179 results about "Region growing algorithm" patented technology

The invention relates to a high-resolution remote sensing image variation detection method based on self-adaptive threshold division. The method comprises the following steps of: firstly, geometrically registering high-resolution remote sensing images during two periods; obtaining difference images of the high-resolution remote sensing images during two periods by a difference method; obtaining a gray value threshold extracted by a variation target by a maximum infra-class variance algorithm; extracting a variation target pixel by the gray value threshold; during the detection after-treatment period, eliminating most detected noise pixels through morphology conversion with respect to more disordered variation spots; extracting the sized variation region by a region growth algorithm in combination with the threshold setting of the pixels contained in the variation region to be extracted; and finally obtaining the border outline of the variation region by a border detection method, overlapping the border outline to an original image and drawing a complete variation region. The invention does not need too much manpower, has rapider speed and can achieve favorable detection precision and visual effect.

The invention is a character extracting method in complex background color image based on runlength adjacency graph, which belongs to character extracting field in preprocess of color image character identification. All color connection sections are acquired with CRAG (color run-length adjacency graph) region growing algorithm after the digital color image is acquired, then the color average is carried on with color classification, acquires several color centers, forms different color layers with the color centers, then the color connection regions accordant to connection region differentiation rule are distributed onto several color layers. Finally the character is selected out through character analysis and size consistency differentiation, and acquires the character image. The method has a high speed and accuracy.

The invention relates to a ship monitoring method. The monitoring method comprises a target identification method and a target tracking method, wherein in the target identification method, after an intercepted real-time video image and a background image are subjected to differential operation and binarization, a ship target is obtained by using a region growing algorithm, and the identified ship target is tracked and locked. By matching the target identification method, the target tracking method, a target locking method and a pairing comparison and database management method, a monitoring system can capture a ship and acquire continuous ship video under the condition of fog or night so as to realize all-weather monitoring of a shipping lane, ensure smoothness of the shipping lane and safety of the ship, reduce manual operation and realize automatic management; and the data information and the like acquired by monitoring are stored into a database, so that the data information and the like are convenient for digital management of the ship, can be timely called as required and can assist managers in learning more information about the ship.

The invention relates to a method for partitioning brainstem areas automatically from MR (magnetic resonance) sequence images. The method is used for partitioning the brainstem areas automatically and continuously from the human body encephalic MR sequence images, so as to offer technological bases for reconstruction of 3D model of the encephalic tissues to doctors to judge the state of an illness of the patient efficiently and intuitively. The method comprises steps of (1) preprocessing: carrying out gray level clustering analysis on all MR nerve sequence images, and clustering the gray levels of all the MR images to be processed to be five-level gray level images; (2) selecting seed point pixel, obtaining the outline border of the brainstem area of the first MR image through a neighbor region growing algorithm based on the image area growing rate; (3) roughly partitioning the current MR image through a block region growing method; (4) repartitioning the roughly partitioned MR images through logical judgment treatment and the region growing method; and (5) repeating the step (3) and the step (4) till finishing partitioning all MR images.

The invention relates to an intelligent extraction method of a build-up area on the basis of nighttime light data. The intelligent extraction method comprises the following steps: adopting an adaptive particle swarm optimization algorithm to realize the optimal selection of the selection parameters of a VIIRS (Visible Infrared Imaging Radiometer Suite) nighttime light image sample and a MODIS (Moderate Resolution Imaging Spectroradiometer) normalized difference vegetation index image sample; adopting a region growing algorithm based on SVM (Support Vector Machine) classification to finish SVM model training, and adopting a cross validation method to carry out precision validation on the model; and according to an optimized parameter, determining a city sample and a non-city sample, and adopting the region growing algorithm based on the SVM to extract a city built-area range. By use of the intelligent extraction method, from a sample selection source, the adaptive optimization of a sample selection parameter is carried out, and the SVM and the region growing algorithm are adopted to improve processing efficiency and precision for the nighttime light data to extract the nighttime light data.

The present invention discloses a Chinese character recognition method based on an optical character recognition technology. For an inputted gray scale text image, a cutting result is obtained by a hierarchical structure cutting method based on a connected region, then the binaryzation and denoising operations are carried out on the image by a region-growing algorithm based on the pixel distribution characteristics to obtain a to-be-recognized character, and then the to-be-recognized character is sent to a general single character recognizer to recognize to thereby obtain a candidate character set, and finally, a final recognition result is obtained by a similar character classification and recognition method. Compared with the prior art, the Chinese character recognition method based on the optical character recognition technology of the present invention enables the character recognition accuracy and efficiency to be improved effectively, enables the text information to be inputted to a computer at a high speed, solves the conflict between the low-speed information input and the high speed information processing, and enables the onerous keyboard recording work to be simplified.

