80results about How to "Guaranteed Segmentation Accuracy" patented technology

The invention discloses a target identification and capture positioning method based on deep learning, and belongs to the field of machine vision. The method comprises the following steps that: firstly, utilizing a Kinect camera to collect the depth and the color image of a scene; then, using a Faster R-CNN (Regions with Convolutional Neural Network features) deep learning algorithm to identify ascene target; according to an identified category, selecting a captured target area as the input of a GrabCut image segmentation algorithm; through image segmentation, obtaining the outline of the target so as to obtain the specific position of the target as the input of a cascade neural network for carrying out optimal capture position detection; and finally, obtaining the capture position and the capture gesture of a mechanical arm. Through the method, the instantaneity, the accuracy and the intelligence of target identification and positioning can be improved.

The invention discloses a retina eyeground image segmentation method based on a depth full convolutional neural network. The retina eyeground image segmentation method includes the following steps of:selecting a training set and a test set, extracting retina eyeground images to obtain optic disk positioning area images, and performing blood vessel removal operation on the optic disk positioning area images; constructing the depth full convolutional neural network, taking the optic disk positioning area images as the input of the depth full convolutional neural network, and performing the training of an optic disk segmentation model on the training set based on trained weight parameters as initial values to fine tune model parameters, and performing fine tuning on parameters of an optic cup segmentation model based on trained optic disk segmentation model parameters; and performing optic cup and optic disk segmentation on the test set by utilizing a trained optic cup segmentation model, performing ellipse fitting on final segmentation results, calculating a vertical cup-disk ratio according to optic cup and optic disk segmentation boundaries, and taking a cup-disk ratio result as important basis for a glaucoma auxiliary diagnosis. The retina eyeground image segmentation method achieves optic disk and optic cup automatic segmentation of the retina eyeground images, has high precision and fast speed.

The invention discloses an image semantic segmentation method, electronic equipment and a readable storage medium. Based on an FCN model based on depth feature fusion, the image semantic segmentationmethod replaces the traditional convolution operation by cavity convolution, constructs original images with different resolutions to form image pyramids, hierarchically inputs the FCN model, fuses the output characteristics of the upper layer with the output characteristics of the lower layer, and fuses output features to a bottom layer from top to bottom layer by layer for transposed convolution, and the output features of the bottom layer perform transposition convolution so as to enable the output resolution to be consistent with a bottom-layer input image, thus improving the sensitivity to target positioning, and the segmentation precision is ensured through optimization processing of a full-connection conditional random field subsequently, thereby obtaining a better segmentation effect.

The invention relates to a power plant high-temperature pipeline defect detection and segmentation method based on OTSU and a region growing method, and the method comprises the following steps: obtaining an original infrared image, carrying out the graying, and extracting a pipeline region through the improved two-dimensional OTSU pre-segmentation; finding out a gray scale range corresponding tothe normal temperature of the pipeline through the gray scale distribution histogram of the pipeline area; judging whether the maximum value of the neighborhood gray average value exceeds a normal gray range or not to finish automatic detection and positioning of the multi-defect seed points; and taking the self-adaptive threshold adjusted according to the gray average value and the standard deviation of the grown region as a growth criterion, and taking a gradient amplitude threshold calculated based on a Prewitt operator as an additional condition to realize growth and extraction of the defect region. Compared with the prior art, the system has the advantages of high reliability and accuracy and good real-time performance, and can meet application requirements.

The invention discloses a turntable type UV laminator which comprises an upper frame and a lower frame, wherein an operating panel is arranged on the upper frame; a dispenser module, a rolling module and a UV hardener module are sequentially arranged on the lower side of the operating panel from the left to right; a control box is arranged on the lower frame; a divider connected with the control box is arranged in the middle of the table-board of the lower frame; a mold for bearing and positioning workpieces is fixed on the divider. According to the turntable type UV laminator, raw materials can be saved, the materials and manual cost are reduced, and the yield of the product can be greatly improved, so that the capacity of equipment can be 450-900PCS per hour.

