436 results about "Template matching" patented technology

The invention discloses a document element extraction method based on an AI large model technology. The method comprises the following steps that various receipts input by a user are received; carrying out analysis and layout analysis on the document through a visual language model or a text analysis technology to generate a unified intermediate representation; a dynamic grading module is adopted to evaluate document complexity from three dimensions of format, structure and variation, and the document complexity is divided into a simple regular type, a structure complex type or a height variation type; adaptively selecting a processing flow according to a rating result, wherein the processing flow comprises high-speed template matching, multi-modal cognitive fusion or intelligent agent driving processing; the processing result is converted into an EasyEX format which is easy to process; the method comprises the following steps of: converting a natural language demand into an execution rule by extracting an Agent and utilizing a Prompt dynamic compiling technology, and positioning and extracting a target field by LLM (Logical Language Model); and finally, outputting structured data after type verification, knowledge graph verification and compliance review. According to the invention, the universality, the accuracy and the automation degree of receipt processing are improved, and the labor cost is greatly reduced.

Disclosed herein are a method, an apparatus and a storage medium for image encoding / decoding. In typical image encoding / decoding methods, a decoder-side motion information derivation method may be limitedly used. Therefore, the improvement of encoding efficiency attributable to the decoder-side motion information derivation method may also be limited. In embodiments, a motion information search method used in an inter-prediction mode, an intra block copy mode and an intra template matching prediction mode is disclosed. With the use of various motion search methods, encoding efficiency may be improved.

Disclosed herein are a method, an apparatus and a storage medium for image encoding / decoding. Methods for performing more accurate prediction are disclosed when performing prediction using a block vector. A block vector for a target block is derived by template matching, and a prediction block for the target block is determined based on the derived block vector. Various and detailed embodiments for a plurality of pieces of information used to derive a block vector, such as a search area, a block vector candidate list, a reference region, a buffer, and a tree type, are provided.

The invention discloses an AI-based archive digitization full-process management and control method, which comprises the following steps: acquiring archive image data and text data, constructing an original archive data set, and generating an initial directory set; constructing an archive directory template set, executing template matching and structure adaptation, and generating an initial structure recognition result; setting a classification rule of directory deviation and structural contradiction, constructing a deviation rule base, executing rule scanning and node comparison, and generating a deviation contradiction label sequence; constructing an evolution state vector, inputting the evolution state vector into an improved GMN model, and generating a structure repair state vector through a structure guiding unit, a deviation sensing unit, a path fusion unit and a node evolution unit in sequence; and executing result updating and conflict rearrangement, completing standard template alignment and semantic coverage verification, and generating a final structure recognition result. According to the method, the structural integrity, the semantic consistency and the automatic repair capability of directory recognition are improved, and the method has relatively high adaptability and intelligent level.

The invention relates to the technical field of industrial vision multispectral imaging detection, in particular to a defect detection method and system based on multispectral fusion imaging, and the method comprises the steps: synchronously collecting a multi-channel spectral image of the surface of an object to be detected, and generating an enhanced feature map fusing multispectral information; identifying and preliminarily marking a suspected defect region by applying a region segmentation algorithm based on anomaly detection; extracting a multi-dimensional spectral response curve of each region in each original spectral channel, and constructing a spectral feature vector; matching the vector with a standard template of a pre-established defect spectral feature database, and classifying and confirming defect types; and carrying out contour refined analysis, calculating the geometric dimension and position of the defect, and generating a structured detection report. According to the method, the sensitivity of defect detection and the classification accuracy are improved through accurate matching of multispectral fusion and spectral features, and automatic and high-precision defect identification is realized.

The invention relates to the field of special equipment safety management, and discloses a special equipment intelligent control method and system based on the Internet of Things. The method comprises the following steps: acquiring a facial image and fingerprint data of an operator through an industrial Internet of Things sensor, and matching by combining a preset biological recognition template to obtain preliminary verification information; personnel qualification grades and historical operation records are called from a database, and a comprehensive qualification score is determined through random forest algorithm processing; acquiring temperature and pressure parameters of the equipment in real time, and marking abnormity to generate a state label if the parameters exceed threshold values after detection; based on the state tag and the qualification score, verifying identity authenticity in a multi-dimensional manner, matching equipment operation qualification requirements, generating a matching degree, generating a temporary permission token when the matching degree reaches the standard, and otherwise, refusing to access; and the token is encrypted and transmitted to the equipment control module, and the operation range is adjusted in real time to complete dynamic permission configuration. According to the method, accurate authority control is realized, the operation safety and management efficiency of the special equipment are improved, and guarantee is provided for safe operation of industrial production.

