57 results about "Difference-map algorithm" patented technology

The invention discloses a government affair knowledge graph updating and version control method and system, and belongs to the technical field of artificial intelligence and knowledge graphs, Git-based file change drives incremental updating of knowledge graphs, and the method is implemented by the following steps: change monitoring: integrating Git version management; capturing file change through double modes of timing scanning and event triggering, establishing one-to-one mapping between a file Git version and a map version, and transmitting difference contents; difference preprocessing: standardizing difference contents into an analysis unit containing positions, types, original contents and updated contents, and filtering meaningless differences through a G-NF algorithm; constructing a difference subgraph; carrying out difference atlas fusion; and version control: storing version archives, and providing a multi-dimensional query tracing function. According to the method, version closed-loop management is achieved, change traceability is accurate and efficient, incremental atlas updating is achieved, total processing resource consumption is reduced, meanwhile, multi-scene version switching is supported, and repeated construction and maintenance cost is reduced.

The invention relates to the technical field of image recognition, in particular to an intelligent comparison recognition method and system for printed images, and the method comprises the steps: building a spatial position mapping relation according to a multi-dimensional feature set, and comparing various types of feature differences of corresponding positions to obtain a feature difference chart; according to a printing process parameter library, analyzing a characteristic offset rule of each difference area in the characteristic difference chart, and judging whether the difference is caused by printing pressure or ink supply fluctuation; and according to a historical defect case library, matching a difference mode of the feature difference chart, and identifying whether an abnormal difference caused by printing plate abrasion or paper deformation exists or not. According to the invention, through a mechanism of dynamically adjusting the feature matching weight, the feature similarity of the key area can be recalculated according to the cause labeling information detected in real time, so that the optimized feature difference graph is generated, the precision of the detection result is improved, timely feedback can be realized in the production process, and quality problem accumulation is prevented.

The invention provides a nameplate printing defect detection method and system based on image segmentation. The method comprises the following steps: acquiring a nameplate surface image and carrying out distortion correction and brightness homogenization preprocessing; generating a pixel-level segmentation mask by using the semantic segmentation network; template registration is carried out based on the segmentation result, and a deformation field vector and a registration difference chart are calculated; performing defect area extraction and type identification on the difference image; according to the method, deep learning semantic segmentation is adopted to realize pixel-level analysis of a nameplate printing area, deformation field registration is combined to accurately quantify overprinting offset, defect quantitative evaluation is realized through multi-dimensional scoring, the detection rate reaches 99% or above, and the detection time of a single piece is less than 0.5 second.

The invention discloses a cross-sensor remote sensing image unsupervised change detection method based on a structured graph auto-encoder, belongs to the field of computer vision and remote sensing image processing, and particularly relates to a cross-sensor remote sensing image unsupervised change detection method. The objective of the invention is to solve the problem of low cross-sensor remote sensing image change detection accuracy caused by difficulty in explicit modeling of semantic change and insufficient robustness to modal difference in an existing model. The method comprises the steps of 1, obtaining a source domain and a target domain; 2, obtaining a source domain graph structure and a target domain graph structure; 3, constructing a learnable structural difference tensor; 4, constructing a structured graph auto-encoder 1; 5, constructing a structured graph auto-encoder 2; 6, obtaining a trained structured graph auto-encoder 1 and a corresponding weight, offset and structural difference tensor; 7, obtaining a trained structured graph auto-encoder 2 and a corresponding weight, offset and structural difference tensor; and 8, obtaining a final difference chart.

A computer-implemented method can include generating a first knowledge graph from a first version of an application programming interface (API), generating a second knowledge graph from a second version of the API, identifying changes from the second knowledge graph to the first knowledge graph, and generating a difference graph based on the identified changes from the second knowledge graph to the first knowledge graph. The difference graph connects the second knowledge graph to the first knowledge graph via one or more revision edges, which represent the identified changes from the second knowledge graph to the first knowledge graph.

