, multiple breast examination images are fused, so that information richness is improved and more accurate diagnosis information is obtained; and the image is subjected to analytic processing to detect out the mass, so that early discovery and diagnosis treatment of breast cancers are facilitated and an image basis is provided for the early diagnosis of the doctors.

The invention discloses a connected region labeling-based frequency hopping signal dynamic clustering extraction method. The method comprises the following steps that: a received signal model is constructed; a morphological method is adopted to perform de-noising and segmentation processing on received signals; connected region labeling is performed on a processed image; the lasting durations of connected region labeling signals and labels corresponding to the lasting durations of the connected region labeling signals are dynamically clustered; secondary dynamic clustering is performed on the appearing time points of the connected region labeling signals and the labels corresponding to the appearing time points of the connected region labeling signals according to a dynamical clustering result; frequency hopping signal detection and extraction are performed according to a dynamical clustering result; and frequency hopping signal verification and quality evaluation extraction are performed on the extracted signals, and a frequency hopping energy diagram can be obtained. With the connected region labeling-based frequency hopping signal based dynamic clustering extraction method adopted, accurate extraction of the energy diagram of required frequency hopping signals can be realized when low-signal noise ratio strong interference exists, and different-time duration interference of which the period is identical with a hopping period exists, or another kind of frequency hopping signals of which the period is identical with the period of required frequency hopping signals exist.