13results about How to "Quick annotation" patented technology

The invention relates to the technical field of three-dimensional computer vision and point cloud processing, and provides a point cloud rapid labeling method based on a three-dimensional CAD model and RGB values thereof. The method aims at solving the problems that in the deep learning data set construction process, three-dimensional point cloud semantic information labeling efficiency is low, the labor cost is high, and the automatic labeling quality is poor. The color information of the three-dimensional point cloud is effectively converted into the semantic label, so that quick and automatic labeling of different three-dimensional and profile surfaces in the point cloud is realized. The method comprises the steps of establishing a category-RGB value-label mapping table, carrying out CAD entity modeling and assigning a standard color to an entity surface geometrical shape, setting entity model gridding parameters and exporting the entity model gridding parameters as a PLY format, assigning color RGB values of mesh model patches to vertexes, uniformly sampling expansion points of the mesh model patches, traversing the points and reading the RGB values, and finally obtaining a model. And adding labels according to the category-RGB value-label mapping table, and the like. The method can be applied to the fields of three-dimensional modeling, product quality detection, industrial design and the like.

The invention is suitable for the technical field of building structure recognition, and provides a building structure recognition method and device and electronic equipment, and the method comprises the steps: obtaining the information of a building structure, and the information of the building structure comprises the contour information of a wall body and / or a hole; wall body section contours with the same elevation are determined according to the information of the building structure; a DCEL structure is constructed according to the wall body section contours with the same elevation; determining all surfaces corresponding to the DCEL structure; the surfaces corresponding to the walls and / or the holes are removed from all the surfaces corresponding to the DCEL structure, the surfaces of the space in the DCEL structure are obtained, the surfaces corresponding to the walls are determined according to the contour information of the walls, and the surfaces corresponding to the holes are determined according to the contour information of the holes. Through the method, the marking efficiency and consistency can be improved.

The invention relates to the technical field related to data processing, in particular to an automatic labeling method, device and equipment based on a large model and a clustering algorithm. The method comprises the following steps: performing preliminary analysis on an original data set by using a natural language processing technology, and generating an initial label for each piece of data in the original data set; performing clustering analysis on the generated initial labels by applying a clustering algorithm, and grouping the initial labels based on similarity or relevance among the labels to obtain a plurality of initial label groups; selecting the most representative label from each initial label group to replace all labels in the initial label group so as to simplify a label system and reduce redundancy; judging the similarity between any two pieces of data in the original data set; and adjusting the label system based on the similarity.

The invention relates to the technical field of lumbar vertebra segmentation, and particularly discloses a small sample lumbar vertebra segmentation method based on space prior information and prototype iteration updating, comprising the following steps: obtaining a lumbar vertebra MR image sample set comprising a plurality of sample lumbar vertebra MR images; constructing a small sample lumbar vertebra labeling model, and training the small sample lumbar vertebra labeling model by using the lumbar vertebra MR image sample set to obtain a trained small sample lumbar vertebra labeling model; and inputting a to-be-labeled lumbar vertebra MR image into the trained small sample lumbar vertebra labeling model for labeling so as to obtain a lumbar vertebra labeling prediction result of the to-be-labeled lumbar vertebra MR image. According to the small sample lumbar vertebra segmentation method based on space prior information and prototype iteration updating, rapid, accurate and high-throughput lumbar vertebra labeling can be realized.

The application relates to a video labeling method and device, computer equipment, a storage medium and a computer program product. The method comprises the following steps: acquiring a playing time corresponding to a to-be-labeled image frame in a playing video; the to-be-labeled image frame is located in a video layer; labeling the to-be-labeled image frame in a labeling layer to obtain labeling information corresponding to the playing time; the labeling information is information of a target labeling frame corresponding to the to-be-labeled image frame, and the target labeling frame is located in the labeling layer; saving the playing video and the labeling information corresponding to the target labeling frame of each playing time in the labeling layer to obtain a labeled target video. The method can improve the efficiency of video labeling.

The invention discloses an image annotation method and device, equipment and a storage medium. The method comprises the steps of determining a to-be-processed image; in response to detection of a first labeling operation and a second labeling operation on a to-be-labeled object in the to-be-processed image, determining a first labeling point and a second labeling point respectively corresponding to the first labeling operation and the second labeling operation; detecting the movement position of the cursor at each moment; and under the condition that a third labeling operation on the to-be-labeled object is detected, displaying a labeling box for labeling the to-be-labeled object based on the first labeling point, the second labeling point, the moving position at each moment and the third labeling operation. The labeling frame determined by the labeling method is the rotating frame, and the size, the position and the rotating angle of the rotating frame are determined according to the to-be-labeled object, so that the labeling frame can better fit the to-be-labeled object, and the labeling frame does not need to be adjusted after labeling is completed; therefore, image annotation can be accurately and quickly performed, and the annotation efficiency and the annotation accuracy of the image are improved.

The invention relates to a network teaching video knowledge point classification extraction method and device, equipment and a medium, and relates to the field of video processing, the method comprises the following steps: using a preset character recognition model to extract text data of each video key frame in a video key frame sequence to generate a text sequence corresponding to each video key frame; inputting a text sequence corresponding to the video key frame into a knowledge point classification model trained to a convergence state, and generating a corresponding knowledge point label for each video key frame; and based on the timestamps corresponding to the video key frames and the knowledge point tags, cutting the network teaching video into a plurality of video clips, and merging the video clips corresponding to the same knowledge point tag to determine the network teaching video corresponding to each knowledge point tag. According to the invention, the network teaching video corresponding to the required learning and teaching content can be quickly and accurately found, and the learning and teaching efficiency is improved.