The invention discloses an SAR (Synthetic Aperture Radar) image change detection method combining convolution and mixed attention. The SAR image change detection method comprises the following implementation steps of: firstly, generating a difference chart for two SAR images by using a composite neighborhood intensity difference method; then, a hierarchical FCM clustering algorithm is used for carrying out pre-classification processing on the difference image, a pseudo label matrix is generated, variable and invariable high-probability sample pixels in pseudo label pixels are selected, spatial positions of the pixels are extracted, and on the pixels of the corresponding spatial positions of the two original SAR images, a pseudo label matrix is generated; pixel blocks with the pixel points as the centers are taken as a training set, and pixel blocks with all the pixel points as the centers are extracted from the two original SAR images to serve as a test set; and then training a neural network combining convolution and mixed attention by using the training sample set, and then carrying out change detection analysis on a test set by using the trained network to generate a final change detection result graph. The method has clear advantages in the aspects of SAR speckle noise suppression and change detection precision.

Aspects of the invention include systems and methods configured to extract enriched target-oriented common sense from grounded graphs to support efficient next step decision making of an autonomous agent. A non-limiting example computer-implemented method includes extracting common sense from a source. The extracted common sense can include a first knowledge graph. An environment state can be extracted from an observation. The extracted environment state can include a second knowledge graph. The second knowledge graph can include an interactive object and a state of the interactive object. A difference graph including the extracted common sense and the extracted environment state can be generated. A next action is selected based on the difference graph and the next action is taken by an autonomous agent.

To provide an inspection device that can more accurately inspect the appearance of an object. [Solution] An inspection apparatus according to one aspect of the present disclosure includes: an acquisition unit that acquires an image of an object; a difference calculation unit that generates a difference map based on a comparison image which is an image of the object stored in advance and the image; a learning unit that generates a learning model that outputs the degree of abnormality of the object shown in the image in response to the input of the image by performing machine learning using a plurality of the comparison images; an abnormality calculation unit that generates an abnormality map for the object using the learning model; a coupling unit that generates a combined abnormality map based on the difference map and the abnormality map; and a determination unit that determines the state of the object based on the feature portion included in the combined abnormality map.

The invention relates to the technical field of software testing, and discloses an AI-based test demand analysis and use case generation method and system.The method comprises the steps that a new and old version demand document set is obtained, a corresponding logic flow graph is constructed, a logic increment difference graph is generated through graph isomorphism matching, and an overall influence area is determined; obtaining a stock test case set and mapping the stock test case set into a stock case path set, and executing path geometric deformation and expansion after an affected path is identified to generate a final evolution path; and finally, calculating the comprehensive energy loss of the evolution path, judging the evolution state of the use case, obtaining a final test use case set and establishing a use case evolution pedigree table. According to the method, activation and evolution of stock test assets are realized, and the maintenance cost of government affair software regression test is reduced.

The application provides a large model-based automatic code generation and optimization method and system. Among them, the application generates an initial logical framework by a large model, forms a symbolic structure through symbolic processing, and generates a code intermediate representation that retains key semantics, and finally outputs source code. An index set is constructed using a vulnerability knowledge graph, a source code instruction sequence is scanned through parallel computing, and risk instruction positions and vulnerability details are marked by combining semantic similarity and path pattern matching. Based on vulnerability information, a historical case database is retrieved, a matching relationship is established, and an optimization strategy containing repair operations and constraints is generated. A semantic analysis model is loaded at the risk position, a difference graph of execution mode is constructed to generate features, a large model parameter is fine-tuned based on the constructed adversarial sample, and the newly generated code is rechecked for vulnerabilities. The application realizes intelligent generation from requirements to code, and completes the loop of vulnerability identification, repair strategy generation, large model dynamic optimization, and post-repair verification.

A SAR image change detection method based on a dual-stream hierarchical fusion engine network includes the following steps: A dual-stream hierarchical fusion encoder is constructed to collaboratively extract global and local features from SAR images at different time phases. The enhanced features are then used in a hierarchically aware U-shaped decoder to achieve hierarchical modeling and refined representation of the difference features. A frequency-domain channel attention mechanism with fused spatial weights is designed to enhance channel selectivity in the frequency domain and strengthen the response to change regions in the spatial dimension. A noise-resistant weighted loss function based on the difference map ablation coefficient is constructed to adaptively adjust the loss weights of each region, achieving noise region suppression and change region enhancement, effectively mitigating gradient bias caused by class imbalance and speckle noise. Training includes a dual-stream hierarchical fusion encoder and a U-shaped decoder network; SAR images from different time phases are input into the trained dual-stream hierarchical fusion engine network, which outputs a change detection binary map.

