682results about How to "Quick split" patented technology

The invention discloses a main carotid artery blood vessel extraction and thickness measuring method based on neck ultrasound images. The method comprises the following specific steps of: reading neck ultrasound three-dimensional body data, and marking a main carotid artery axis based on a main carotid artery branching node; sequentially projecting and segmenting the neck ultrasound three-dimensional data in the three-view drawing direction to obtain two-dimensional cross section, coronal plane and vertical plane sequence images; and preprocessing, dividing and reconstructing the two-dimensional cross section, coronal plane and vertical plane sequence images or counting the thickness of intima-media membrane to obtain the final relevant information of internal and external profiles of the neck ultrasound main carotid artery blood vessel wall and the thickness of the blood vessel wall. By the method, the defects that the calculating complexity in the blood vessel dividing method is high, the thickness of the blood vessel wall cannot be accurately measured and an error is likely to be caused by subjective factors during computer-aided diagnosis are overcome, and the internal and external profiles of the neck ultrasound main carotid artery blood vessel wall and the thickness of the blood vessel wall can be completely, rapidly and accurately obtained. In comparison with the manual dividing method, the method is rapid in operation, and can be used for auxiliary diagnosis and prevention and treatment of neck atherosclerosis and cardiovascular disease.

The invention discloses an image type fire flame identification method. The method comprises the following steps of 1, image capturing; 2, image processing. The image processing comprises the steps of 201, image preprocessing; 202, fire identifying. The fire identifying comprises the steps that indentifying is conducted by the adoption of a prebuilt binary classification model, the binary classification model is a support vector machine model for classifying the flame situation and the non-flame situation, wherein the building process of the binary classification model comprises the steps of I, image information capturing;II, feature extracting; III, training sample acquiring; IV, binary classification model building; IV-1, kernel function selecting; IV-2, classification function determining, optimizing parameter C and parameter D by the adoption of the conjugate gradient method, converting the optimized parameter C and parameter D into gamma and sigma 2; V, binary classification model training. By means of the image type fire flame identification method, steps are simple, operation is simple and convenient, reliability is high, using effect is good, and the problems that reliability is lower, false or missing alarm rate is higher, using effect is poor and the like in an existing video fire detecting system under a complex environment are solved effectively.

The invention discloses a tunnel back-break detection method. The method includes the following steps that S1, according to center line data of a tunnel line, the thickness value and width value of a section are set, and tunnel section point cloud automatic partitioning is carried out; S2, the partitioned section point cloud is projected to the XOY plane, and a convex hull extraction method is adopted for carrying out automatic extraction of a section point cloud contour lines to obtain a measured section line; and S3, the tunnel design section is utilized, the distances between feature points of the tunnel design section and the measured section line and the locations of the feature points are calculated, and back-break automatic detection is carried out. Tunnel back-break detection is carried out by utilizing three-dimensional laser-point cloud data, and rapid partitioning of the section point cloud is achieved by adopting radius searching and a rectangular partitioning algorithm; on the basis of back-break area statistics of a polygonal intersection algorithm, the method is suitable for back-break detection of various types of tunnels; and compared with a traditional total-station section measurement mode, the detection efficiency is high, results are comprehensive and full and accurate, and the measurement precision meets the back-break detection requirement.

The invention discloses a steel rail surface defect image adaptive segmentation method. The method comprises the following steps of S1, extracting a steel rail region by adopting a row grayscale mean successive summation method; S2, preprocessing a steel rail region image; S3, performing structure region and non-structure region division on the steel rail region image; S4, further distinguishing a defective region and a shadow region by utilizing a non-local feature of the image in the structure region; S5, adaptively building a background image model according to different features in the image; S6, performing image difference; and S7, performing dynamic threshold segmentation. The image is divided into the structure region and the non-structure region by utilizing image local information, the size of a pixel neighborhood window is adaptively adjusted by utilizing non-local information to calculate a mean, the accurate background image model is built, and the image difference and the dynamic threshold setting are performed, so that while a defective part of the image is highlighted, the influence of uneven illumination and steel rail surface reflection property on steel rail surface defect detection is effectively reduced, an ideal image segmentation effect is achieved, and the rail surface detection precision is ensured.