The invention discloses an intelligent grabbing system anomaly detection and processing method and device based on 3D vision, and belongs to the technical field of industrial robot 3D vision guide grabbing. The method comprises the steps of collecting 3D point cloud data of stacked flexible plates; carrying out point cloud preprocessing and template matching, and determining a plate pose and a grabbing point; generating a cushion block detection area and a deformation detection area based on the grabbed point pose; counting the number of point clouds in the detection area, comparing the number with a threshold value, and judging whether cushion block residues or plate excessive deformation exists or not; if yes, triggering processing, and if not, outputting a grabbing instruction. The device comprises a truss manipulator, an end effector, a 3D visual sensor, a visual processing unit and a robot control unit. According to the method, the detection area is dynamically generated, point cloud threshold judgment is fused, self-adaptive detection and processing of excessive deformation of the cushion block and the plate are achieved, the robustness, safety and automation level of a grabbing system are remarkably improved, and the method is suitable for automatic unstacking operation of stacked flexible plates.

The invention relates to the field of electronic design automation, provides a method for semi-automatically processing an EDA (Electronic Design Automation) project file and generating a YOLO format annotation data set, and aims to solve the technical problems of low efficiency and poor precision of the traditional pure manual data annotation data set. According to the main scheme, the method comprises the steps of reading an engineering file of a jia-creative EDA project, analyzing and extracting element information, generating a standardized CSV data format, converting the standardized CSV data format into a label in a YOLO format, calculating normalized bounding box coordinates of elements, and recognizing special original elements and supplementing labeling information in combination with a template matching technology; drawing a bounding box on the schematic diagram image according to a YOLO label, highlighting different element types by using different colors, and generating a visual annotation image; revising and combining the newly generated YOLO label and a preliminary YOLO label generated by the AI model to obtain a label set; then importing into Labelimg for manual correction, and generating a final label set; and training a target detection model by using the final label set, and improving the pre-labeling precision through data set expansion iteration.

The invention relates to the technical field of image recognition, in particular to an intelligent solid waste recognition system based on multi-modal fusion, which comprises a data acquisition module, a visual guidance module, a recognition module and a cooperative processing module, and is characterized in that the system quickly analyzes surface information such as visible light images, three-dimensional point clouds and infrared spectrums of materials through the visual guidance module; and coding to generate a dynamic vector for guiding a subsequent refined analysis task. The identification module constructs a two-dimensional pixel map on the internal density map according to the guidance of the dynamic vectors, and utilizes a map neural network to deeply analyze local density textures so as to identify high-value components such as an internal circuit board; and the co-processing module performs template matching of the internal components and external surface anomaly detection according to the instruction so as to assess the security risk. According to the method, the external form and internal structure information of the material are deeply fused through the strategy of coarse first, fine second and dynamic guidance, and intelligent recognition of the complex electronic waste is achieved.

The invention relates to the technical field of intelligent safety monitoring, and discloses a fire hazard identification method and system based on a multi-modal large model. According to the method, standardized preprocessing is carried out on field multi-modal data through a unified timestamp system and a quality evaluation mechanism; vision, text and sensor features are extracted and aligned by using a pre-trained deep learning model, adaptive feature fusion is realized by using an attention mechanism of scene perception, hidden danger recognition and confidence evaluation are completed through a hierarchical classification network and an uncertainty quantification method, and structured rectification suggestions are generated based on template matching and multi-target optimization. And progressive model updating of experience playback and collaborative management of RESTful API (Application Program Interface) are supported. According to the invention, the accuracy and efficiency of fire hazard identification can be effectively improved, and intelligent decision support is provided for safety management.

