72 results about "Graphic recognition" patented technology

The invention discloses an AI intelligent image-text situation content accurate layout full-marketing generation method, and particularly relates to the technical field of image-text recognition. The method comprises the following steps: performing semantic analysis on a text input by a user, extracting a deliberate graph item, reverse semantics, an auxiliary emotion item and a tonality keyword, and constructing a multi-semantic field group; the field groups are injected into a collaborative generation engine composed of an image generation sub-model and a semantic prediction sub-model, and the semantic prediction sub-model predicts graph attribute tags and guides the image generation sub-model to conduct regional attention intervention; after the image is generated, the matching degree of the image content and the field group structure is evaluated through a structure-level semantic consistency judgment mechanism, and regeneration is triggered if the image content and the field group structure are inconsistent; finally, the image content with the aligned structure is output and subjected to image-text combination typesetting with the original text, and image-text situation content meeting the multi-platform marketing application requirement is generated. According to the method, the semantic accuracy, the structural consistency and the visual expression quality are remarkably improved while the generation efficiency is ensured.

The invention relates to the field of drawing recognition, in particular to a PDF drawing data extraction method and system based on intelligent recognition. Comprising the following steps: reading an internal structure of a PDF engineering drawing to obtain a native text stream, a vector path and a grating image; identifying the native text flow through a shunt preprocessing framework to form structured text data; rendering the vector path and the grating image to obtain a background image; analyzing the structured text data by utilizing the intelligent recognition model through the character recognition and extraction sub-model, obtaining drawing metadata and recording the position, and obtaining a character recognition result; analyzing the background image through a graphic element recognition and classification sub-model, recognizing and classifying component elements, and obtaining a graphic recognition result; and performing fusion according to the visual space corresponding relation to form a drawing analysis result. According to the method, the adaptive capacity of engineering drawings with various sources and different qualities is improved through the shunting preprocessing framework and the intelligent identification model.

The invention relates to the field of damaged vision recovery of artificial retinas, and particularly discloses a preparation method of a neuromorphic visual interface for recovering the pattern recognition capability of a blind animal. The interface is composed of a photo-thermal assembly, a photoelectric assembly and an electrode array. The photo-thermal assembly is dispersed in a continuous medium, can rapidly and transversely transfer a thermal signal, and is used for repairing the rapid extraction process of an animal visual pathway on a graphic contour; the electrode arrays are located on the upper surface and the lower surface of the photoelectric assembly, can vertically transmit electric signals in a delayed mode and correspond to the follow-up pattern detail machining process of a visual path. The interface also reproduces a central-peripheral activation-inhibition effect formed by retinal horizontal / bipolar cell transverse / longitudinal signal transmission, and realizes multi-channel signal connection with a visual center. The technology is mature, processing is simple, an artificial system which is highly matched with animal optic nerve signals and functions is developed, the artificial system is different from existing light magical vision, and recovery of the pattern recognition function is achieved.

The present invention provides a graphic recognition method and obstacle avoidance system for intelligent agricultural robots, which relates to the technical field of image pattern recognition. By introducing polarization imaging, the present invention effectively suppresses water droplet highlights, avoids misjudging optical artifacts as diseases, and significantly improves recognition accuracy. For low-confidence targets, the original micro-area thermal excitation physical review mechanism can confirm their true physical properties online, solving the problem of recognition uncertainty; in addition, the dynamic risk volume constructed based on multi-dimensional information, combined with the adaptive speed control of path risk foresight, realizes differentiated intelligent obstacle avoidance for rigid and flexible obstacles. While ensuring safety, this method greatly improves the robot's travel efficiency and smoothness in complex environments.

The invention discloses a standard part batch replacement method, device and equipment and a storage medium, and the method comprises the steps: loading a three-dimensional model of a clamp, carrying out the pattern recognition of a replaced standard part, and extracting part features; traversing the clamp three-dimensional model to identify a standard component needing to be replaced, and adding a reasonable interference part identification mark; capturing assembly surface features and executing standard part batch replacement operation according to the matching relationship; traversing the replaced clamp three-dimensional model, and generating a standard part batch replacement report according to the interference condition; and correcting the standard part batch replacement report, and storing the reasonable part interference identification marks in a database. By automatically recognizing the features of the replaced standard parts, screening the standard parts needing to be replaced in the clamp three-dimensional model, conducting batch replacement through feature matching and storing the reasonable interference recognition marks in the database for calling, the workload of manually marking the replaced standard parts and the reasonable interference marks is reduced, the misoperation rate is reduced, and the work efficiency is improved. The standard part batch replacement efficiency is improved.