The invention discloses an image enhancement and partition method, which comprises the following steps of I, image enhancement: a processor and an image enhancement method based on fuzzy logic are adopted for performing enhancement processing on an image needing to be processed, and the process is as follows: i, converting from an image domain to a fuzzy domain: mapping the grey value of each pixel point of the image needing to be processed to a fuzzy membership degree of a fuzzy set according to a membership function (shown in the specifications); ii, performing fuzzy enhancement processing by utilizing a fuzzy enhancement operator in the fuzzy domain; iii, converting from the fuzzy domain to the image domain; II, image partition: partitioning a digital image subjected to enhancement processing, i.e. a to-be-partition image, according to an image partition method based on a three-dimensional fuzzy division maximum entropy. The method is simple in steps, reasonable in design, convenient to realize, good in processing effect and high in practical value, and an image enhancement and partition process can be simply, conveniently and quickly completed with high quality.

The invention discloses an effective image-region detection and segmentation method for iris recognition. The method comprises: S1, roughly positioning an iris on the basis of Purkinje image analysis in an iris image; S2, accurately positioning the inner and outer boundaries of the iris on the basis of an elastic force model; S3, positioning upper and lower eyelids of the iris on the basis of sorting filtering and histogram filtering; and S4, detecting lashes and shadow on the basis of a lash-shadow blocking-degree prediction model in the image. The method can rapidly and accurately obtain the boundaries of the iris, effectively solves the problems that the conventional iris segmentation algorithm is low in speed and precision, and provides rapid reliable positioning and segmenting results for subsequent iris feature analysis. The method can be widely used in a large number of application systems using iris recognition for identity recognition and security protection.

The invention discloses an abnormal vehicle license plate recognition method and a system. the method comprises the following steps: 10, an image of an abnormal vehicle license plate is captured; 20, the image of the abnormal vehicle license plate is pre-treated to obtain a filter image; 30, a vehicle body image is detected in the filter image; 40, the abnormal vehicle license plate is positioned in the vehicle body image; 50, characters of the abnormal vehicle license plate are segmented in the vehicle license plate region; and 60, a Chinese character classifier, an alphabet classifier and a mixed number and alphabetclassifier are arranged respectively so as to recognize the characters of the abnormal vehicle license plate, and the following operation is carried out on each classifier. Thus, the vehicle license plate can be recognized in the abnormal condition, and the vehicle license plate recognition precision is high.

The invention discloses a track center line automatic detection method based on vehicle-mounted mobile laser point cloud. The method comprises the following steps: S1, point cloud segmentation of a roadbed and a track; s2, track point cloud classification; s3, automatic track center line detection: according to a cross section design drawing of a standard steel rail, automatically reconstructing astandard three-dimensional geometric model of the steel rail in a segmented manner, carrying out iterative registration on the standard steel rail model and the segmented steel rail point cloud in the S2, and carrying out automatic reconstruction on a segmented three-dimensional model of the steel rail; calculating track geometric parameters by utilizing the reconstructed track three-dimensionalgeometric model, calculating track center line point locations in a segmented manner on the basis of the reference track, and sequentially connecting the track center line point locations extracted inthe segmented manner to form a line center line. According to the method, roadbed and rail point clouds can be rapidly segmented, the area of rail point cloud classification search is reduced, automatic classification of laser point clouds on the surfaces of left and right steel rails is realized, and the precision requirement of existing rail transit midline detection can be met.

The invention relates to a method for automatically separating adherent hyaline-vascular type lung nodule in a CT image, comprising the following steps: (1) inputting an interested body of the CT image containing the adherent hyaline-vascular type lung nodule; (2) preprocessing the interested body, thus obtaining a front image area; (3) extracting flow direction characteristics based on a relational matrix on the front image area; (4) establishing an adherent hyaline-vascular type lung nodule model based on the flow direction characteristics, direction and angle; (5) estimating model parameters based on the expectation-maximization method in the model; (6) obtaining mean shift bandwidth parameters by calculating the parameters of the model; and (7) putting the mean shift bandwidth parameters into the mean shift clustering algorithm for automatic separation. The method of the invention can obtain the self-adapting bandwidth parameters in an accurate and rapid manner while applying the method for selecting the self-adapting bandwidth parameters to automatically separate adherent hyaline-vascular type lung nodule in a CT image, thus meeting the dual requirements on speed and accuracy in practical application.