The invention discloses a six-dimensional sensor multi-source data fusion system based on dynamic weight, relates to the technical field of sensor data processing, and is used for improving fusion precision and robustness of measured data of a multi-source sensor. Firstly, a working condition modal identification module analyzes time-frequency characteristics output by a six-dimensional sensor, matches the time-frequency characteristics with a predefined working condition template, and identifies a current dominant working condition modal; then, a credible domain dynamic mapping module counts residual mean values and variances of single-point output and global fusion estimation values in a sliding time window, and adaptively adjusts a standard deviation multiple in combination with an instantaneous signal-to-noise ratio to form a dynamic credible domain boundary; the closed-loop weight optimization module distributes weights according to the relative positions of the data points and the credible domain boundary, punishes the abnormal measurement data points and generates a dynamic weight matrix; and finally, the fusion execution module performs federated filtering fusion by using the dynamic weight matrix, outputs global optimal estimation feedback to update residual statistics, and realizes multi-source data fusion closed-loop optimization.

The invention relates to the technical field of image analysis, in particular to a traditional Chinese medicine acupuncture positioning method and system based on image processing, and the method comprises the following steps: collecting a posture image, analyzing the deviation and motion state of an included angle, screening a bone segment with a stable structure, mapping acupuncture point anchoring input, recognizing the sudden change of a proportion, and adjusting the coordinates of the bone segment. And after affine errors are compared, graph mapping information is obtained, and acupoint path output annotation records are segmented. According to the method, by analyzing the posture deviation and the bone chain dynamic characteristics, the judgment capacity for the motion state is enhanced, the stable structure is screened through the bone segment included angle change trend, the acupoint anchoring precision is improved, the accuracy of bone segment structure fitting is optimized by combining the proportion trend and mutation point recognition, and the accuracy of bone segment structure fitting is improved by adopting bidirectional mapping error comparison and type classification. According to the method, the adaptability of graph coordinates and template matching is improved, continuous segmentation and structure recognition of path paragraphs are achieved through sliding window merging and label labeling, acupuncture point positioning is more coherent, and the structure is clear.

The invention discloses a visual duplicate removal method, device and equipment for overlapped targets, and relates to the technical field of automation control, and the method comprises the steps: collecting a scene image of a to-be-captured target, and recognizing a matching template and pose information of each target in the scene image based on template matching; constructing a reference polygon enveloping a target boundary according to the matching template; performing external expansion or internal shrinkage processing on the reference polygon based on a preset safe distance, and generating a safe region polygon in combination with the matching template; transforming the associated security area polygon into the same image coordinate system through the pose information of each target; and in the image coordinate system, judging whether any two targets have an overlapping region or not so as to carry out overlapping removal processing. Through accurate geometric description, preset safety distance and target matching and overlapping judgment, misjudgment and missing detection of target overlapping are remarkably reduced, and the grabbing collision risk caused by neglecting of the physical size of an actuator is avoided.

The invention relates to the technical field of machine vision and industrial quality control, and discloses a cigarette packet surface printing defect detection method and system based on vision intelligence. The method comprises the following steps: obtaining an initial image of the surface of a cigarette packet, and adaptively adjusting optical parameters to obtain a clear image; pixel-level features are extracted, gray level conversion is carried out, and a potential defect area boundary is positioned; identifying edge distortion features and classifying font deformation types; separating a target pixel block, matching the target pixel block with a standard template, and quantifying color deviation; carrying out ambiguity evaluation on the deviation overrun region, and determining a fuzzy position; extracting the sub-image blocks and performing cross validation in combination with the classification labels to generate a comprehensive defect description vector; and generating a real-time feedback signal in the high-speed production line according to the vector, dynamically adjusting imaging parameters, and realizing continuous and accurate defect identification. The method can effectively improve the detection precision and adaptability of the cigarette packet surface printing defects, and is suitable for real-time quality control in a complex industrial environment.