The application relates to a SAR image change detection method based on fusion difference maps and morphological reconstruction, which comprises the following steps: S1, acquiring two SAR images of the same region at different times; S2, obtaining a logarithmic mean ratio difference map and a logarithmic ratio difference map according to the two SAR images; S3, performing SLIC superpixel segmentation on the logarithmic ratio difference map to obtain a superpixel segmentation difference map; S4, performing wavelet fusion processing on the logarithmic mean ratio difference map and the superpixel segmentation difference map to obtain a wavelet fusion difference map; S5, performing morphological reconstruction on the wavelet fusion difference map to obtain a final difference map; and S6, processing the final difference map by using a fuzzy C-means clustering algorithm to output a detection result map. The method can better utilize the information of neighboring pixels, has excellent detection effects on different types of SAR images, has very good robustness to noise, and can maintain the details of the image.

This invention discloses a high-quality hybridflow image compression method at extremely low bit depths, comprising: processing the input image by a continuous coding branch to obtain continuous bitstream bits, and decoding loopbacks to generate continuous reconstructed latent variables; processing the input image by a discrete branch to generate vector-quantized continuous latent variables; calculating a cross-stream difference map based on the continuous reconstructed latent variables and the vector-quantized continuous latent variables; performing masking decisions based on the cross-stream difference map to generate a final mask and transmission strategy map; organizing and encoding hierarchical syntax based on the final mask and transmission strategy map to obtain mask bits and index-related bits; and packetizing the continuous bitstream bits, mask bits, and index-related bits to output a bitstream. This invention solves the problems of inaccurate masking decisions and low indexing coding efficiency, achieving high fidelity and high perceptual quality at extremely low bit depths.

The application discloses a kind of for low-illumination wide field of view video image significant change detection method and device, method includes: using logarithm ratio operator and subtraction operator respectively obtains global difference map and carries out fusion;Using frequency domain attention mechanism obtains the significant change area of global difference map;Combining morphological filtering, and energy feature and density feature are handled to noise, and the local change area pair where target is located is extracted;Using logarithm ratio operator extracts the difference feature of local change area pair, obtains local change difference map;Weighted fusion is used to carry out feature optimization to local change difference map, obtains local feature fusion map;Local feature fusion map is classified using k-means clustering algorithm to generate corresponding local change map, and all local change map is integrated into global change map, obtains the final change detection result.The device includes: processor and memory.

The invention discloses a multi-dimensional fairness automatic analysis method and system for a large image model, and the method comprises the steps: extracting sensitive attribute tags of an input image sample, and carrying out the classification according to sensitive attributes; carrying out attribute change processing on the input image sample based on the sensitive attribute type; extracting an output representation of the image pair after the image pair passes through the image large model, and calculating an output change index before and after attribute change processing of the input image sample according to the output representation; judging whether the output change index before and after the attribute change processing is higher than a first preset threshold value, if so, further calculating a fairness index quantification score, and comparing the image activation graphs of the input image sample and the attribute change image sample to obtain a difference graph when the score is higher than a second preset threshold value; and positioning a specific semantic region generated by model deviation according to the difference chart. According to the invention, a structured and automatic evaluation process can be provided, and the evaluation efficiency is improved.

The invention provides a luminous character defect detection method, which comprises the following steps of: after obtaining a difference chart of luminous characters, extracting each independent difference area in the difference chart, generating morphological structure elements with corresponding sizes and orientations based on a minimum enclosing rectangle of each independent difference area, and respectively adopting each morphological structure element to detect the defects of the luminous characters. Performing morphological closed operation on the difference image, extracting each independent difference region from the difference image after the morphological closed operation, and judging whether the defect is a pseudo defect or not based on the geometrical characteristics of each independent difference region; in this way, pseudo defects in the difference image are recognized and eliminated, the problem of defect false detection caused by glittering character edge burrs, registration errors and the like is solved, and the detection accuracy is remarkably improved.

The invention provides a digestive endoscopy decontamination quality evaluation method based on image recognition, and the method comprises the steps: constructing a shared and differentiated feature extraction double-flow convolutional neural network through the double-flow input of original and simulated degraded images, and fusing multi-scale features; a LINGAM causal discovery and gradient mask mechanism is introduced, a causal relationship between an image quality factor and residual judgment is dynamically modeled, and implicit causal graph optimization is realized; a causal intervention feature reweighting and anti-fact attribution mechanism is adopted to correct the feature weight of the low-quality region and improve the robustness of the model; according to the method, multi-task joint loss and progressive causal freezing training are carried out, so that the model has precision and interpretability, an image quality factor, residual judgment and a causal difference graph are finally output, dynamic and multi-dimensional quality closed-loop management and auxiliary decision making in a clinical scene are realized, and the analysis reliability of an endoscopic image in a complex environment is effectively improved.