The invention discloses a biochip analysis method based on an active contour model and a cell neural network. The method comprises the following steps that improved Hough transformation is adopted to perform slant correction on a rectangular sampling point, and improved Radon transformation is adopted for a circular sampling point; initial positioning is performed on the sampling points by using a projection method, and an optimized network is generated; then the network is adaptively adjusted on the basis of neighborhood search, and secondary precise positioning is performed on the sampling points; the active contour model is optimized by using a greedy algorithm, and a CNN (Cable News Network) is utilized to classify the sampling points in accordance with signal strength; Multiple snakes are combined with the CNN, the CNN first learns about the convergence behavior of the sampling point snake with a strong signal and then guides the convergence of the sampling point snake with a weak signal, and finally, reasonable partition of the sampling points is realized; and signal data of microarray sampling points is extracted and output. By using the method, the problems of slant correction of a biochip image, difficulty in partition of sampling points with irregular shapes and sampling points with weak signals and the like are solved, automatic identification of biochip sampling points is realized, and the method is suitable for quick analysis of large-scale biochip sampling points.

The invention discloses a method for segmenting characters form a video image, which comprises the following steps: positioning an original character image in the video image; extracting character stroke information from the original character image, obtaining a binary stroke image of the original character image according to the character stroke information; establishing a color model aiming at a character region in the binary stroke image, extracting a color layer image of the character region according to the color model; and removing excess backgrounds and noise by using an improved connection body analysis method with stroke masks so as to obtain the target character image. The invention has little computation and good property and can rapidly and accurately segment the characters from the video image of the complex backgrounds without machine learning.

The invention provides a non-contact biometric feature identification method and system. The method comprises the following steps of: extracting biometric features of a visible light image and a near-infrared image collected in a non-contact way; calculating the similarity x1 between the biometric feature of the visible light image and a prestored visible light biometric feature template and the similarity x2 between the biometric feature of the near-infrared image and a prestored near-infrared light biometric feature template; and comparing a detected sample x=(x1 x2) consisting of x1 and x2 with a training sample to judge whether the detected sample belongs to a within-class sample, and if so, judging that the identification is successful. By using the method provided by the embodiment of the invention, the identification precision of the method is greatly higher than that of a single biometric feature identification way, and the problems of less feature information and low distinguishing precision caused by the adoption of the current single palm pulse identification way and poorer anti-fake capability caused by the adoption of a single palm print identification way can be solved.

The present invention provides a method and a system for medical image automatic segmentation. The method for medical image automatic segmentation comprises: obtaining a salient image of a to-be-trained medical image by using a visual attention model, and using the salient image to train parameters of a deep learning neural network; obtaining a salient image of a to-be-segmented medical image by the visual attention model, and inputting the to-be-segmented medical image to the trained deep learning neural network for segmentation, to obtain an initial segmentation result; using the initial segmentation result to construct an initial contour of a statistical shape model and to optimize the statistical shape model, and using the optimized statistical shape model to segment the to-be-segmented medical image. The method combines the statistical shape model and the deep learning model, and calculated amount of matching operation in the statistical shape model is reduced by using the initialsegmentation result of the deep learning network, thereby realizing fast and accurate segmentation of the three-dimensional medical image by the statistical shape model.

The invention provides a three-dimensional lung vessel image segmentation method based on a geometric deformation model. The method comprises the following steps: (1) determining vessel segmentation computing regions according to the physiological structure characteristics of a human body, wherein region selection completely covers targets to be segmented and the shape characteristics of the regions are stable, thereby avoiding computing a global region and improving segmentation speed; (2) computing the mean value of the vessel regions and positioning internal and external homogeneous regions of the targets; (3) computing vessel edge energy and evolving a curved surface along second derivatives in an image gradient direction so that the curved surface is accurately converged to a target edge; (4) correspondingly establishing a three-dimensional vessel segmentation curved surface evolution model and effectively combining the mean value and edge energy of the internal and external regions of the lung vessels; and (5) adopting optimized level set evolution for obtaining solution according to the established deformation model and impliedly solving a curved surface motion according to the level set function curved surface evolution. A large quantity of lung CT image experiments proof that the method provided by the invention has the advantages of rapid and accurate lung vessel segmentation and strong robustness.

