33results about How to "Simplify the segmentation process" patented technology

The invention discloses a data safety protection method and device based on data partitioning. The method includes the steps that private data are extracted out of original data according to a preset private data extraction rule and partitioned into private data and non-private data; padding data are added to the private data, and encrypted data are obtained; a mapping table is generated according to the serial number of the private data, the padding data and the encrypted data; meanwhile, an index table is generated according to the serial number of the private data, nouns of the mapping table and original file names corresponding to the private data; an information safety card of the private data is generated according to the mapping table and the index table, and the information safety card of the private data is stored in a database according to preset private data storage strategies. According to the data safety protection method and device, the data are partitioned, the private data generate the mapping table and the index table, the information safety card of the private data is established, storage is conducted through different strategies, storage and reading efficiency is improved, and data portioning and encrypting difficulty is greatly simplified.

The invention discloses a polarization SAR image segmentation method based on characteristic value measurement spectral clustering. The method mainly solves the problems that the number of parameters is large and adaptive adjustment is difficult to carry out in the existing polarization SAR image segmentation process. The method includes the steps of 1, carrying out eigenvalue decomposition on a polarization SAR image to form a feature sample set x, 2, solving corresponding mean values of three eigenvalues in an 8 neighborhood of each pixel to form a mean feature sample set, 3, respectively building a similarity matrix for the feature sample set x and the mean feature sample set utilizing mahalanobis distance so as to obtain a mixed similarity matrix w' according to the two similarity matrixes, 4, obtaining a clustering label C1 of the mixed similarity matrix w' through the spectral clustering algorithm, and 5, repeating the steps 3 and 4, integrating obtained class label sets by utilizing the MCLA so as to obtain a final segmentation result. The polarization SAR image segmentation method has the advantages of being high in adaptivity, low in complexity, detailed and accurate in segmentation result, and capable of being used for target detection and target recognition of the polarization SAR image.

The invention provides a method for segmenting and marking a multi-branch tubular structure in a three-dimensional image, and belongs to the field of medical image processing. According to the method,a multi-branch tubular structure is regarded as a set of a plurality of single-branch tubular structures, and for the single-branch tubular structures, a center line is obtained through key point detection and neural network fine adjustment methods; then a cross section image of the tubular structure is acquired along the center line based on the center line, and finally a segmentation result ofthe tubular structure is acquired by using a segmentation network. The specific multi-branch tubular structure existing in the image can be segmented and automatically marked, and especially for someimages with lesions, such as aortic dissection, aortic aneurysm and coronary artery stenosis images, a good segmentation result is achieved.

The embodiment of the invention discloses a head image area segmentation method and device, electronic equipment and a storage medium, a point cloud registration method and device, electronic equipment and a storage medium, and a face modeling method and device, electronic equipment and a storage medium. The head image region segmentation method comprises the steps of determining an image region where a body part in a target image is located from the acquired target image; calculating a width value of the body part in the target image based on the determined image area; based on the width value of the body part in the target image, determining the segmentation position of the trunk object and the head object in the target image; and performing image segmentation on the foreground image region in the target image at the segmentation position to obtain a head image region. According to the embodiment of the invention, the head image region can be obtained based on the width value of the body part in the target image, so that the segmentation accuracy of the head image region is improved, and the segmentation process is simplified.

The embodiment of the invention discloses a video segmentation method and device, equipment and a medium. The method comprises the steps: obtaining a to-be-segmented video, and determining the corresponding relation between knowledge point data and video frames in the to-be-segmented video; and segmenting the to-be-segmented video according to the corresponding relationship to obtain at least onevideo clip. According to the video segmentation method provided by the embodiment of the invention, the to-be-segmented video is segmented according to the knowledge point data in the to-be-segmentedvideo, so that the segmentation process of the video is simplified, and the segmentation accuracy of the video is improved.

