36results about How to "Improve segmentation results" patented technology

The invention provides a superpixel method for medical image segmentation. The method comprises the steps of: processing a medical image into a superpixel; for the medical images obtained after superpixel segmentation, using bilateral filtering to preserve the edge and filter the noise to reduce the error rate of the network model; configuring a network framework, and constructing a convolution network for the medical images obtained after superpixel segmentation by iterative training parameters. Based on the linear iterative clustering segmentation method, this method applies the thought of the U-Net network to the post-optimization of super-pixels, which makes up the defect of inaccurate segmentation of inner edge of super-pixel, increases the standard layer to improve the weight sensitivity of each network layer, improves the convergence performance of the network, and makes the segmentation result closer to the actual value. Because the anatomical structure and pathological tissueof medical images are very clear, the medical images segmented by SLIC algorithm can obtain more comprehensive super-pixel, and the edge accuracy of super-pixel can be further improved by convolutionnetwork.

The invention relates to the technical field of artificial intelligence, and discloses a three-dimensional point cloud semantic segmentation method, device and equipment and a medium, and the method comprises the steps: carrying out the point cloud division and quantitative discrimination of to-be-predicted three-dimensional point cloud data through employing a preset space cell, and obtaining target point cloud data; inputting the target point cloud data into a point cloud semantic category prediction model to perform semantic category probability prediction to obtain a point cloud semantic category probability prediction value of the target point cloud data, wherein the point cloud semantic category prediction model is a model obtained by training based on a Point SIFT neural network module and a Point Net + + neural network; and determining a target semantic category of each point in the target point cloud data according to the point cloud semantic category probability prediction value. According to the method, rapid and accurate logic division is carried out on the point cloud of the complex large-scale target object, the recognition precision of point cloud segmentation is improved, fine features of the complex target object can be well processed, and the accuracy of semantic category prediction is improved.

The invention provides a weak supervised image semantic segmentation method based on iterative mining common features of objects, belonging to the technical field of pattern recognition. In the training phase, the method acquires the training data set, constructs and trains the multi-label classification network, and acquires the initial seed area corresponding to each training image. Then, the superpixel region and the region label of each training image are obtained for training the region classification network, and the updated region label of the superpixel region is obtained for trainingthe semantic segmentation network. After iteration, when the performance of the semantic segmentation network converges, the trained semantic segmentation network is obtained. In the use stage, the color image is input into the trained semantic segmentation network, and the network outputs the semantic segmentation results of the image. The invention can realize reliable pixel-level semantic segmentation under the condition of only image class label, reduces the time and labor cost of data labeling, and has wide application prospect.

The invention discloses a method for splitting images based on clustering of immunity sparse spectrums, mainly solving the problem that a spectrum clustering method has poor stability and high complexity. The method comprises the following steps: (1) extracting features of images to be split; (2) normalizing feature data to eliminate the magnitude effect among data; (3) carrying out real coding onthe normalized feature data; (4) randomly generating initial species groups of the coded data and calculating the affinity; (5) cloning based on the affinity size of an antibody; (6) carrying out Gaussian mutation on the cloned antibody species groups and selecting the antibody of the highest affinity as an input for the next round; (7) iterating the set maximum iterations to obtain a final selected sample subset; (8) carrying out greed spectrum dimensionality reduction on the selected sample subset, clustering the dimensionally reduced data and outputting the final image splitting result. Compared with the prior art, the method has the advantages of no need of priori knowledge, high accuracy and low calculation complexity. The method can be used for object detection and object identification.

The invention discloses a remote sensing image segmentation method based on a disparity map and a multi-scale deep network model, which mainly solves the problems of low segmentation precision and weak robustness of the existing remote sensing image segmentation method. The implementation scheme includes: reading a data set, and generating a training data set of remote sensing image segmentation;constructing a multi-scale fusion segmentation network model; using the training data set to train a segmentation network model, and storing seven models with different iteration times; obtaining seven different segmentation result graphs by using the stored segmentation network model; carrying out majority voting on the seven different segmentation result graphs, and carrying out super-pixel processing on the voted result graph to obtain a preliminary segmentation result graph; obtaining a disparity map of the test scene by using an SGBM algorithm; and optimizing the preliminary segmentationresult graph by using the disparity map to obtain a final segmentation result. Compared with an existing method, the method has the advantages that the segmentation precision and robustness are obviously improved, and the method can be widely applied to urban and rural planning and intelligent urban construction.

The invention discloses a tooth orthodontic treatment monitoring method based on an intraoral image and a three-dimensional model. The tooth orthodontic treatment monitoring method comprises the following steps that step 1, a preprocessed intraoral image and a dental jaw three-dimensional digital model of a patient at a P1 stage are obtained; step 2, tooth-gingiva and tooth-tooth segmentation is performed on the dental jaw three-dimensional digital model based on the intraoral image of the patient in the P1 stage, a segmented dental jaw three-dimensional digital model is obtained; step 3, a preprocessed intraoral image of the patient in a P2 stage is obtained; and step 4, the segmented dental jaw three-dimensional digital model is converted according to the intraoral image of the patient in the P2 stage, and a dental jaw three-dimensional digital model of the P2 stage and coordinate transformation data and morphological change data of the dental jaw models from the P1 stage to the P2 stage are generated. According to the tooth orthodontic treatment monitoring method based on the intraoral image and the three-dimensional model, coordinate transformation and form change conditions ofteeth of the patient after tooth orthodontic treatment can be known only by shooting the intraoral image, so that a scanner is prevented from being used for scanning an oral cavity after treatment.