The invention discloses a frequency-domain-analysis-based method for detecting a region-of-interest of a visible light remote sensing image, belonging to the technical field of remote sensing image processing and image recognition. The method comprises the following implementation processes of: (1) preprocessing the image; (2) carrying out quaternion Fourier transformation on the preprocessed result to obtain the frequency domain information of the image; (3) retaining a phase spectrum and obtaining the high-frequency information of a magnitude spectrum by using a Butterworth high-pass filter; (4) carrying out quaternion Fourier inversion on the phase spectrum and the high-frequency information of the magnitude spectrum to obtain a characteristic pattern; and (5) filtering the characteristic pattern by using a gaussian pyramid and reducing dimensions to obtain a saliency map; (6) carrying out threshold segmentation and binaryzation on the saliency map to obtain a region-of-interest template; and (7) raising the dimensions of the template and carrying out masking operation on an original image so as to obtain the final region-of-interest. The method realizes the rapid and accurate location of the region-of-interest, has the advantages of high accuracy of region description, low computation complexity and the like and can be applied in fields such as environmental monitoring, land utilization, agricultural investigation and the like.

The invention discloses a nasopharyngeal carcinoma radiotherapy target region automatic segmentation method based on a deep neural network, and belongs to the field of nasopharyngeal carcinoma radiotherapy target region automatic segmentation. The method comprises the following steps: acquiring CT image data of a patient, sketching a nasopharyngeal carcinoma radiotherapy target region and endangering organs based on the CT image data, and dividing the sketched CT image data into a training set and a verification set; constructing a deep neural network segmentation model, preprocessing trainingsamples in the training set, using the preprocessed training samples for training the deep neural network segmentation model, wherein preprocessing comprises the steps of reducing the numerical rangeof the training samples to a specified range and augmenting the training samples; and reducing the numerical range of the training samples in the verification set to a specified range, inputting thenumerical range into the trained deep neural network segmentation model, and quantitatively evaluating the recognition effect of the model. According to the invention, the model can automatically output the segmentation result of the nasopharyngeal carcinoma radiotherapy target area and organs endangering the nasopharyngeal carcinoma radiotherapy target area in a short time only by inputting the CT image data of the patient.

The invention discloses a captive test (CT) image partitioning method based on adaptive learning. The CT image partitioning method comprises the following steps of: 1) acquiring a CT image; 2) extracting characteristics of the CT image; 3) inputting strokes for representing a lesion region and a non-lesion region on the CT image by a user; 4) constructing a region model of the image by taking the strokes input by the user as a basis according to the extracted characteristics of the CT image, and constructing an edge model of the image by adopting an edge detection method; and 5) combining the region model and the edge model to construct a new model, calculating the new model to obtain a partitioning result. By adopting the method, a difference between the lesion region and the non-lesion region on the CT image can be effectively described; the method adapts to the complexity of the CT image; the problems caused by low signal-to-noise ratio (high noise) of the CT image are solved; the user can quickly and precisely partition the lesion region on the CT image in an interactive manner with high efficiency; and therefore, the production efficiency of a medical department can be greatly improved.

The invention discloses a spectral-domain optical coherence tomography (SD-OCT) retina image choroidal neovascularization (CNV) segmentation method based on concavity and convexity, and belongs to the technical field of image processing. The method comprises the following steps: first, estimating the retina and choroid area of an input SD-OCT image, and positioning the internal limiting membrane (ILM) and the choroid-sclera junction (CSJ); then, estimating the retina pigment epithelium (RPE) layer according to the gradual change feature of the reflectivity of the retina image, and estimating the Bruch's membrane (BM) layer based on the concavity and convexity of the RPE layer; and finally, estimating a preliminary CNV region according to the thickness difference between the RPE layer and the BM layer, and correcting the upper border of CNV to get a final CNV segmentation result. The experimental results show that the algorithm proposed in the invention can be used to segment CNV robustly and precisely, and is of great significance to facilitating subsequent CNV quantitative analysis and improving the work efficiency of doctors.

The invention discloses a uniform angle division orthodontic arch wire bending sequence planning method and relates to the technical field of orthodontic arch wire bending techniques. The uniform angle division orthodontic arch wire bending sequence planning method is provided according to individual orthodontic arch wire curves of patients, on the basis of an orthodontic arch wire curve formationcontrol point information set and a robot movement information set of formation control points and with the combination of characteristic that an orthodontic arch wire is bent by using a robot. The method is technically characterized by comprising the following steps: according to the individual orthodontic arch wire curves of patients, inputting the orthodontic arch wire curve formation controlpoint information set M and the robot movement information set N of the formation control points into an orthodontic arch wire bending system; dividing the individual orthodontic arch wire curves; andsequencing divided uniform division domains.

