101results about How to "Improve labeling accuracy" patented technology

The invention relates to an indoor scene semantic annotation method based on RGB-D data. According to the method, a coarse-to-fine global recursion feedback semantic annotation framework based on the RGB-D data is built, in addition, the whole semantic annotation framework is divided into two major parts including the coarse-granularity region stage semantic label deduction and fine-granularity pixel stage semantic label refinement. The framework is different from the traditional region stage or pixel stage semantic annotation framework, the framework rebuilds the relationship between the coarse-granularity region stage semantic label deduction and the fine-granularity pixel stage semantic annotation, and a reasonable global recursion feedback mechanism is introduced, so that the coarse-granularity region stage semantic annotation result and the fine-granularity pixel level semantic annotation result realize the alternate iterative updating optimization. Through adopting the mode, the multi-mode information of different region layers in the scene images is better merged, and the general problem that an annotation base element is difficult to be properly selected in the traditional indoor scene semantic annotation scheme is solved to a certain degree.

The invention relates to a streetscape semantic annotation method based on a convolutional neural network and a semantic transfer conjunctive model. A device according to the streetscape semantic annotation method comprises a deep characteristic extracting part and a soft limited semantic transfer part. A more balanced training set is constructed, and furthermore a super-pixel classification deep model with prior information is trained. According to the streetscape semantic annotation method, the prior information of a scene can be sufficiently mined, and a characteristic expression with more remarkable difference is learned so that the annotation accuracy of a superpixel is greatly improved. Through a Markov random field model, an initial result is optimized and an unnecessary noise is eliminated so that an annotation result is further improved. Finally per-pixel annotation accuracy and average classification accuracy are respectively higher than 77% and 53%.

One or more embodiments of the invention provide an image data generation method and device. The method comprises the steps of obtaining a simulation object model of a target object and a simulation environment model of a target scene; constructing a simulation scene of the target scene based on the simulation object model and the simulation environment model; generating a rendered image based onthe simulation scene, and determining annotation information of the rendered image, the annotation information being used for representing distribution information of a simulation object model contained in the simulation scene in the rendered image. A simulation scene of a target scene is automatically constructed; rendering the simulation scene by using a three-dimensional rendering technology toobtain a plurality of target annotation images; therefore, a large number of actually shot images do not need to be shot on site, the actually shot images do not need to be labeled manually, the synthesized image with high image reality sense and high labeling accuracy can be generated quickly, and a large number of available sample data with labeling information are provided for model training.

The invention discloses a method for automatically annotating remote sensing images on the basis of deep learning. The method for automatically annotating the remote sensing images includes extracting visual feature vectors of the to-be-annotated remote sensing images; inputting the visual feature vectors into a DBM (deep Boltzmann machine) model to automatically annotate the to-be-annotated remote sensing images. The DBM model implemented in the method sequentially comprises a visible layer, a first hidden layer, a second hidden layer and a tag layer from bottom to top, and is acquired by means of training. The method for automatically annotating the remote sensing images has the advantages that the deep Boltzmann machine model implemented in the method comprises the two hidden layers (namely, the first hidden layer and the second hidden layer respectively), accordingly, the problem of 'semantic gaps' in image semantic annotation procedures can be effectively solved by the two hidden layers, and the integral annotation accuracy can be improved.

The invention discloses a multi-source multi-view-angle transductive learning-based short video automatic tagging method. The method comprises the steps of obtaining short video data; preprocessing the short video data to generate image key frames consistent in format, audio tracks, texts and semantic tags; extracting multi-view-angle eigenvectors of the image key frames, the audio tracks and the texts; establishing a short video tagging database, wherein the multi-view-angle eigenvectors and the semantic tags are stored in the short video tagging database; calculating the similarity between the multi-view-angle eigenvectors; establishing a multi-view-angle fusion space through the similarity between the multi-view-angle eigenvectors; and transductively solving the multi-view-angle fusion space, and automatically tagging the semantic tags to the to-be-tagged short video data. The invention furthermore discloses a multi-source multi-view-angle transductive learning-based short video automatic tagging system. According to the method and the system, multi-source information attached to the short video data is fully considered, so that the tagging accuracy is improved.