The invention discloses a method for measuring the size of a three-dimensional reconstruction liver model on the basis of an improved region growing algorithm. The method includes the steps that seed point selection and growth criteria in a traditional region growing algorithm are improved by means of a quasi-Monte Carlo method, and an abdomen CT image is segmented by means of an improved region growing segmentation method to extract a liver area; a segmented binary image is used for three-dimensional reconstruction to obtain the three-dimensional reconstruction liver model only provided with a surface mesh, and the model is sealed; a regular square bounding box is arranged, the bottommost face of the bounding box is provided with a projection plane, the sizes of pentahedrons defined by a triangle patch on the reconstruction model and a projection projected by the triangle patch on the projection plane in an enclosed mode are calculated, wherein the triangle patch is provided with a normal vector in the positive direction and a normal vector in the negative direction; finally, the algebraic sum of the sizes of the pentahedrons is the size of the liver model. The method can well represent the shape of the liver, and therefore size measurement is effectively carried out on the three-dimensional reconstruction liver model. Besides, the size of a part of the liver can be measured, and measurement precision is high.

The invention discloses a dynamic image moving object detection method. The method includes firstly, reading two images of time-consecutive frame and respectively subjecting the images to Gaussian filtering; secondly, calculating consecutive frame differential images to obtain a binary image with moving information; thirdly, utilizing the improved Chan-Vese algorithm to detect the moving object of the image; and finally, utilizing the improved region growing algorithm to detect the accuracy of the moving object. The two consecutive frame differential images are used as a seed pixel point set of the moving object, the Sobel edge detection operator is used for detecting the pixels around the seed pixel, the pixels around the seed pixel are marked as moving object pixels if the pixels around the seed pixel are detected as edge pixels, the detected edge pixels and the moving object detection result are mixed, and accordingly the moving object can be detected accurately.

The invention discloses a cerebrovascular image segmentation method based on a multi-angle serialized image space feature point set, comprising the following steps: S1, registering a live image and a mask image of cerebral vessels; S2, extracting a geometric feature point set of a serialized subtraction image; S3, locally adjusting the positions of the feature points at the edges of the blood vessels in the serialized subtraction image; S4, adopting a spatial rotating coordinate system to remove the feature points in non-vascular positions in the subtraction image; S5, determining the adaptive segmentation threshold of the serialized subtraction image; and S6, based on region growing and vascular image segmentation of adaptive threshold and by taking the feature points obtained in S4 as seed points and the adaptive segmentation threshold obtained in S5 as the standard of the law of growth, adopting a region growing algorithm to segment the serialized subtraction image to obtain a pure cerebrovascular image.

The invention discloses a method used for detecting polysilicon solar energy battery piece EL image surface dark patch defects. According to the method, binaryzation extraction of a target battery pieces is carried out based on a battery piece EL image collected using a near infrared camera, image segmentation is carried out through a region growth mode based on complex and various background interferences caused by polysilicon so as to obtain possible defect connected domains; two methods are adopted for elimination of false detection, wherein according to one method, connected domain analysis is carried out so as to extract connected domain area and opening area characteristics, and according to the other method, the images corresponding to the connected domains are subjected to curve detection so as to eliminate false detection through image texture analysis. The method is capable of determining solar energy battery piece dark patch defects accurately, and realizing locating of darkpatch defects.

The invention provides an object positioning method and device based on RGB-D information and a machine vision system, and relates to the technical field of digital image processing. The object positioning method based on RGB-D information comprises the following steps: obtaining environment images under a current viewpoint; based on an RANSAC algorithm, in geometrical similarity measurement criterion, introducing a color similarity metric to measure the similarity between points and a plane, carrying out partitioning on a point cloud graph, and selecting a seed point from each block in sequence to obtain plane parameters; carrying out local point judgment according to color information and geometry information of the environment images, carrying out re-estimation on the plane parameters and extracting a plane characteristic equation; and obtaining a point cloud cluster of each object under the current viewpoint through a region growing algorithm based on the RGB-D information to realize object positioning. The method realizes an efficient and stable object localization algorithm through combination of the RGB-D information and the region growing algorithm, can locate a plurality of objects having a certain mutual shielding, and has high real-time performance and robustness.