The invention discloses a solid wood floor surface defect detecting method based on image fusion and division, and relates to the field of floor surface defect detecting. Aiming at the problems that due to the fact that division of a region growing algorithm is low in speed and inaccurate, solid wood floor surface defect detecting is low in speed and accuracy, and quality and sorting grades of solid wood floors are affected, the solid wood floor surface defect detecting method includes the steps that an R component image of defects is extracted and shrunk, and a region growing method is used in a low-dimensional image space to rapidly position the defects; the shrunk image is amplified and recovered by using gradient information interpolations, and the defects are marked to generate a reference image; the edges of the reference image are retrieved and marked through wavelet transform, and taboo quick search is performed on an original image with edge pixel points as seeds to rapidly and accurately divide defect regions. Defect detecting tests are performed on 20 sample images containing live knots, dead knots and cracks, the average dividing time is 13.21 ms, and the accuracy rate of defect division regions reaches 96.8%.

The invention discloses an iris image segmenting method and device. The method includes performing rough positioning on a pupil in an iris image; expanding an image in a candidate area on the outer boundary of an annular iris into a rectangular image in a polar coordinate system and acquiring an excircle boundary estimation value of a target iris according to the gray level change of the rectangular image; intercepting an upper eyelid zone and a lower eyelid zone of the iris image according to the excircle boundary, acquiring boundary points of an upper eyelid and a lower eyelid, performing curve fitting on the boundary points of the upper eyelid and the lower eyelid, and obtaining an upper eyelid boundary curve and a lower eyelid boundary curve; performing noise detection on valid zones determined according to the upper eyelid boundary and a lower eyelid boundary; performing fine positioning on the excircle boundary of the iris through a radial projection method and performing fine positioning on the boundary of the pupil by utilizing a calculus detection operator. The invention solves a problem of low accuracy of low quality iris image segmentation performed by iris image segmenting methods in the prior art.

The invention discloses a lung CT image data segmentation method and system, and belongs to the technical field of medical image processing and artificial intelligence. The method comprises the following steps: labeling a lung contour and a target area, and carrying out numerical cutting and normalization processing on image data; performing training learning by using a first neural network to obtain a lung contour segmentation model; extracting a lung contour, determining a lung region of interest, and performing numerical cutting and normalization processing; cutting the lung region of interest, and performing training learning by using a second neural network to obtain a lung target region segmentation model; and segmenting a lung target area according to the image data and the lung target area segmentation model. The system comprises a labeling module, a first cutting normalization module, a first training learning module, a second cutting normalization module, a second training learning module and a detection module. According to the method, the processing efficiency of the lung CT image data is improved, and the target area in the lung CT image data can be quickly segmented.

The invention relates to a segmentation method of an interactive three-dimensional segmentation sequence image and an application thereof. The segmentation method is the relative fuzzy connectivity segmentation method which is based on three-dimensional voxel and confidence interval, and a similarity region is constituted by gathering pixels with certain specific similar characteristic. The selection of seed points is carried out on the three-dimensional image, thereby being capable of accurately judging whether the seed points belong to the points of a target segmentation object or not. The segmentation method does not need the excessive manual participation, the implementation speed is fast, the result can be obtained within a relatively short period of time, and the parameters do not need to be determined according to the experience. The application of the segmentation method of the interactive three-dimensional segmentation sequence image is used for the segmentation of a liver sequence image. The invention combines the spatial voxel and the similarity among the pixels of the CT sequence image based on the analysis of the characteristics of the abdominal liver CT image and uses the relative fuzzy connectivity method which is based on the three-dimensional voxel and the confidence interval to precisely extract the liver, thereby providing accurate data for the follow-up liver three-dimensional reconstruction.

The invention discloses a tomato grafting method capable of improving the survival rate. The tomato grafting method comprises the steps of variety selecting, strong stock culturing, scion forming, grafting and managing after grafting. High-quality seeds are selected and sterilized, a strong stock and scion are cultured, a W-shaped staggered grafting way is adopted, the contact area is enlarged, the rigorous and scientific later period maintenance is conducted, the resistance of a plant is improved, the cell division is promoted, the wound healing is accelerated, and the grafted young plant can recover and grow quickly. The survival rate of the grafted tomato young plant cultured according to the grafting method reaches more than 98%.