The invention discloses a home security prediction method and device, electronic equipment and a storage medium, and relates to the technical field of data processing. Visual and non-visual data can be fused to generate perceptual event features, and a dynamic causal knowledge graph including home entities, events and situational states is constructed; deep modeling of association between the overall state of the family and the event causality is realized; complex scene anomalies can be captured through causal chain template matching and anomaly marking, multiple groups of causal hypotheses are generated, an optimal result is screened, explainable early warning containing causal logic is generated in combination with risk scores, and the limitation of a traditional static rule or a single sensor is avoided, so that the method is suitable for popularization and application. The technical problem that an existing traditional scheme lacks deep modeling capability of family overall state, member behavior mode and causal association between events and is difficult to adapt to complex dynamic scenes can be solved. The technical effects of improving the adaptability of the home security system to complex dynamic scenes, improving the early warning accuracy and interpretability and enhancing the intelligent level of the system are achieved.

The invention discloses a dynamic image motion correction method based on a graph neural network, and the method comprises the following steps: a), constructing a target region trajectory tracking model based on the graph neural network, and achieving the high-precision motion trajectory modeling; b) analyzing an image displacement mode in real time through a dynamic discrimination algorithm; and c) realizing adaptive image motion correction based on the target area track features. According to the method, the problem of complex motion trail modeling limitation caused by dependence on fixed template matching in a traditional method is innovatively solved, and the problem of spatial-temporal characteristic aliasing caused by unsteady state deformation of nervous tissues is effectively solved. According to the technical scheme, the dependence on a hardware synchronization signal acquisition module is eliminated, the resource configuration requirement of the edge computing equipment is remarkably reduced, and meanwhile, the multi-scale time sequence integration efficiency is improved. According to the method, dynamic imaging reconstruction of subcellular neural activities can be realized, and dynamic change details in a target neuron issuing process can be accurately restored.

The invention relates to the technical field of action recognition, in particular to an AI-based motion posture recognition system, which comprises a key point extraction module, a graph structure construction module, a node trajectory modeling module, an action segmentation analysis module and a posture recognition output module. According to the method, the spatial position of each node of the human body in the continuous image under the time sequence structure is extracted, and the graph structure sequence based on the topological relation between the nodes and the motion trend is established, so that the dynamic modeling and the structure expression of the skeleton motion can be realized, and the trajectory characteristics of node position change, speed fluctuation, direction change and the like are analyzed; according to the method, key paragraphs with inversion and abnormity in actions are effectively identified, action segmentation classification is carried out in combination with frame sequence spacing and key node activity states, accurate analysis of complex action processes is realized under the condition of not depending on external hardware, and the accuracy and timeliness of posture identification are improved in a mode of matching with a standard template.

An image encoding / decoding method for template matching handling, a method of transmitting a bitstream and a recording medium storing a bitstream are provided. An image decoding method performed by an image decoding apparatus may comprise determining a prediction mode of a current block, generating a prediction block of the current block based on the prediction mode, and reconstructing the current block based on the prediction block. The prediction mode may be determined based on template matching-based coding tool enabling information.

The invention relates to the technical field of computer vision, and discloses a catering consumption automatic settlement method based on image recognition, and the method comprises the steps: obtaining multi-scale image data through a multi-level image quality evaluation and self-adaptive preprocessing technology; target detection and instance segmentation are realized by adopting hierarchical feature extraction and dish template matching; carrying out dish three-dimensional measurement and volume calculation based on a stereoscopic vision and shape recovery method; realizing classification and identification of dishes through multi-modal feature fusion and an attention mechanism; performing nutrient component analysis and health assessment based on nutrition database matching and image feature analysis; and intelligent settlement is realized through price calculation, nutrition statistics, intelligent recommendation and settlement processing. According to the invention, dish identification, nutrition analysis and dynamic pricing can be rapidly completed, and a complete technical solution is provided for intelligent catering service.

The invention relates to a robot gear disc loading method based on 3D vision, and belongs to the technical field of industrial robot automation. The method comprises the steps that connection between a 3D camera and a robot is established, hand-eye calibration is completed, a template library of a gear type circular workpiece and a template library of a material disc type non-circular workpiece are established respectively, template matching pose output is carried out, and the robot gear disc loading precision is improved; outputting a high-precision pose matrix of the workpiece; and the grabbing points are optimally sorted according to the running direction of the robot, the placing points of the gears are sorted according to the preset sequence of the teaching points of the robot, and regular visual correction is executed to maintain the long-term precision of the system. The problems of low efficiency, error proneness, poor flexibility and the like of traditional manual tray loading are solved, high precision, high rhythm and full automation of multi-model gear tray loading operation are achieved, and the intelligent level and stability of a production line are remarkably improved.

