67results about How to "Solve the segmentation problem" patented technology

The invention relates to a classifying method of three-dimensional point-cloud data, in particular to a feature ball based classifying method of three-dimensional point-cloud data of an outdoor scene. The method includes the steps of 1, constructing a conditional random field model; 2, constructing a three-dimensional point-cloud feature ball; 3, calculating point feature vectors; 4, calculating edge feature vectors; 5, calculating group feature vectors; 6, learning parameters of the conditional random field model; and 7, subjecting the three-dimensional point-cloud data to inference classification. The method has the advantages that feature vectors of all levels of three-dimensional point clouds are accurately and comprehensively calculated through construction of the three-dimensional point-cloud feature ball, the three-dimensional point cloud of the outdoor scene is accurately and reliably segmented, point cloud groups uniform in property are formed, the problem that imperfect structure of the point cloud feature vectors and inaccuracy of point cloud segmentation caused by the factors such as complex geometric topological structure of the outdoor scene are effectively solved, and the effect of classified recognition of the three-dimensional point-cloud data of the outdoor scene is greatly improved.

The invention discloses a similarity data clustering method for dam safety monitoring data. The method comprises the following steps of separating a single measuring point sequence trend term from thehigh-frequency noise by utilizing an EMD algorithm, detecting the time sequence change points by adopting an inflection point detection method of a cumulative sum control graph, and splitting to obtain all subsequence sets; adopting a DTW distance measurement method for calculating the distance problem of the subsequence, and calculating the distance minimum value between the two pieces of subsequence data dynamically; and clustering the mined sub-time sequences by using hierarchical clustering, and dynamically analyzing the time sequence clustering distribution condition under different clustering numbers through the obtained tree-shaped clustering graph. According to the method, the similarity of the monitoring data is reasonably analyzed, the correlation of the same monitoring point inthe time sequence can be mined, and meanwhile the correlation between the safety monitoring data can be quantified. And the monitoring data subjected to similarity analysis processing can accuratelyreflect the change trend of the dam in the time dimension, and the subsequent monitoring data mining difficulty can be effectively reduced in combination with the change trend rule.

The invention discloses a bar reviewing-counting method and device. Bundled bars can be automatically reviewed and counted by the method and device, and counting can be more precise and faster. The method has the advantages that the technical problem that background interference occurs in the site of end partitioning, feature recognition and removal detection for the bundled bars is solved in terms of image processing, and counting precision is improved; a high-brightness LED light source is used in the reviewing-counting device is used to enhance detection effect, an industrial camera is used to acquire images for the ends of bars, an industrial personal computer is used for smooth-filtering and setting a threshold for binary treatment, corrosion and connected domain treatment, counting and comparative judgment are achieved, counting results are output by a field display, and the device has the advantages of high speed, high adaptability, simplicity and reliability and the like; production efficiency is improved, labor intensity is reduced for workers, and the method and device has great practical value.

The invention relates to the technical field of multimedia information processing, and provides a video reorganization system capable of automatically partitioning a shot and a video reorganization method thereof. The video reorganization system is as follows: a partition processor is respectively connected to an original video library and a reorganizer; and a generation and storage module is connected with the reorganizer. The video reorganization method comprises the following steps that: a user opens the original video library and extracts a video in need of being processed from the original video library; the partition processor performs automatic shot partition of the video, such that an independent video processing unit is obtained; a single-video effect library, a replacement background module and a multi-video operation module of the reorganizer are used for correspondingly processing a video processing unit respectively; and the processed video is transmitted to the generation and storage module and then stored and played. The video reorganization system disclosed by the invention is convenient to install and low in system hardware requirement and is capable of realizing operations, such as automatic shot partition, single-video effect processing, background replacement and multi-video mixing, on the same platform; therefore, the video processing efficiency of the video reorganization system is ensured; and thus, the video reorganization system has perfect functionality.

The invention discloses a binary tree-based text line accurate locating method. The method comprises the steps of 1, constructing a binary tree search space for an over-segmented text line for an input natural scene image with the over-segmented text line; 2, simulating an access mode of postorder traversal in the constructed binary tree search space to search for paths, wherein each path is a node set, and the optimal path is a node set sequence obtained after all node sets are subjected to pruning and fusion operations; and 3, performing the pruning and fusion operations: for each node set contained in a path of the binary tree search space, a proper situation can be selected from eight pruning and combination policies to perform processing according to a confidence degree comparison result of a father node and left and right sub-nodes in the node set; and finally obtaining the optimal path which solves the over-segmentation problem, wherein the optimal path corresponds to a reset text line accurate locating result. According to the method, accurate locating of the text line in the scene image is realized.

The invention relates to the field of image segmentation, and particularly relates to an active contour image segmentation method and device based on improved SPF. According to the method, firstly, asign pressure function is redefined based on the global information of the image, and then a local image fitting LIF model is introduced, finally, a weight function model is established based on imagelocal and global information, the self-adaptive adjustment of the weights between the local information items and the global information items is achieved, and a new level set evolutionary equation is obtained. According to the method, the problem that the gray non-uniform image cannot be accurately segmented by only utilizing the image global information is solved, and the defect that a model based on the image local information is sensitive to noise and initial contour is overcome. The method is simple in calculation and high in convergence speed, and can effectively solve the problem of segmentation of multi-target and gray non-uniform images, and meanwhile, the robustness for the initial contour and the noise is relatively high.

