43 results about "Graphic recognition" patented technology

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 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 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 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.

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 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.

This invention provides a drawing recognition method and a drawing recognition device that can accurately extract symbols and annotations from drawings and associate them with each other to create data. [Solution] A drawing recognition method for extracting symbols and annotations contained in a drawing file, comprising: a drawing file formation step of forming a symbol extraction drawing file S2 from an original drawing file S1; a symbol extraction step of extracting symbols from the symbol extraction drawing file S2; an annotation search area setting step of setting an annotation search area SA centered on each of the multiple symbols extracted in the symbol extraction step; and an annotation extraction step of extracting annotations corresponding to each symbol from the figures detected in the annotation search area SA centered on each of the symbols extracted in the annotation search area setting step.

The application discloses an AI-P&ID graph recognition data labeling method, relates to the technical field of graph labeling, and is used for solving the problem that the integrity of pipeline topology is destroyed. A plurality of remodeling areas are obtained by remodeling the areas of a P&ID graph to be measured, and local line widths of the areas are generated. Width compression or expansion compensation is performed on the line of the remodeling area according to the local line width. The preliminary center line is obtained by center line prediction on the processed image, and the skeleton node information is extracted. The local main direction and the adjacent interval length of the skeleton node in each remodeling area are detected. The cross direction difference degree is calculated based on the local main direction, and the skeleton node density is generated according to the adjacent interval length. The connection state of the skeleton node is judged by comprehensively considering the cross direction difference degree and the skeleton node density. In the broken state, the gap backfilling is performed, and the pseudo connection point removal or semantic labeling association is selected according to the number of skeleton nodes in the area, so that the skeleton breakage, the pseudo connection point and the node drift are avoided.

The present disclosure relates to a pattern type recognition method and device, electronic equipment and readable storage medium. The method comprises: obtaining an initial image to be recognized, the initial image comprising a pattern to be recognized; obtaining a feature vector of the pattern to be recognized in the initial image based on a preset pattern vector extraction model; obtaining a similarity between the feature vector and each candidate feature vector in a preset database to obtain at least one target candidate feature vector; and determining a type of the pattern to be recognized according to a pattern type of the at least one target candidate feature vector. In this embodiment, the problem that an existing pattern recognition model needs to be retrained for recognition when the pattern to be recognized is a new type can be solved without retraining the model when the pattern to be recognized is a new type, which is beneficial to improving the use efficiency of pattern type recognition.

The invention discloses an AI behavior abnormity capturing and alarming method for a panoramic video, relates to the field of graph recognition processing, and aims at performing one-time cutting on a panoramic video frame, dividing a picture into regions such as personnel, a tower, a line and a background, eliminating redundant information and realizing efficient separation of a multi-target region. And secondly, under the secondary cutting mask, performing secondary cutting on the personnel area, and subdividing the personnel area into functional sub-areas such as a head part, a body bone part, a hand part and the like according to safety management requirements, so as to provide structured input for subsequent targeted recognition. And finally, proposing abnormal behavior partition parallel identification based on a lightweight model, respectively outputting the abnormal probability of each part through a plurality of parallel region abnormal behavior identification models, immediately triggering an alarm when the probability exceeds a threshold value, and intercepting a video clip in real time for storage.

The application discloses a kind of based on virtual simulation graphics processing engine development method and system, it is related to computer graphics and virtual reality technical field, including acquisition three-dimensional point cloud data, convolution operation is carried out by convolution neural network algorithm, extract three-dimensional space feature matrix, construct pattern recognition model, the original data of the graphics to be processed is input to pattern recognition model, output graphics parameter set, in the matching virtual scene model in preestablished scene library, and the parameter of model is adjusted;According to the model after adjustment establishes physical coordinate system, by grid division algorithm the graphics to be processed is converted into grid unit, calculates the displacement data of grid unit;Read displacement data, generate and send graphics processing instruction sequence to graphics processor, by to grid unit superimposed texture information and ray tracing effect, output imaging result.The application establishes complete processing chain from data acquisition to rendering output, improves the automation level and accuracy of graphics processing.