Embodiments of the present disclosure provide a solution for video processing. A method for video processing is proposed. The method comprises: determining, for a conversion between a video unit of a video and a bitstream of the video, a candidate list for the video unit based on one or more non-adjacent video units of the video unit, wherein the candidate list is an intra block copy (IBC) candidate list or an intra template matching prediction (IntraTMP) candidate list, wherein an order positions of the one or more non-adjacent video units is predefined or based on coding information; and performing the conversion based on the candidate list.

Methods for video decoding and encoding, apparatuses and non-transitory computer-readable storage media thereof are provided. In one method for video decoding, a decoder may obtain a first and a second block, where the first block is coded with an Intra Block Copy (IBC) mode or an intra Template Matching Prediction (TMP) mode, and the second block is coded with the intra TMP mode or the IBC mode. Additionally, the decoder may obtain a boundary strength for a deblocking filter by applying a predefined criterion in one of an intra mode or an inter mode. Further, the decoder may apply, and based on the boundary strength, the deblocking filter on the first block and the second block.

Embodiments of the disclosure provide a solution for video processing. A method for video processing is proposed. The method includes: deriving, for a conversion between a video unit of a video and a bitstream of the video, a prediction or reconstruction of the video unit based on one of: one or more intra template matching prediction (intra TMP) candidates, one or more intra prediction modes (IPMs), one or more reordered intra TMP candidates, or one or more reordered IPMs, and wherein the video unit is applied with a fusion of intra template matching prediction (intra TMP) mode and a coding tool; and performing the conversion based on the prediction or reconstruction of the video unit.

The invention discloses an image edge matching degree calculation and inspection point position deviation correction control method and system, and innovatively adopts a semantic segmentation and multi-level feature fusion mechanism to solve the problems of low precision and poor robustness of traditional IMU coarse adjustment, single template matching and SLAM algorithms. The method comprises the following steps of: acquiring an image through a pan-tilt camera, performing de-noising processing, and calculating an initial translation offset by using an RANSAC algorithm; generating a mask matrix in combination with the pre-training model, and extracting the edge gradient direction and strength; region pairing is established based on semantic consistency, and the matching degree is calculated through Fourier descriptor conversion; the control quantity is reversely deduced through the pixel-holder coordinate mapping relation, image acquisition is executed recursively until the matching degree reaches a threshold value, and semantic-level high-precision automatic correction and intelligent control of the target area are achieved.

The invention discloses a multi-temporal optical remote sensing image robust registration method based on multi-scale joint similarity measurement, and the method comprises the steps: 1, inputting a dual-temporal image, and obtaining uniformly distributed control point pairs through SIFT extraction and interactive manual auditing; 2, constructing a global dense displacement field by using a thin-plate spline, completing coarse registration and partitioning according to an overlapping strategy; 3, performing three-stage coarse-to-fine template matching on each image block, inheriting an initial displacement value step by step, and realizing radiation difference and geometric deformation synchronous self-adaption by taking a product of normalized mutual information and a normalized cross correlation coefficient as joint similarity measurement; and 4, performing sub-pixel-level deformation correction on the reliable displacement field by using a local thin plate spline, and outputting a registration image and metadata. According to the method, a global sparse-local dense framework and a multi-scale progressive + joint measurement + quality constraint mechanism are coupled, and a high-robustness and high-precision registration solution is provided for quantitative remote sensing application such as change detection and disaster assessment.