The invention relates to the field of electric power systems, and discloses a relay protection setting value automatic checking system and method based on artificial intelligence. The method comprises the following steps of: inputting a constant value sheet into a constant value sheet table, carrying out region correction and pre-segmentation processing on the input constant value sheet picture by using an OCR (Optical Character Recognition) pattern recognition technology and a BMHS (Binary Matrix Hazardous Substances) improved algorithm, recognizing characters in the constant value sheet table one by one by using a TableMaster algorithm, and carrying out correlation comparison analysis on a recognition result and a preset dictionary, so as to correct the recognition result. The method comprises the following steps: recombining a corrected identification result into a complete fixed value sheet document, performing item-by-item comparative analysis on the recombined fixed value sheet document and fixed value items in a fixed value sheet published in a system, so as to automatically check the difference problem of the fixed value sheet, generate a check report, and display the check information of the fixed value sheet to a user, thereby improving the user experience. Checking errors can be reduced, and the efficiency and accuracy of constant value checking can be improved.

The invention provides a wafer defect measuring method and related equipment. The wafer defect measuring method comprises the following steps: acquiring wafer images of a wafer at a plurality of positions; identifying a graph in each wafer image through a graph identification model, and obtaining a corresponding positioning graph from the graph of each wafer image; positioning the wafer based on each positioning graph, and obtaining a defect image of the wafer; recognizing a graph in the defect image through a graph recognition model, and obtaining a target graph from the graph of the defect image; and measuring the wafer defect in the defect image based on the target graph. According to the wafer defect measurement method, the accuracy of pattern recognition and the accuracy of defect positioning can be improved, missing detection and false detection are further prevented, and the detection speed and the detection precision can be both considered. In addition, when the pattern recognition model is trained, only the pattern needs to be used for training, the data acquisition cost can be reduced, the data balance is improved, and the interpretability and generalization ability of the model are improved.

This disclosure relates to a hand-drawn graphic recognition method, apparatus, and system, and a computer-readable storage medium. The hand-drawn graphic recognition method includes: receiving an input of a hand-drawn graphic, the input including a stroke set, the stroke set including at least one stroke, each stroke including an information sequence of a plurality of trajectory points, information of each trajectory point including coordinates and writing time of the trajectory point; determining whether the stroke set forms a closed graphic according to coordinate data of the input stroke; and in the case where the stroke set forms a closed graphic, recognizing a category of the closed graphic based on the coordinate data of the stroke.

The present invention relates to the technical field of graphic recognition, and specifically relates to a ship type recognition method and system based on transfer learning, including the following steps: jointly preprocess visible light ship images and synthetic aperture radar images, separate the ship inherent feature layer and the environmental noise feature layer through an adversarial domain decoupling network, and generate a cross-domain feature matrix free of interference; strengthen the spatio-temporal consistency features related to the ship motion mode, and output an enhanced enhanced feature matrix; construct a differentiable decision tree classifier based on the enhanced feature matrix of S2, select the category with the highest probability as the final recognition result for output, compare the confidence level of the final recognition result with a preset threshold, and if it is lower than the preset threshold, trigger incremental learning. The present invention improves the adaptability to complex sea conditions, ship type diversity and occlusion conditions, significantly reduces the misrecognition rate and the success rate of adversarial attacks, and effectively improves the reliability of the system in an open environment.

The invention provides an intelligent graph recognition method and device based on collaborative drawing, equipment and a storage medium. The method is applied to the technical field of computers, and comprises the following steps: acquiring a canvas for collaborative drawing, performing pattern recognition processing on a pattern in the canvas for collaborative drawing, determining a boundary of the pattern, and performing segmentation processing on the pattern according to the boundary of the pattern to obtain at least one pattern and a pattern block corresponding to the pattern; the canvas for collaborative drawing is a drawing result obtained by collaborative drawing of a plurality of users; performing intelligent pattern recognition processing on the pattern block, and determining a visual component corresponding to a pattern in the pattern block; pixel point pickup processing is carried out on the graphic block, and each pixel point of the graphic block is traversed to obtain a pixel color value of the graphic block; and the data board is generated according to the visual component corresponding to the graph in the graph block and the pixel color value of the graph block, so that the identification accuracy is improved, and the development efficiency is improved.

The invention relates to a vector structure data generation method and device, equipment, a medium and a program product. The method comprises the steps of obtaining a target house type image; performing pattern recognition processing on the target house type image to obtain pattern recognition data; performing text recognition processing on the target house type image to obtain text recognition data; and generating vector structure data corresponding to the target house type image according to the pattern recognition data and the text recognition data. By adopting the method, the accuracy of the vector structure data can be improved.