The application relates to a pedestrian anomaly detection method and device and a storage medium, wherein the method comprises the following steps: inputting a first modality image and a second modality image into a target detection model to respectively extract a first modality feature map and a second modality feature map; calculating the difference between the first modality feature map and the second modality feature map via a differential attention mechanism module in the target detection model to generate a feature difference map, and calculating an attention weight based on the feature difference map; fusing the first modality feature map and the second modality feature map based on the attention weight via a feature fusion module in the target detection model to obtain a fused feature map; inputting the fused feature map into a detection head in the target detection model, and outputting a predicted pedestrian position and a predicted pedestrian staying state; and generating a pedestrian anomaly detection result based on the predicted pedestrian position and the predicted pedestrian staying state. Through the application, the problem of low accuracy of pedestrian anomaly detection is solved.

The application discloses a kind of based on optimization's guiding filter image fusion change detection method and device, method includes: by extreme minimum scale difference operator, pixel value difference value is to the picture of same scene different phase, obtains difference map;Using the way of guiding filter, difference map is fused on global scale, to obtain the optimal difference map;Mean filter is used to phase map I1 and I2, and the base layer difference map corresponding to each difference map is obtained, and the optimized brightness saliency map F1 and F2 are obtained by pca fusion as the image of generating saliency map, according to the principle of maximum saliency to determine weight map;Weight map is regularized for double-scale difference map reconstruction, and the final difference map is obtained;In clustering segmentation stage, by introducing soft threshold function, the final difference map is further processed, to suppress existing abnormal noise.The device includes: processor and memory.

The invention relates to the technical field of education management, and discloses a learning cognitive path planning system based on a cognitive map. Comprising a cognitive map construction module for recognizing subject knowledge points, extracting a dependency relationship and constructing a cognitive map; the evaluation portrait construction module is used for constructing a learning evaluation portrait; the difference atlas drawing module is used for identifying a difference atlas in the cognitive atlas; the path calculation and screening module is used for calculating the path value of the feasible path and screening out a learning cognitive path; according to the method, the cognitive dynamic change condition of the learner can be regularly monitored and analyzed, the continuous cognitive updating effect of the learner along the timeline is realized, and the limitation and hysteresis existing when a learning evaluation portrait is constructed by single-dimensional static data are avoided; and the phenomenon that part of associated subject knowledge points are missed and lost due to direct comparison of the real-time cognitive level and the planned cognitive level is avoided, and the planning accuracy of the learning cognitive path is improved.

The application discloses a kind of unsupervised synthetic aperture radar image change detection methods based on deep feature map, its implementation steps are: first, generate mean ratio difference map.Then using fuzzy C-means clustering algorithm, difference map is processed by three classification, and the initial result map containing change area, non-change area and uncertain area is generated, and in the change area and non-change area of initial result map, pixel is selected as pseudo-label respectively;With pseudo-label as center, image block is divided on two time SAR image, and training sample set is formed.Next, the training sample set is trained to convolutional neural network model, and after training, change detection analysis is carried out, and the prediction result map containing change and non-change category probability is obtained.Again, construct deep feature map energy function, and use minimum cut algorithm to optimize it, to generate the final change result map.The application has clear advantage in noise resistance and detection precision.

The invention discloses a protection action curve visualization engine based on an IED (Intelligent Electronic Device) model difference atlas, which relates to the technical field of relay protection teaching, training and testing of a power system and comprises an IED model difference atlas construction module and a WebGL (Web Graphics Library) visualization engine module. According to the technical scheme, data are analyzed and a JSON equation is constructed based on a to-be-processed SCD file through an IED model difference atlas construction module, a boundary gradient vector is calculated based on the JSON equation to obtain a difference degree matrix, and a protection action curved surface is constructed and rendered in a unified parameter space based on the difference degree matrix through a WebGL visualization engine module. And a visual result of the protection action curve is displayed, so that students can understand protection action conditions and critical regions more visually, and the teaching efficiency and the practical operation conversion rate are remarkably improved.