The invention relates to a data packaging method capable of matching multiple transmission modes and a system. The method comprises steps: S1, service data are divided into fragmented files with fixed lengths; S2, according to a broadcasting plan, the fragmented files and auxiliary data are added in an interleaving mode in a broadcasting queue; S3, the fragmented files are packaged into a plurality of UMCP (Unit Multi-Cast Protocol) blocks and whether data check is added to the UMCP blocks according to the link quality condition; S4, one or more UMCP blocks is packaged into network protocol frames according to the needs of network adaptation. Through carrying out pre-treatment on the fragmented files with fixed lengths, the data dividing process is simplified, service data concurrence in multiple channels can be realized, the broadcasting efficiency is improved, reliability of transmission is improved through mixed broadcasting of service data and auxiliary data and the check function, selective data receiving is realized through service needs, service flexibility and receiving efficiency are improved, the method can be adapted to multiple kinds of data link layer network protocols, and cross network collaboration service in different network conditions can be realized.

The invention discloses a text segmentation method and device, and relates to the technical field of cloud computing and text processing. The specific implementation mode comprises the steps: acquiring a target text and performing sentence segmentation processing on the target text, thus obtaining a sentence segmentation result; in response to any sentence segmentation result, determining that thelength of the sentence segmentation result exceeds a preset length threshold, and performing word segmentation processing on the sentence segmentation result to obtain at least three vocabularies; performing vocabulary combination on at least two vocabularies in the at least three vocabularies to obtain word groups; comparing the length of the current word group with a preset length threshold; and in response to the fact that the length of the word group does not exceed a preset length threshold, taking the word group as a segmentation result of the target text. According to the method, the text segmentation process can be simplified, computing resources are saved, and the text processing efficiency is improved. Moreover, the length of the text segmentation result can be controlled, and the problem that the obtained segmentation result is too long to achieve the segmentation purpose is avoided.

The invention discloses a segmentation method, system, medium and device based on a U-Net network. The method comprises the following steps: acquiring a brachial plexus ultrasound image; performing down-sampling on the brachial plexus ultrasound image for four times to obtain a first feature map of which the size is 1/2 of the size of the image before down-sampling; performing U-Net-based jump connection on the first feature map to obtain a second feature map with significant features; decoding the second feature map based on U-Net, and recovering to the size of the brachial plexus ultrasound image; and carrying out convolution on the decoded features to obtain a segmentation mask of the brachial plexus ultrasound image. According to the invention, no operator is used for image preprocessing, and the image segmentation process is simplified; the features are extracted by using an efficient OfficientNet series network, so that the speed is high and the effect is good; U-Net jump connection is designed, more significant features are extracted on a large receptive field by using cavity convolution and maximum pooling operation, and the effect of noise suppression is achieved.

The invention provides a segmentation method and a segmentation system for large joint tissue, solving the technical problem of low segmentation efficiency of the existing joint tissue. The segmentation method comprises the steps: forming an image segmentation reference; forming tissue types and position features of a single pixel in the MRI image according to the image segmentation reference so as to determine a tissue plane contour; and forming a tissue three-dimensional contour according to the tissue plane contour in each MRI image in combination with the image segmentation reference. Thetissue segmentation process is simplified by utilizing skeleton features; related image and information characteristics are used to form automatic processing of an object contour and identification accuracy is increased; and an image quantization rule is formed by using a segmentation reference, and pixel connotation information and a modeling framework are effectively fused, so that a large jointcomposition tissue can realize automatic main body segmentation, accurate contour positioning and automatic three-dimensional modeling in an MRI image, and the identification efficiency of professional human identification resources is effectively improved.

The embodiment of the invention discloses a model separation method. The method comprises the following steps: obtaining a three-dimensional model containing a heart model according to the obtained three-dimensional model, constructing a three-dimensional matrix of the three-dimensional model; performing corrosion operation in morphology; performing operation on the three-dimensional matrix by adopting the constructed first-class three-dimensional matrix; obtaining a three-dimensional matrix after corrosion operation; performing expansion operation in morphology; calculating the three-dimensional matrix subjected to corrosion operation by adopting the constructed second-class three-dimensional matrix to obtain a three-dimensional matrix subjected to expansion operation, and subtracting thethree-dimensional matrix of the three-dimensional model from the three-dimensional matrix subjected to expansion operation to obtain a difference matrix so as to separate the auricle model from the model corresponding to the difference matrix. The embodiment of the invention also discloses a model separation device, a terminal and a computer storage medium.