The invention provides a drawing processing method and device for a digital ship electrical design platform, and the method comprises the steps: determining a selected target object from a target drawing in response to a first selection operation for the target drawing displayed in a graphical user interface of drawing design software, the target object comprises a target cable segment and target equipment blocks connected with two ends of a target cable; wherein different types of drawings correspond to different identification rules; identifying attribute information of the target object based on a target identification rule which corresponds to a pre-configured pattern identification mode and is matched with the type of the target drawing; and based on the preset table template matched with the type of the target drawing, performing structured processing on the attribute information of the target object, generating a target structured table, and displaying the target structured table in the graphical user interface, thereby realizing specialized recognition of the electrical system diagram in the ship industry, and improving the recognition efficiency and the recognition precision.

The application discloses a kind of based on multi-task learning's light hand-drawn figure recognition and parameter regression method and system, including hand-drawn track point sequence is preprocessed into standardization binary image, high-level semantic feature is extracted by feature extraction network, and class and parameter are output in parallel by classification branch and multi-parameter regression branch, call special regression subnetwork refinement regression for pentagon, pentacle, finally output shape type and geometric parameter according to JSON protocol.Model uses two-stage training, mask MSE regression is done to polygon, curve, circular class in joint stage, independent standard MSE regression is done to pentagon, pentacle in special stage, combined with weight cross-entropy classification loss and multi-branch selective back propagation strategy, the application realizes light, efficient hand-drawn figure real-time identification and regular, applicable to mobile terminal image editing class application.

The invention relates to the technical field of intelligent desks, and discloses an intelligent desk for guarding health of teenagers, the intelligent desk comprises a supporting device, a display device, a lighting device, a sensor device and a control device, the display device, the lighting device and the control device are all located at the top of the supporting device, and the sensor device is located in the supporting device. The panel camera can scan facial expressions of students, the facial expressions are analyzed through the mainboard CPU, the emotional state of the teenagers and whether the teenagers feel fatigue, tension or anxiety can be evaluated, and the information is very helpful for timely finding and correcting bad habits of the teenagers and providing psychological support. According to the utility model, students' homework can be scanned through the image-text recognition camera, the homework can be checked and answered through the mainboard CPU, so that the learning efficiency of the students can be effectively improved, and the display screen can be externally connected with other learning equipment through the USB connector, so that various learning contents can be provided for the students.

The invention belongs to the technical field of pattern recognition and garden engineering monitoring, and particularly discloses a garden landscape tree construction progress monitoring and recognition system which comprises a data acquisition layer, a data transmission layer, a system platform layer and an application layer. The data acquisition layer is used for acquiring construction site environment parameters and tree state data in real time; the data transmission layer transmits data of the data acquisition layer to the system platform layer by adopting a network communication technology; the system platform layer is used for data storage, analysis and visualization; the application layer is used for providing mobile terminal application and decision support functions; the construction progress recognition accuracy is improved, the advanced image recognition algorithm and the multi-sensor data fusion technology are adopted, the defects of a traditional color segmentation method are overcome, the construction progress can be recognized more accurately, personal errors are reduced, the tree growth environment is monitored comprehensively, and soil humidity, illumination, temperature and other environmental factors are monitored in real time through multiple sensors.

The invention relates to the technical field of image recognition and pattern recognition, and discloses a fusion tracking method based on AI large model video analysis and RFID positioning, which comprises the following steps: when a target enters a visual non-overlapping blind area, according to a phase gradient change trend of a radio frequency identification signal sequence acquired by a radio frequency signal acquisition unit, acquiring a target image according to a phase gradient change trend of the radio frequency identification signal sequence; generating a probability weight map representing the spatial position of the target; projecting the image to an image coordinate system, and generating an airspace attention weight adjustment mask; according to the method, the mapping relation between the radio-frequency signal phase gradient and the image neuron response weight is established, the spatial topology constraint is introduced in the feature extraction stage, structural contraction of the feature space is achieved, and the target handover is completed by utilizing the mask to modulate the depth feature map and extracting candidate target feature vectors according to the modulated depth feature map. And the target binding stability in the cross-monitoring-area tracking process is effectively enhanced.