The invention relates to an AMD lesion OCT image classification segmentation method and system based on a bidirectional guide network, and the method comprises the following steps: obtaining an OCT image, and dividing the OCT image into a training set, a verification set and a test set; constructing a mask complementary convolutional neural network for classification of OCT images; adopting a Grad-CAM algorithm to calculate a class activation graph of the mask complementary convolutional neural network, and obtaining the output of the class activation graph; constructing a class activation graph guided U-shaped segmentation network for segmenting a lesion area in the OCT image; training the network through the training set and the verification set to obtain an optimized mask complementary convolutional neural network and a class activation graph guided U-shaped segmentation network; and substituting the test set into the optimized mask complementary convolutional neural network and the U-shaped segmentation network guided by the class activation graph to realize classification and segmentation of the OCT image. According to the method, OCT images containing glass membrane warts, CNV and normal retinas can be accurately classified, and an accurate segmentation result of a lesion area is generated.

The invention discloses a remote sensing image building accurate segmentation method. The method comprises the following steps: constructing a building extraction network comprising a feature extraction module, a cavity convolution module, an attention module, an up-sampling module and a convolution prediction module; based on the training sample set, adopting a multi-scale composite loss function combining Dice Loss and BCE Loss to train the constructed building extraction network; and inputting a remote sensing image to be extracted into the trained building extraction network to obtain a building extraction result. The method has the remarkable effects that the feature learning and generalization ability is high; the network is low in complexity and easy to train; and the building extraction precision is high.

The present invention concerns a method of segmenting a media item comprising media elements. The method comprises: selecting a first seed media element for a first cluster of media elements, referred to hereinafter as a first adaptel; selecting a second seed media element, different from the first seed media element, for a second cluster of media elements, different from the media elements of the first adaptel, the second cluster being referred to hereinafter as a second adaptel; successively forming the first and second adaptels, starting from the first and second seed media elements, respectively, by allocating a first plurality of the media elements to the first adaptel, and then allocating a second plurality of the media elements to the second adaptel; and reallocating at least one media element of the first adaptel to the second adaptel if a dissimilarity measure between the at least one media element and the second adaptel is smaller than the dissimilarity measure between the at least one media element and the first adaptel. The second seed media element is selected from a set of the media elements neighboring the first adaptel.

The invention discloses a two-dimensional image auxiliary segmentation method based on three-dimensional point cloud of an overhead contact system cantilever system. The method comprises the followingsteps: step 1, acquiring three-dimensional point cloud data of the overhead contact system cantilever system; step 2, performing uniform resampling on the three-dimensional point cloud data by adopting voxel filtering; step 3, converting the uniformly resampled three-dimensional point cloud data of the cantilever system into a two-dimensional image; step 4, segmenting the two-dimensional image obtained in the step 3, then sequentially carrying out image closed operation and median filtering processing, and returning to the three-dimensional point cloud to obtain a segmentation result of eachlinear part of the three-dimensional point cloud of the cantilever system. According to the method, two-dimensional images are adopted for auxiliary segmentation, efficient segmentation results of alllinear parts of the overhead contact system cantilever system are finally returned, the method has the advantages of being good in noise immunity, high in robustness and high in precision, and the segmentation effect of the overhead contact system cantilever system is improved; and the consumption of manpower and material resources is reduced, and the influence of weather and experience judgmentof operators is avoided.

The invention belongs to the technical field of point cloud instance segmentation, and discloses a label optimization point cloud instance segmentation method. The method comprises the steps: firstly carrying out the feature extraction of a point cloud through a graph convolution neural network; establishing an instance label matrix for the training set, and performing label propagation on the instance label matrix by using a label propagation algorithm to obtain an optimized instance label matrix; and finally, performing instance segmentation on the point cloud by combining the label matrix and the optimized instance label matrix. According to the method, the similarity relationship of the points in the point cloud is fused, so that the supervision of the label matrix has better distinguishing representation, and a better segmentation result is obtained. According to the method, the global shape information and the local feature information of the large-scene point cloud model are considered, and the effect of integrating the global information is achieved; a label propagation algorithm is used for optimizing a label matrix, and the point cloud segmentation result is improved through the combined action of original data set labels and optimized labels.

The invention provides an interactive image segmentation method and an interactive image segmentation system based on a concentrated mistaken segmentation area, comprising the following steps of: carrying out foreground and background matting processing on initial segmentation of an input image to obtain foreground and background matting images; generating an under-segmentation and over-segmentation geodesic distance guide map for the input image and the under-segmentation and over-segmentation indication points; according to the input image, the initial segmentation image and the under-segmentation and over-segmentation geodesic distance guide image, extracting the characteristics of the whole image; extracting under-segmentation and over-segmentation region features according to the background and foreground matting images and the under-segmentation and over-segmentation indication points; and performing feature fusion on the under-segmentation and over-segmentation region features and the full-image features to obtain a corrected segmentation image. According to the method, the priori knowledge and the learning ability of the neural network are combined, the accuracy and interpretability of image segmentation are improved, the method serves as a means for obtaining segmentation data annotation, annotation can be completed only through several times of click interaction, andpixel-by-pixel annotation is avoided.