The invention provides a semantic segmentation method, device and equipment and a computer readable storage medium. The method comprises the steps: obtaining a display operation for a three-dimensional model; in response to the display operation, displaying the three-dimensional model on a human-computer interaction interface, wherein the human-computer interaction interface comprises semantic segmentation options; obtaining a selection operation for the semantic segmentation options; and in response to the selection operation, after a two-dimensional segmentation result of a two-dimensional image is obtained, displaying a semantic segmentation result of the three-dimensional model on the human-computer interaction interface, wherein the semantic segmentation result is determined accordingto the two-dimensional segmentation result of the two-dimensional image and the model attribute of the three-dimensional model, and the two-dimensional image and the three-dimensional model belong tothe same scene. Through the semantic segmentation method based on artificial intelligence provided by the invention, the segmentation efficiency of the three-dimensional model can be improved, and the user experience is improved.

The present invention discloses a wavelet scatternet-based SAR image segmentation method, which resolves a technical problem that conventional texture-based SAR image segmentation is inefficient and time-consuming. Implementation steps are that: performing pre-processing of denoising and uniformization on an SAR image; setting a scattering transform path, selecting a wavelet function and a window function, and generating a scattering propagation operator and a scattering operator; performing scattering transform on the SAR image, to obtain a scattering coefficient of an SAR image pixel point; performing K-Means cluster on a scattering texture feature after dimension reduction, to obtain a preliminary segmentation result; searching for affine space in which pixels of different categories are located, to form an affine classifier; and performing sliding window correction on the preliminary segmentation result by using the affine classifier, to implement SAR image segmentation. According to the present invention, it is unnecessary to perform a partitioning operation on the SAR image, and the scattering texture feature that can reduce a difference between same texture and increase a difference between different texture is extracted, and precise and fast segmentation can be performed on the SAR image. The method is used for fast segmentation on a single SAR image.

The invention discloses a lung anatomy position positioning algorithm based on a deep learning technology, which can accurately and quickly divide lung CT, and can simply, quickly and accurately realize automatic segmentation of lung lobes based on lung CT images, thereby realizing the anatomy position positioning of lung lesions. Compared with a traditional segmentation method, the method has theoutstanding advantages that (1) the process is simple, and the end-to-end segmentation mode does not need to pay attention to other processes; (2) the multi-stage and multi-output network architecture controls the network in different stages, so that the segmentation effect is better, and the segmentation precision can be ensured to the maximum extent through a semantic-based segmentation mode; and (3) the generalization ability is strong, and the data in the training process is enhanced, so that the model can learn different and diverse data, namely, the generalization ability of the segmentation model is ensured, meanwhile, the risk of over-fitting is also avoided to a certain extent, and the geometric deformation and illumination influence of CT (computed tomography) are insensitive when lung lobe division is performed on different CT.

The invention discloses an image partitioning method and device. The method comprises the steps that a first frame three-dimensional point cloud image collected under a horizontal viewing angle is acquired; and a first ground equation and feature transformation are acquired; according to the first ground equation and point cloud data in the first frame three-dimensional point cloud image, a secondground equation of the first frame three-dimensional point cloud image is determined; according to the feature transformation and the second ground equation, a first ceiling equation of the first frame three-dimensional point cloud image is determined; according to the second ground equation and the first ceiling equation, first to-be-partitioned data is determined, wherein the first to-be-partitioned data is point cloud data, except point cloud data in the second ground equation and point cloud in the first ceiling equation, in the first frame three-dimensional point cloud image; and image partitioning is performed according to the first to-be-partitioned data.

The invention discloses an SAR image semantic segmentation method based on a fast ridgelet deconvolution structure learning model, which mainly solves the problems of inaccurate segmentation image and long model training time in the prior art. The method is performed through the following steps: 1. according to the sketch model of an SAR image, extracting a sketch graph; 2. according to the sketch graph, obtaining a region graph; using the region graph to divide the SAR image into a hybrid pixel subspace, a homogeneous pixel subspace and a structural pixel subspace; 3. Building a ridgelet filter set and a fast ridgelet deconvolution structure learning model and using the learning model to segment the hybrid pixel subspace; 5. using the visual semantics and sketch characteristics to sequentially segment the structural pixel subspace and the homogeneous pixel subspace; and 6. Combining the segmentation results of the three pixel subspaces to obtain a final segmentation result. The method of the invention improves the segmentation effect of an SAR image and can be used for detecting and recognizing subsequent SAR images.