The invention discloses a plane random placement image analysis processing system and a processing method thereof, and the system is suitable for a scanning device which is composed of a movable carrying platform, a scanning camera, an adjusting mechanism and a fixing mechanism. Comprising an image acquisition module, an image splicing module, an image preprocessing module, a target segmentation module, a shape recognition module, a pattern distribution module and an output module, and all the modules are called in sequence to form a closed-loop processing flow. The method comprises multiple steps of image acquisition, splicing, preprocessing, segmentation, identification, pattern distribution and result output, image frames are acquired by adopting a snakelike trajectory, and splicing fusion, accurate identification and task output of random image targets are realized in combination with strategies of displacement estimation, multi-scale fusion, brightness compensation, feature matching and the like. The method can be widely applied to scenes such as pattern recognition, pattern matching and batch output, and has the advantages of high automation degree of the processing flow, high recognition accuracy, high target placement randomness and the like.

The present invention is a process and structure of a ferroelectric neural network cell with self-learning capability and enhanced character recognition length. The ferroelectric neural network cell is a content addressable memory (CAM) structure (or FeCAM for short) based on a ferroelectric field-effect transistor (FeFET), and the FeCAM is a 1N1P-FeCAM including an N-type FeFET and a P-type FeFET. Therefore, the present invention reduces the number of devices to minimize cell area, and each cell consists of two complementary FeFETs to achieve a density that is 33% higher than that of Ternary CAM (TCAM). H2 plasma treatment (HPT) is effective in enhancing symmetry between ION1 and ION2 to increase the number of mismatch cells (Max HD, MHD). Therefore, the present invention is useful to graphic recognition and can be applied to various fields of artificial intelligence.

A 3D printing device and its laser positioning accuracy calibration method are disclosed. The calibration method includes: placing a standard plate with several graphics on a working plane; setting an imaging system at the laser incident end of a galvanometer; keeping the laser off and controlling the galvanometer deflection to scan any position A of each graphic; acquiring an image of the standard plate observed under the reflection of the galvanometer at position A through the imaging system; performing image processing on the standard image; extracting the coordinate values ​​of the positioning patterns at position A of the corresponding graphics using a graphic recognition algorithm; forming an actual matrix with the coordinate values ​​of the positioning patterns corresponding to all graphics in the array; calculating the deviation between the coordinate values ​​of each positioning pattern in the actual matrix and the corresponding target coordinate values ​​in the target matrix; and generating a galvanometer accuracy calibration table using a calibration algorithm. This invention's laser positioning accuracy calibration method for the 3D printing device improves accuracy and is simple to implement.

The present disclosure relates to a hand-drawn figure recognition method, device and system, and a computer readable storage medium. The hand-drawn figure recognition method comprises: receiving an input of a hand-drawn figure, the input comprising a stroke set, the stroke set comprising at least one stroke, each stroke comprising a sequence of information of a plurality of track points, and the information of each track point comprising coordinates of the track point and writing time; determining whether the stroke set constitutes a closed figure according to coordinate data of the strokes of the input; and in the case where the stroke set constitutes a closed figure, identifying a category of the closed figure based on the coordinate data of the strokes.

The invention belongs to the field of education intelligence technology and pattern recognition, and discloses an auxiliary method and device for axisymmetric pattern recognition and symmetry axis detection. According to the method and the device, graphic images (non-natural images) drawn or shot by students are acquired through an image acquisition module, and after preprocessing such as binaryzation and denoising, features are extracted to be combined with a target detection method to carry out automatic identification of axisymmetric graphs on the images; and then the number, direction and symmetry type information of symmetry axes are extracted through a symmetry analysis module, and an identification result is compared with a standard teaching model so as to realize automatic determination and teaching feedback. The method and the device are composed of an image acquisition module, a target detection module, a symmetry analysis module and a result feedback module, and have a closed-loop structure of'detection-analysis-comparison-feedback '. Compared with the prior art, the method and the device have the advantages that the symmetry of the complex graph can be efficiently recognized in a mathematics teaching scene, the learning interactivity and the teaching efficiency are improved, and the method and the device are simple in structure, easy to implement and high in processing efficiency and have good education intelligence application prospects.

The invention relates to a form and position code and distance measurement method with an identification and positioning function, comprising a two-dimensional code, a central positioning point, a data check point and a graphic code, the central positioning point is located in the middle of the two-dimensional code, the data check point is located on the side of the two-dimensional code, when the two-dimensional code is scanned, the central positioning point is determined by acquiring the data check point, and the graphic code is scanned; the graphic code is in a preset geometrical shape, and the graphic code is arranged at a preset position on the outer side of the two-dimensional code. The form and position code can be used for the portable pattern recognition equipment to quickly obtain the actual distance from a target point location, and data or point location docking of the pattern recognition equipment in a system model is quickly realized after distance data and system data are exchanged through the data two-dimensional code. And rapid butt joint between model data or a digital twin system and an actual working condition is facilitated.