The invention discloses a medical image marking method and a system thereof, wherein the method and the system belong to the field of medical image processing. The method comprises the steps of performing analysis and image preprocessing on the medical image which is uploaded by a user, and obtaining a processed medical image; according to a preset operation distribution strategy, distributing themarking operation of the processed medical image to an assigned user supplying a marking process which corresponds with the processed medical image to the assigned user, wherein the marking process is used for marking the processed medical image by the assigned user; acquiring the marking result of the processed medical image by the assigned user, and performing storage. The medical image markingmethod and the system thereof can perform optimized marking on the medical image, and particularly the multi-mode multi-sequence MR image, and furthermore realize flexible and quick professional marking on the medical image.

The invention provides a corpus text processing method and device and electronic equipment. The method comprises the steps of inputting a corpus text set to be processed into a language model, and obtaining feature vectors of corpus texts; performing clustering processing on the corpus text set based on a clustering algorithm and the feature vectors of the corpus texts to obtain corpus classification information; modifying intention category annotation information annotated by the target corpus text to obtain the target corpus text; and adding the target corpus text into the original trainingsample to train a language model to obtain an optimized language model. According to the invention, clustering processing is carried out on the corpus text set through the language model and the clustering algorithm, and the intention category annotation information annotated by the target corpus information in the corpus classification information is corrected to train the language model, so thatthe language model can be iteratively optimized in the use process, the generalization ability of the language model and the clustering algorithm is improved, and the labeling accuracy of the intention category labeling information corresponding to the corpus text is improved.

The invention relates to an interactive behavior recognition method and device, computer equipment and a storage medium. The method comprises the steps of obtaining a to-be-detected image; inputting the to-be-detected image into a preset multi-task model to obtain key points and a detection box of pedestrians in the to-be-detected image, the key points being located in the detection box, and the multi-task model being used for pedestrian detection and human body key point detection; According to the key points of the pedestrian and a preset article shelf image corresponding to the to-be-detected image, determining interaction behavior information of the pedestrian and the corresponding article shelf. By adopting the method, the interaction behavior of the pedestrian and the article can beefficiently identified.

The invention relates to artificial intelligence, and provides an OCR image sample generation method and device, a printing form verification method and device, equipment and a medium, and the methodcomprises the steps: receiving an image generation instruction, and obtaining an image sample; inputting the image sample into a preset font typesetting generation model, obtaining first annotation information by performing text detection and character recognition on the image sample, and obtaining a simulation result generated by reconstruction of the font typesetting generation model; inputtingthe image sample and the simulation image into a preset style synthesis model, extracting style features and content features by the style synthesis model, and generating a synthesis result by the style synthesis model; and obtaining an OCR image sample label, recording the synthesized image as an OCR image sample corresponding to the image sample, and associating the OCR image sample with the OCRimage sample label. In addition, the invention also relates to a blockchain technology, and the information can be stored in the blockchain node. According to the invention, the OCR image sample withthe same texture style as the image sample is automatically generated, and the sample label is automatically labeled.

The invention relates to an image analysis method and a device, an electronic device and a storage medium, which relate to the technical field of image processing. The method comprises the following steps: scanning an object to be measured to obtain a sample image of the object to be measured; labeling a first type image in the sample image to obtain the label of the first type image, and generating an identification model according to the first type image and the label of the first type image; automatically labeling the second class of images other than a first class of images in the sample image by the recognition model to obtain the label of the second class of images. The present disclosure can realize automatic annotation and improve image annotation efficiency.

The invention discloses a multifunctional digital meter automatic reading method based on a target detection algorithm, and the method comprises the following steps: S1, carrying out the lightweight improvement of a YOLOv4 network model, and obtaining a YOLOv4 lightweight network model; S2, collecting the multifunctional digital table picture, and performing position calibration and category calibration on numbers on the multifunctional digital table picture; and S3, training the YOLOv4 lightweight network model by using the multifunctional digital table picture calibrated in the step S2 and the calibration information of the multifunctional digital table picture to obtain a YOLOv4 lightweight digital detection model. The multifunctional digital meter automatic reading method based on thetarget detection algorithm is high in recognition speed, high in recognition accuracy and good in stability, and the problems that an existing digital meter automatic reading method is prone to beingaffected by external environment changes and image imaging quality and lacks stability and accuracy of digital recognition are solved.