The invention discloses an image segmentation method. The method comprises that an image to be segmented is loaded; a characteristic vector of each pixel in the image is collected; selection of a region of interest (ROI) and a non region of interest (NROI) is received; according to characteristic vectors of pixels in the ROI, the pixels in the ROI are clustered into a first amount of interest classifications, and classification information of each interest classification is determined; according to characteristic vectors of pixels in the NROI, the pixels in the NROI are clustered into a second amount of non interest classifications, and classification information of each non interest classification is determined; the probability that each pixel in the image is a pixel of interest is calculated; the color similarity of every two adjacent pixels in the image is calculated; and a label value of each pixel in the image is determined according to the probability that each pixel is the pixel of interest and the color similarity of every two adjacent pixels. The invention also discloses an image segmentation application in which the method is enforced and a computing device including the application.

The invention discloses a partition method for kidney artery CT contrastographic picture vessels based on a three-dimensional Zernike matrix. The partition method comprises the following steps: firstly, acquiring a forecast voxel point set and extracting a local geometric structure of the voxel point; then, constructing a descriptor with space rotation invariance by mapping the local geometric structure into a unit ball and solving the three-dimensional Zernike matrix; using the local geometric structure characteristic descriptors composed of different order and repeatability descriptors for expressing the characteristics of the local geometric structure in a quantizing form; and finally, adopting a study classifying model based on a support vector machine for classifying the acquired local geometric structure characteristic descriptors, thereby confirming the voxel points in the vessel lumen and acquiring a final vessel partition result through region growth. According to the method provided by the invention, the semiautomatic and accurate partition for kidney artery CT contrastographic picture vessels is realized, and the working efficiency of doctors and the accuracy of clinic auxiliary diagnosis are promoted.

The invention discloses an image segmentation method and device, equipment and a storage medium, and relates to the technical field of artificial intelligence. The method comprises the following steps: acquiring a first image sample and a second image sample, wherein the quality of annotation data of the first image sample is higher than that of annotation data of the second image sample; performing segmentation processing on the first image sample and the second image sample through a student network and a teacher network to obtain a student segmentation result of the first image sample, a student segmentation result of the second image sample, a teacher segmentation result of the first image sample and a teacher segmentation result of the second image sample; determining training loss according to the annotation data of the first image sample and the segmentation result; and training the student network based on the training loss. The image information is extracted from the samples with the low-quality labels through the teacher network, and the student network is trained, so that the problem of model overfitting caused by lack of the samples with the high-quality labels is avoided.

The invention provides a tongue image segmentation method based on color decomposition and the threshold technology. The method comprises steps that S1, a tone component of a tongue image in the HSI color space is transformed, and tone components of a tongue and an upper lip after transformation and the neighbor tissue are made to have tone difference; S2, image threshold segmentation of the transformed tone component is carried out to obtain the binary segmentation result, after morphological operation of the binary segmentation result is carried out, the initial tongue region containing thetrue tongue and the upper lip is extracted; and S3, the initial tongue region is corrected, two image threshold segmentation methods are selected, one of segmentation results of the two image threshold segmentation methods is utilized to separate the true tongue region from the upper lip region, and the final segmentation result of the tongue image is obtained. The method is advantaged in that themethod is simple and effective, and image segmentation performance is substantially improved.

The invention discloses a neural network model for segmenting an image, an image segmentation method and device thereof, and a readable storage medium, the neural network model comprises an intelligent selection module, and the intelligent selection module further comprises a feature extraction unit and an intelligent selection unit. As the feature extraction unit adopts multi-scale cavity convolution, information of different scales of an input feature map is obtained, and a large amount of rich feature information is provided for subsequent feature screening; and the intelligent selection unit carries out intelligent screening on the input feature map channel by training a weight value according to the size of the weight value, so that the intelligent selection module can reduce the parameter quantity and the calculated quantity while ensuring the segmentation precision. Therefore, the neural network model provided by the invention can quickly extract effective features of the imageby adopting the intelligent selection module, is small in calculation amount and few in model parameters, and is suitable for a mobile terminal.