The invention provides a non-uniform ion implantation process monitoring method based on template matching image recognition, and the method comprises the steps: 1, carrying out the to-be-monitored non-uniform ion implantation of a wafer, carrying out the modeling according to the measurement data of the wafer, and obtaining a template used for monitoring the dose / resistance distribution of the non-uniform ion implantation; step 2, carrying out resistance measurement on the batch of wafers after the non-uniform ion implantation, and drawing a resistance distribution diagram according to the coordinates and the resistance values of the measurement point positions; and 3, carrying out image identification comparison operation on the template obtained in the step 1 and the resistance distribution diagram obtained in the step 2 to obtain a similarity score, and carrying out statistical process control and control on the similarity score. Abnormalities such as deviation and rotation of dose distribution patterns of the non-uniform ion implantation process are effectively captured, and monitoring of the non-uniform ion implantation process is achieved.

The invention relates to the technical field of industrial automation and computer vision, and particularly discloses a non-intrusive equipment monitoring system design method based on optical character recognition (OCR). The method comprises the following steps: firstly, transmitting a video source to an industrial personal computer through a video encoder and a switch; secondly, establishing a virtual keyboard and mouse remote control channel through the development board and the keyboard and mouse simulator; a template matching mechanism is introduced, corresponding characteristic values and content recognition templates are established for different interfaces of the equipment, and then a PP-OCRV4 model is used for carrying out recognition processing on the image; and finally, establishing monitoring to monitor and control the equipment identification process. According to the invention, a modularized system building method is adopted, the function of each module is specifically developed, a monitoring system for automatically identifying the interface data of the storage equipment is reasonably constructed, and the system can ensure long-time high-efficiency operation, accurately identify the data of each interface and store the data.

The invention provides a pallet pose recognition method for an unmanned forklift, and the method comprises the steps: firstly obtaining depth point cloud data, and converting the depth point cloud data into 2D image data; then constructing a pallet coarse positioning model, processing the 2D image data through the pallet coarse positioning model, obtaining 2D bounding box data, and obtaining a pallet index matrix through the 2D bounding box data; then acquiring a plate instance point cloud based on the plate index matrix, and acquiring a left support leg point cloud, a middle support leg point cloud and a right support leg point cloud through a classification cutting strategy and the plate instance point cloud; according to the method, the 3D depth point cloud is converted into the 2D binary image, a complex 3D point cloud recognition problem is converted into a more efficient and mature 2D image target detection problem, and the template matching problem of a pure point cloud scheme is replaced by a lightweight neural network, so that the optimal balance between the speed and the precision is realized.

The invention provides a non-contact underwater image recognition method and system, and relates to the technical field of underwater image recognizing.A plurality of cameras are symmetrically arranged at the position, close to the tail end, of an underwater slender structure in the transverse flow direction and the longitudinal flow direction, and a baseline beacon is arranged at the tail end of the underwater slender structure, so that the coordinates of the tail end are obtained in real time; the method comprises the following steps: carrying out self-calibration on a camera, obtaining an internal reference matrix and a distortion coefficient, calculating an external reference matrix, accurately converting tail end coordinates into a local coordinate system, extracting texture features from an underwater slender structure image to generate a texture primitive template, dividing a cross-correlation grid in each frame of image, and carrying out template matching and similarity calculation to identify the contour of the underwater slender structure. And finally, the configuration parameters of the underwater slender structure are output through the fitted curve after interpolation, quantitative indexes are provided for configuration analysis and state evaluation, the adaptability and stability of the system in a complex environment are enhanced, and the service life is prolonged.

The disclosure provides a kind of recognition positioning method and system of colloidal gold particles in cryo-EM image, the scheme includes: obtaining the electron microscope image to be identified, and carries out corresponding pretreatment;Wavelet transform is handled to the image after pretreatment based on Atrous algorithm, obtains corresponding wavelet image;For the wavelet image obtained, in turn, binaryzation processing, noise point removal and clustering operation are carried out, obtain the corresponding several clusters of colloidal gold particles in binary image;From the several clusters, the cluster satisfying the preset constraint is screened out, and the average template is obtained based on the obtained cluster satisfying the preset constraint;Template matching is carried out using the average template and the several clusters respectively, and the cluster whose matching result is lower than the preset threshold is filtered out, and the remaining cluster is used as the candidate colloidal gold particle target;The position and size estimation of the candidate colloidal gold particle target obtained are obtained by corresponding the original image.