The invention relates to an image labeling method and device, computer equipment and a readable storage medium. The method can comprise the following steps: marking the same to-be-marked image according to a pre-trained odd number of image marking models, and obtaining corresponding odd number of first marking images; Wherein the image labeling model is used for labeling a target object in an image; And according to the odd number of first annotation images, determining a target annotation image of the to-be-annotated image. Image labeling method, obtaining An odd number of first annotation images according to an odd number of pre-trained image annotation models; improving the marking efficiency of the to-be-marked image; Besides, the computer equipment determines the target annotation image of the to-be-annotated image according to the obtained odd number of first annotation images, so that the problem that the annotation quality of the to-be-annotated image is low due to insufficientpersonal experience of annotators or incomplete existing rules is avoided, and the annotation accuracy of the determined target annotation image is improved.

The invention provides a text data labeling method and device, electronic equipment and a storage medium, relates to the technical field of data processing. The text data annotation method comprises the steps of obtaining to-be-annotated text data, and performing conversion processing on the text data according to a pre-trained topic model to determine vector representation data corresponding to the text data; determining the similarity between the text data through the vector representation data; determining similar text data of which the similarity exceeds a preset threshold, and extractingfirst text data and second text data of the similar text data in a preset similarity interval; and presenting the first text data and the second text data to a display interface, so that a target object marks the similar text data according to the first text data and the second text data. According to the method, the label labeling efficiency of the sample text data can be improved, and the user experience is improved.

The invention relates to the field of intelligent decision, and discloses an image classification method, which comprises the following steps: training a pre-constructed first convolutional neural network model by using a first annotated image set to obtain a first image classification model; performing image screening and segmentation processing on the to-be-labeled image set to obtain a segmented image set; performing classification labeling on the segmented image set by using a first image classification model to obtain a second labeled image set; combining the first annotated image set and the second annotated image set to obtain an annotated image set; using the annotation image set to carry out iteration annotation training on a pre-constructed second convolutional neural network model to obtain a target image classification model; and classifying the to-be-classified image by using the target image classification model to obtain a classification result. The invention also relates to a block chain technology, and the annotated image set can be stored in a block chain node. The invention further provides an image classification device, electronic equipment and a storage medium. According to the invention, the accuracy of image classification can be improved.

The invention relates to an obstacle map construction method, a cleaning robot and a storage medium. The obstacle map construction method comprises the steps of acquiring image information in the advancing direction of the cleaning robot; under the condition that it is judged that a target obstacle exists in the advancing direction of the cleaning robot according to the image information, determining position information of the target obstacle; and adding the position information into a grating map of the clean area to construct an obstacle map. According to the invention, the problems of tedious operation, poor experience and low labeling accuracy caused by manual labeling and adding of obstacle semantics during construction of an obstacle map in the prior art can be solved.

The invention relates to a digital labeling method and device for a three-dimensional object, belongs to the technical field of artificial intelligence, and solves the problems of high difficulty in knowledge labeling of the three-dimensional object and low labeling accuracy in the prior art. The method comprises the following steps: acquiring two-dimensional pictures, pose information and labeling labels of a three-dimensional object at different angles to form a training sample set; constructing an offset matrix and an enhanced angle rotation matrix of the sample based on the pose information in each sample; training the annotation network model by using the training sample set and the corresponding offset matrix and the enhanced angle rotation matrix to obtain an optimized annotation network model; and marking a to-be-marked three-dimensional object by using the optimized marking network model based on the two-dimensional picture and the pose information of the to-be-marked three-dimensional object. According to the method, the three-dimensional object labeling is converted into the two-dimensional labeling, the labeling difficulty is reduced, the association relationship between the labeling and the three-dimensional object is established through the artificial intelligence model, and the labeling accuracy of the three-dimensional object is improved.