The embodiment of the invention provides a segmentation method and device for a target object in a three-dimensional image and electronic equipment. The method comprises the following steps: accordingto a plurality of branch feature three-dimensional networks of a multi-channel three-dimensional network model, respectively carrying out feature extraction on three-dimensional images of a pluralityof modal groups to be segmented to obtain branch feature maps of a plurality of branches; according to the fusion feature three-dimensional network, performing feature extraction and fusion on the branch feature maps of the plurality of branches to obtain a fusion feature map; and according to the size amplification three-dimensional network, performing fusion and size amplification on the fusionfeature map and the branch feature maps of the plurality of branches to obtain a three-dimensional image of the segmented target object. In the embodiment of the invention, different morphological characteristics of the same target object in the three-dimensional images of different modal groups are extracted and fused, and the type and edge recognition precision of the target object is greatly improved, so that the segmentation precision of the target object is improved.

The invention provides a cell nucleus segmentation method and device, and the method comprises the steps: inputting a to-be-segmented image into a trained segmentation model under the condition that the resolution of the to-be-segmented image is not greater than a preset resolution, and obtaining a segmentation result of a cell nucleus in the to-be-segmented image, wherein the preset resolution isdetermined by the fixed input resolution of the trained segmentation model, and the trained segmentation model is obtained by respectively training preset segmentation models by adopting different types of training samples; training a preset segmentation model by taking a preset natural scene image as a training sample to obtain a first segmentation model; training the first segmentation model bytaking the multi-class tissue pathological images corresponding to the various tissues as training samples to obtain a second segmentation model; and finally, training the second segmentation model by taking the multi-class histopathological image of the required tissue as a training sample to obtain a trained segmentation model. The applicability of the segmentation method is improved.

The invention discloses an intelligent segmentation method for a video image target, and the method employs a YUV color space and a specific distance measurement to overcome the impact from illumination, maintains a plurality of clustering centers to process a dynamic background, and employs a maximum continuous unmatched time length parameter to exclude foreground pixels out of a background model. According to the method, structured background motion can be obtained for a long time in a limited storage space, and a compact model can be established for a dynamic background; the influence of illumination on background modeling and foreground detection can be overcome; periodically switching between a modeling stage and a detection stage is carried out to meet the application requirement oflong-time uninterrupted operation of video monitoring. The method has better segmentation accuracy and higher processing speed, and is more suitable for video object segmentation of scenes such as passenger flow statistics, traffic flow video monitoring, industrial automatic monitoring and safety protection.

The invention relates to a virtual manicure try-on method based on deep learning, and the method comprises the steps: obtaining a single image or video image containing a nail, employing a deep learning full convolution neural network method to segment and track a nail region in real time, combining with the color of the nail selected by a user and the re-coloring of a pattern template, and achieving the virtual manicure try-on function. Compared with the prior art, the method has the advantages that the deep learning and virtual reality technologies are adopted, the shot single picture or continuous video images containing the nails are processed, the nails are segmented and tracked in real time, and the function of virtual manicure try-on is achieved in combination with the selected templates such as the colors/patterns of the nails. According to the method, through the form of a mobile phone APP, customers are not limited by places and equipment, manicure try-on and selection are completed, and the experience feeling of the customers is enhanced.

The invention relates to an iris segmentation neural network model training method and an iris segmentation method and device. The iris segmentation neural network model training method comprises thesteps: acquiring an iris recognition sample set, wherein the iris recognition sample set comprises a plurality of iris recognition samples, and the iris recognition samples comprise iris recognition tags; training an iris recognition network model based on a network automatic search technology and the iris recognition sample set; determining an iris segmentation neural network model, wherein the iris segmentation neural network model takes an iris recognition network model as a basic feature network; acquiring an iris segmentation sample set, wherein the iris segmentation sample set comprisesa plurality of iris segmentation samples, and the iris segmentation samples comprise iris segmentation labels; and training an iris segmentation neural network model based on the iris segmentation sample set. Through the method and the device, the consumption of computing resources can be reduced on the premise of ensuring the segmentation precision.