The invention provides a method and system for generating a SysML model to a two-dimensional CAD model, the generation method adopts a SysML internal structure diagram model to generate the two-dimensional CAD model, and the generation method comprises the following steps: S1, generating a two-dimensional CAD graph based on picture frame geometric information of an internal structure diagram of a system modeling language model; s2, identifying a structural element list, an associated port list and a connector list in the internal structural diagram; s3, structural element information is obtained, port geometric information is calculated, and a two-dimensional CAD graph of structural elements and ports is generated; and S4, obtaining geometric information of the connector, and calling an application programming interface to generate a two-dimensional CAD graph of the connector. According to the method, the two-dimensional CAD model can be automatically generated from the SysML internal structure diagram efficiently, accurately and flexibly, the method is suitable for the fields of nuclear engineering design and the like, the design efficiency can be effectively improved, the design quality is guaranteed, and the design flexibility is enhanced.

Systems and methods of detecting a vortex made by a travelling object is disclosed. Techniques include positioning a media collector to capture a visual media file of the vortex. In some configurations, a graphic recognition algorithm and vortex similarity engine are used to determine whether a visual media file captured by a media collector contains a vortex. In some configurations, a computer may trigger an alert if a travelling object vortex is not expected to be in the visual media file.

The invention provides a visual monitoring method, device and system for mold processing and a storage medium, and relates to the technical field of industrial software. The method comprises the steps of obtaining line segment coordinate information in a mold graph; performing closed pattern recognition according to the line segment coordinate information to obtain a heating area and a corresponding heating area coordinate array; mapping the heating areas and the heating area coordinate array to a graphical interface, drawing a graph of the heating areas in the graphical interface according to the heating area coordinate array, and assigning a corresponding temperature interface for each heating area; acquiring temperature data of each heating area according to the temperature interface; according to the temperature data of each heating area, the color of the graph of each heating area is dynamically rendered in the graphical interface, so that the problem that an existing electric heating system cannot directly import and recognize the mold graph can be solved, and the temperature monitoring efficiency is improved.

The invention provides a pattern recognition method and system. The method comprises the steps of obtaining a geometric pattern drawn by a user; extracting a feature descriptor of the geometric figure from each primitive in the geometric figure; determining the contour confidence of each primitive in the geometric figure according to the position feature of each primitive in the geometric figure and the connection relationship between the primitives, and further determining geometric constraint conditions between the primitives in the geometric figure; obtaining a plurality of example graphs, and determining a shape matching operator between each example graph and the geometric graph according to each matching feature point in each example graph and the shape similarity between each example graph and the geometric graph; and further determining the recognition contribution degree of each primitive contour in the geometric figure, and performing contour completion on the geometric figure based on the recognition contribution degree of each primitive contour. By adopting the scheme provided by the invention, the non-linear contour of the hand-drawn graph can be adaptively complemented in the graph recognition process.

The invention provides an intelligent agricultural robot pattern recognition method and an obstacle avoidance system, relates to the technical field of image pattern recognition, and aims at effectively inhibiting water drop highlight by introducing polarization imaging, preventing optical artifacts from being misjudged as diseases and remarkably improving the recognition accuracy. For a low-confidence-coefficient target, an original micro-area thermal excitation physical review mechanism can confirm the real physical attribute of the low-confidence-coefficient target on line, and the problem of recognition uncertainty is solved; besides, based on a dynamic risk volume constructed based on multi-dimensional information, and in combination with adaptive speed control of path risk look-ahead, differential intelligent obstacle avoidance of rigid and flexible obstacles is realized. According to the method, safety is ensured, and meanwhile, the passing efficiency and smoothness of the robot in a complex environment are greatly improved.

The application discloses an intelligent control system and method for glue line image-text recognition and automatic waste disposal, and relates to the technical field of computer vision. The system comprises an image acquisition module, an image preprocessing module, a glue line area positioning module, an image-text recognition module, a defect detection module, an intelligent decision module and a waste disposal execution module. The image acquisition module is used for acquiring image information of a printed matter to be detected. The image preprocessing module is used for carrying out denoising and enhancement processing on the acquired image. The glue line area positioning module is used for identifying and positioning a region where the glue line is located. The image-text recognition module is used for recognizing image-text information in the glue line region. The defect detection module is used for detecting printing defects in the glue line region. The intelligent decision module is used for generating a waste disposal decision according to the recognition result and the defect detection result. The waste disposal execution module is used for executing an automatic waste disposal operation according to the waste disposal decision.