The invention discloses a Laser point cloud-oriented single tree segmentation method based on Faster R-CNN. The method comprises the steps of obtaining forest stand point cloud data; calculating pointcloud features of the scanned forest segments to realize branch and leaf separation of forest segment point cloud data; performing adaptive voxelization operation on the main point cloud data of theforest stand, and performing multi-angle projection on the main point cloud data to generate a corresponding depth image; detecting a trunk in the generated depth image by adopting a deep learning method; and obtaining the spatial three-dimensional point cloud of the corresponding trunk through back projection by utilizing the position information of the trunk in the detected depth image; and taking the obtained point cloud of the trunk part as a seed point, and combining a region growing algorithm to realize individual tree separation. According to the method, a deep learning method is adopted, learning is carried out by means of big data samples, the accuracy of individual tree segmentation is higher, and possibility is provided for accurately solving the problem of individual rubber tree segmentation based on LiDAR data of the ground by using deep learning.

The invention relates to a method for segmenting an inhomogeneous medical image. The method for segmenting the inhomogeneous medical image comprises the following steps that firstly, foreground seed points and background seed points on the image to be segmented are selected; secondly, the probability that each grey level belongs to the foreground or the background of the image to be segmented is evaluated according to grey level information of a selected seed point set, the grey levels are mapped on all pixel points of the image, and therefore a corresponding probability density distribution graph is obtained; thirdly, the selected foreground seed points and the selected background seed points are used as growing seed points respectively, one probability threshold on the corresponding probability density distribution graph is used as a growing condition, a region growing algorithm is executed, and therefore a foreground seed point group and a background seed point group which have grown automatically are obtained; finally, the obtained seed point groups which have grown automatically are used as seed points of a random walk algorithm, the random walk algorithm is executed, and a final segmentation result is obtained. By the adoption of the method for segmenting the inhomogeneous medical image, the sensitivity to the number and the position of initial seed points can be reduced, and the segmentation precision of the inhomogeneous medical image is obviously improved.

The invention provides a method for manufacturing femtosecond laser complex components in a large batch. The surface processing of the complex component comprises: setting, according to the area and the focal depth of a laser processing region, a constraint condition and a region growing algorithm by using a complex component model reconstruction and surface processing pattern splicing algorithm;dividing the processing region, simplifying the region boundary to obtain the processing region, extracting the key features of the processing pattern types and layout features; designing a slicing algorithm according to the process of periodic and non-periodic layout in order that the processing pattern is automatically and quickly laid out on the surface of the complex component; by using the processing region division and pattern segmentation algorithm and setting the element type and combination in the processing pattern, obtaining the intersection of the element and the processing regionboundary and classifying the sub-pattern to the corresponding processing region; by using processing pattern real-time detection and online splicing manufacturing technology and by setting and installing a high-precision CCD camera, obtaining the video image of a processed part, recognizing the boundary of the processed pattern in real time based on an image processing algorithm, and then according to the boundary information of the pattern to be processed, predetermining the registration error of the two processing patterns, obtaining a required correction transformation parameter, and achieving online splicing manufacture in a laser processing process.

The invention provides a product surface defect detection method and device, and equipment. The method comprises: obtaining an image and determining whether the image is a to-be-detected image needingto detect a specified type of defect or not; if yes, carrying out sliding sampling on the to-be-detected image by utilizing a sliding window through a pre-trained defect detection model, carrying outdefect detection on the sampled window area, and outputting a position identifier of the window area with defects in the to-be-detected image; carrying out image segmentation on the window area withthe defects by utilizing an image segmentation algorithm to obtain contour areas of the defects in the window area with the defects; and connecting the adjacent contour regions through a region growing algorithm to obtain the form and number of defects existing in the to-be-detected image. According to the invention, a sliding sampling detection mode can be carried out on an image, and the problems of time and labor waste, incapability of detecting surface fine defects and poor positioning performance in the prior art are solved.

The invention discloses a method for estimating the defect degree of carbon fiber surface based on a region growing algorithm. Firstly, image preprocessing is performed on a carbon fiber profile imageto obtain a preprocessed surface defect enhancement image; then, an ROI region of the carbon fiber profile image is extracted based on the preprocessed image so that defect regions can be found out better; after that, the regional growth algorithm is used for segmenting the defect regions of the researched carbon fiber surface image from the image; finally, the proportion of defects in the imageto the entire image is calculated by using a matlab correlation program, and the number of the defects in the image can be counted. According to the method, not only can the tiny defects existing on the surface of a carbon fiber material be effectively segmented, but also the proportion of the tiny defects to the whole image can be calculated, a reliable measurement index is provided for evaluating the advantages and disadvantages of the carbon fiber material by professionals, and the effect of assisting in evaluation of the quality of the evaluated carbon fiber material is achieved.