The invention discloses an image segmentation method combining thermodynamic diagram channel and improved U-Net. The method comprises the following steps: 1) obtaining an original image; 2) preprocessing the original image to obtain a thermodynamic diagram; 3) constructing a multi-scale convolution module to improve a U-Net network structure; 4) entering a thermal map prepared in step 2) into theimproved U-Net network structure for image segmentation to obtain the segmented image. The multi-scale convolution module and a thermodynamic diagram channel are added to the U-Net network structure.Compared with a traditional U-Net model, the multi-scale convolution module and the thermodynamic diagram channel are added, so that not only can complete feature information be obtained, but also theprecision of a target edge can be effectively improved, and the edge is smoother.

The invention discloses a sign language image segmentation method based on centroid positioning and distance transformation. The method comprises the following steps: firstly, converting a read sign language image into a YCbCr color space, and performing skin color clustering; Performing smoothing processing on the segmented sign language image by adopting median filtering, performing maximum threshold binaryzation on the filtered sign language image, and finally performing processing by adopting an image region filling algorithm to ensure the integrity of a sign language region in the image;performing Processing by adopting area operator and centroid positioning to obtain a hand-arm region; separating the hand-arm region by the distance-based image based on the distance transform method,so as to achieve accurate segmentation of the hand region and the arm region. The method provided by the invention solves the problem of segmentation of the arm area, is suitable for segmentation oftwo conditions of a large bent arm and a long arm, has good robustness, can adapt to the two arm conditions, and can also adapt to gesture rotation and gesture size change.

The invention belongs to the field of electric logging value simulation, and discloses a method for simulating fracture-vug type reservoir body electric logging response. The method includes establishing an equivalent fracture-vug type reservoir body stratum model according to regional geological data, determining basic parameters of the equivalent fracture-vug type reservoir body stratum model; establishing finite element functions according to the DC logging response of the equivalent fracture-vug type reservoir body stratum model; carrying out grid discretion and resistivity assignment on the equivalent fracture-vug type reservoir body stratum model based on finite element theory; and assembling and solving finite element conductance array and obtaining depth lateral logging value. The method solves the problem of trans-scale grid dissection of a fracture-vug type reservoir body, effectively carves the boundary of the fracture-vug type reservoir body, scientifically simulates the values of fracture-vug type reservoir body electric logging, and provides theoretical basis for identifying and evaluating electric logging of carbonatite and volcanic fracture-vug type reservoir body according to electric logging data.

The invention discloses a wheat ear detection and counting method based on a deep learning point supervision idea, and the method comprises the steps: carrying out the pre-collection of a field wheatear image of a specific region, and obtaining an input image; inputting the input image into a network structure and obtaining output parameters; and obtaining each wheat ear patch and predicting thenumber of wheat ears. According to the network structure, firstly, features are extracted through a down-sampling network, and then the extracted features are subjected to up-sampling through an up-sampling path, so that the size of output is consistent with that of an input image, the probability of each pixel in the output image is obtained, and plaques of wheat ears are obtained based on the probability. The method provided by the invention can effectively overcome noise in a field environment, and realizes rapid detection and accurate counting of wheat ears. The reliability of the method is verified through various pictures of different field environments, different illumination intensities, different growth conditions and different shooting lens distances of two cities.

The invention belongs to the field of image processing, and provides a method and a device for image segmentation, a computer readable storage medium and terminal equipment so as to enhance the robustness of fuzzy image segmentation with noise. The method comprises the following steps that: carrying out local gray-level mapping processing on a to-be-segmented image subjected to Gaussian smoothingto obtain a first image; carrying out mean value filtering on the first image to obtain a second image; according to the category uncertainty and the regional stability of a pixel gray level, constructing an energy function E(t), and solving to obtain the corresponding threshold value toptimum when the energy function E(t) is minimum; and taking toptimum as a segmentation threshold value to segment the to-be-segmented image. By use of the technical scheme provided by the invention, on one hand, an influence caused by uneven grey level distribution can be effectively reduced, on the other hand,the segmentation problem of a fuzzy image under different noise levels can be effectively solved, and the method exhibits better robustness on the image with the noise.

The invention discloses a part identification method based on a multilayer random forest. The part identification method comprises the following steps: firstly, establishing an image training set anda test set; performing depth feature extraction; establishing a random forest classifier through training; and finally, constructing a multi-layer random forest for classification and identification.The beneficial effects of the invention are that the part identification method can achieve the recognition of the assembly state of the assembly and the recognition of parts at the same time, and canachieve the effective segmentation and recognition of the parts of the assembly in different assembly states.

The invention relates to the field of medical image processing and computer vision, particularly relates to a three-dimensional tooth model grid segmentation method based on a local attention mechanism. The method comprises the following steps: judging and classifying each triangular mesh in a three-dimensional tooth model by adopting a trained three-dimensional tooth model segmentation network, determining whether the area where each triangular grid is located belongs to gingiva or a certain tooth, and accurately finding the complete area where each tooth is located in the three-dimensional tooth model, and therefore the three-dimensional tooth model is accurately segmented. According to the method, the segmentation accuracy of low-feature-recognition-degree regions such as tooth edges and adjacent teeth can be effectively improved.

The invention provides a character segmentation method based on character features, which is applied to the technical field of image processing and comprises the steps of obtaining a to-be-processed image; performing binarization processing on the to-be-processed image to obtain a binarized image; performing feature extraction on the binary image by adopting a basic feature extraction network; forthe extracted features, carrying out feature extraction on the forms of the characters to obtain first features, and carrying out feature extraction on the number of the characters to obtain second features; fusing the first feature and the second feature by adopting a semantic segmentation network to generate a semantic segmentation graph; and determining a segmentation position of the characteraccording to the semantic segmentation graph. In addition, the invention further discloses a character segmentation device based on the character features and a computer storage medium.