The application relates to the technical field of aviation equipment maintenance, and particularly discloses an aircraft wing carbon fiber skin ultrasonic automatic scanning system and method, which comprises a movable gantry frame and an industrial mechanical arm. The movable gantry frame comprises a plurality of stands and two bed bodies, the plurality of stands are respectively arranged at the bottom ends of the two bed bodies, the top portions of the two bed bodies are jointly provided with a movable cross beam, the top portion of the movable cross beam is provided with a horizontal sliding table, the top portion of the horizontal sliding table is provided with a vertical sliding saddle, the industrial mechanical arm is arranged at the bottom end of the vertical sliding saddle, the bottom end of the industrial mechanical arm is provided with a scanning probe, a limiting mechanism and a transfer trolley are arranged below the movable cross beam, and a control box is arranged below one of the bed bodies. In the application, the automatic lifting type automatic loading and unloading of the composite material wing component is realized through the automatic control technical means of the detection rack and the movable platform, the manual lifting risk is reduced, the labor intensity of the personnel is greatly reduced, and the risk of occupational diseases is reduced.

The invention discloses a method for assisting a doctor in quickly searching for anomalies in an fMRI image, and belongs to the crossing field of medicine and computers. Firstly, feature points are detected in two images by using an SURF algorithm; then, randomly selecting a point from a neighborhood around each feature point, comparing the gray values of the points, and coding a comparison result as 0 or 1; in this way, each feature point can generate a binary code. And the similarity between the two images is measured by calculating the Hamming distance of the codes. And finally, abnormal characteristic values in the image are screened and marked in the pathological image to assist a doctor to quickly find a lesion area, so that the diagnosis time is shortened and the diagnosis accuracy is improved. According to the method, feature point matching can be carried out under different scales, rotation and illumination conditions, and certain robustness is achieved.

The invention discloses a neuroblastoma identification and labeling system. According to the technical scheme provided by the invention, the system comprises a data acquisition module, a training test module and an identification labeling module, the data acquisition module is used for extracting pathological section images of neuroblastoma cases in a database to generate a first section set; the training and testing module is used for training and testing the first neuroblastoma recognition model by using the first slice set to obtain a second neuroblastoma recognition model; and the identification and labeling module is used for performing cell identification and labeling on the target slice image by using the second neuroblastoma identification model. According to the invention, various cells and / or tissues in the pathological section image can be identified and labeled by using the trained neural network model based on artificial intelligence, and a doctor is assisted to carry out pathological judgment, so that the workload of the doctor is greatly reduced, the working efficiency is improved, and extremely high identification speed and accuracy are ensured.

The present disclosure provides a knowledge base update method, device, electronic device and storage medium for intelligent customer service, which belongs to the field of artificial intelligence technology. The method includes: screening out candidate recording files according to user satisfaction with multiple recording files; obtaining abnormal recording files based on preprocessed files corresponding to the candidate recording files; and updating the knowledge base based on updated question and answer pairs generated based on target question data in the abnormal recording files. The present disclosure does not need to play the recording files completely. Based on the user's satisfaction with the answer data provided by the intelligent customer service, the candidate recording files are screened, and on this basis, the candidate recording files are preprocessed to obtain abnormal recording files. The above-mentioned screening and preprocessing processes are used to greatly shorten the annotation time of abnormal recording files and improve the update speed of the knowledge base.

The invention provides a picture data annotation and quality detection method, system and device and a medium, and belongs to the technical field of picture data processing.The method comprises the following steps that a picture data annotation model is constructed based on an annotation task, and a pre-annotated public data set is used for training; obtaining a set number of picture data, and marking the picture data by using the picture data marking model to obtain a picture quality detection data set; constructing a machine learning model according to a predefined picture quality label, and training by using the picture quality detection data set to obtain a picture quality detection model; the method comprises the following steps: acquiring to-be-processed picture related data, performing data preprocessing, identifying a labeling task type by using a picture data labeling model, performing automatic labeling of the corresponding task type, performing automatic quality detection by using a picture quality detection model, and generating a picture quality detection report. According to the invention, automatic annotation and quality evaluation of the picture are realized, and the picture processing efficiency and processing effect are improved.

The invention discloses an MBD model construction method and system for compartment products, and a storage medium. The method comprises the following steps: S1, constructing an MBD model information standard library of reusable parts in the compartment products; s2, dividing reusable parts into a plurality of hierarchies, setting views needing to be displayed and filled information, and constructing an MBD data set after all hierarchies are filled with the information; s3, filling the corresponding information of the designed compartment products with the information of each level in the MBD data set to obtain MBD models of the compartment products, and constructing all the MBD models into an MBD database; s4, comparing the new product with the MBD model in the MBD database, and if the MBD model with the same name as the new product exists, filling the information of the MBD model into the new product; otherwise, filling the information of the MBD model closest to the name of the new product into the new product; s5, supplementing and revising the MBD information in the new product to obtain an MBD model of the product; according to the method, the MBD model of the compartment type product can be quickly constructed.