The embodiment of the invention provides a visual sorting method which comprises the following steps: providing a raw material belt, acquiring material information of the raw material belt, and determining optical characteristics of the raw material belt according to the material information; a recognition pattern is arranged on the raw material belt, and the pattern feature of the recognition pattern is determined by the optical characteristic; the raw material belt is subjected to die cutting, so that the raw material belt forms a die cutting part, and the die cutting part is provided with an identification pattern; providing a visual module, and obtaining image data of the die cutting component through the visual module; collecting image data, judging the position of an identification graph in the image data, marking the image data to form a good product data set and a defective product data set, and expanding the defective product data set through a generative adversarial network; and constructing a deep learning model through the non-defective product data set and the defective product data set, and judging whether the die cutting part is qualified or not through the deep learning model. According to the visual sorting method provided by the embodiment of the invention, the visual detection accuracy of the die cutting part can be improved.

This invention discloses a data annotation method for AI-P&ID graphic recognition, belonging to the field of graphic annotation technology, to solve the problem of pipeline topology integrity being destroyed. It obtains multiple reshaped regions by reshaping the P&ID map under test and generates local linewidths for each region. Based on the local linewidths, it performs width compression or expansion compensation on the lines of the reshaped regions. It then predicts the centerline of the processed image to obtain a preliminary centerline and extracts skeleton node information. It detects the local principal direction and adjacent interval length of skeleton nodes in each reshaped region, calculates the cross-direction difference based on the local principal direction, and generates skeleton node density based on adjacent interval lengths. It combines the cross-direction difference and skeleton node density to determine the connection status of skeleton nodes. In the case of a broken state, it performs gap backfilling and selects to remove pseudo-connection points or semantically annotate and associate them based on the number of skeleton nodes in the region, avoiding skeleton breakage, pseudo-connection points, and node drift.

The invention discloses a multi-mode trigger type intelligent switch device, belongs to the field of intelligent homes, and solves the problem that an existing intelligent switch cannot meet the requirement for precise power control in a scene of'inconvenience in getting up, no control terminal and inconvenience in sounding '. The device comprises a switch panel with physical keys, a control device in the switch panel, an image recognition assembly in data connection with the control device, and at least one group of expansion control assemblies. The switch panel is provided with a five-hole power supply socket, and the control device is in independent switch association with the household appliances to be controlled. The image recognition assembly can adjust the collection visual field through a shielding cylinder or a multi-degree-of-freedom adjusting platform, and infrared supplementary light is arranged to adapt to the dark environment. The expansion control assembly comprises a human-in-the-eye sensor and can control the image recognition assembly to be started and stopped. The control device is internally provided with an operation habit learning module which is connected with the photosensitive sensor and can dynamically adjust a trigger response threshold value. The device achieves flexible operation through multi-mode triggering, is accurate in control, is suitable for existing household installation, and is high in practicability.

A tablet for extraction of a beverage, formed by cohesion of a dosed amount of a precursor substance in granular or powder form has a reference surface with one or more graphic recognition markings adapted to provide protection from abrasion of the precursor substance, and optionally a higher degree of cohesion, in a region where the one or more graphic recognition markings are formed compared to a region of the reference surface devoid of the graphic recognition markings. A method for producing a tablet for extraction of a beverage, the tablet being formed of a dosed amount of a precursor substance in granular or powder form, involves forming a tablet with self-supporting structure, and making graphic recognition markings on a reference surface of the tablet, adapted to provide protection from abrasion of the precursor substance in a region of the reference surface where the graphic recognition markings are formed.

The invention discloses an online identification and analysis method and system for engineering drawings and a storage medium, and belongs to the technical field of intelligent identification and calculation of the engineering drawings. A CAD plug-in is constructed, and the CAD plug-in is called to identify the format of the engineering drawings; analyzing the engineering drawing according to the format of the engineering drawing; based on a CAD-SCAN graph recognition engine, an image processing algorithm is adopted to detect and recognize components in the engineering drawing; traversing the recognized components, and for the components needing to be calculated, adopting a dynamic programming algorithm to carry out calculation, and keeping the rest components unchanged; and data analysis and structuring: analyzing all the components to obtain structured data so as to generate an engineering quantity list and component details. According to the method, online accurate identification of the engineering drawings is realized, meanwhile, efficient processing of the engineering drawings is supported, and the method has relatively good practicability.