The invention relates to the technical field of machine vision and image processing, and discloses a plastic tray appearance defect automatic detection method based on machine vision. In the plastic tray conveying process, tray surface images are collected in real time, a gray level image matrix is constructed, smooth images are obtained through filtering under different smooth scales, local binary codes are generated through a fixed direction neighborhood comparison center and a neighborhood gray level, a square window is established at the position away from the boundary, and a local texture energy diagram is obtained through summation of codes squares. The method comprises the following steps: calculating a difference value among energy diagrams of different scales according to pixels, counting an average value and a standard deviation to construct a global threshold value, carrying out binary segmentation on a difference chart, screening defect regions according to eight neighborhoods and areas, marking pixels of the defect regions, and outputting geometric positions and statistical information, thereby weakening the influence of tray movement and illumination change, and improving the accuracy of the detection result. Noise suppression and texture reservation are considered, the sensitivity of a traditional edge and fixed threshold method is reduced, and automatic defect detection and quantification are achieved.

The present application relates to a kind of target detection method and system based on cosine perception adaptive knowledge distillation, the method constructs teacher-student distillation framework, and constructs the target detection network model including benchmark detector, adaptive context cosine difference fusion module, cosine perception adaptive space-channel feature distillation module and dynamic cosine perception mask prediction distillation module;Cosine difference map is calculated on the multi-scale feature layer of teacher-student model by difference fusion module;Student model is distilled by feature distillation module;Student model is distilled by prediction distillation module;Combined with feature distillation loss and prediction distillation loss, student model is jointly optimized, and the lightweight high-performance target detection model is obtained;Target detection network model is trained and used for target detection, to obtain detection result.The present application guides distillation with the cosine perception information of context, to improve the accuracy and robustness of knowledge distillation target detection method.

The application relates to a sticker effect detection method and device, computer equipment and a storage medium. The method comprises the following steps: obtaining a to-be-detected image obtained by adding an edited sticker in a target image, and obtaining an editing parameter under at least one editing item corresponding to the sticker; performing pixel point comparison on the target image and the to-be-detected image; determining a difference map corresponding to a difference pixel point in the to-be-detected image; performing editing parameter screening on each editing item based on the difference map to obtain a screening result; the editing parameter screening is screening a target editing parameter from a candidate editing parameter under the corresponding editing item, so that the original sticker is matched with the difference map after being edited according to the corresponding target editing parameter; when the screening result is that the target editing parameter exists under each editing item, the target editing parameter is compared with the corresponding editing parameter to obtain a sticker effect detection result. Based on the above method, the sticker effect of the to-be-detected image can be quickly and accurately detected.

The application discloses a wide field of view video image change detection method and device, and the method comprises the following steps: using an improved adaptive fast guided filter for video image change detection; after filtering the multi-temporal video image, a logarithmic ratio operator considering neighborhood information is proposed, the mean ratio difference graph generation mode is improved, and a difference graph is generated; the improved MR image and the improved LR image are subjected to image fusion through discrete wavelet transform, and a fused difference graph is obtained; a soft threshold function is used to perform initial classification on the fused difference graph, and an initial change area and an unchanged area are obtained; a cumulative distribution function is used to compress the pixel value of the classification result from [0, 255] to [0, 1]; a super-fast robust constraint fuzzy C-Means clustering algorithm is proposed, and an improved adaptive median filter is used for denoising processing. The device comprises a processor and a memory.

This invention belongs to the field of computer vision and remote sensing image processing technology, and relates to a method for training a road change detection model based on region supervision and a SAM model. The method includes: performing feature difference analysis on dual-temporal remote sensing images to obtain multi-scale difference features; extracting local and global attention features based on the multi-scale difference features and fusing them to generate attention pseudo-labels; inputting the difference maps and region labels corresponding to the dual-temporal remote sensing images into the SAM model to generate SAM segmentation maps; performing dual-path threshold judgment based on the attention pseudo-labels and the SAM segmentation maps to obtain adaptive pseudo-labels; inputting the dual-temporal remote sensing images into the change detection model to generate road change prediction results, and optimizing the change detection model based on the loss value generated by the adaptive pseudo-labels and the road change prediction results. This invention effectively improves the detection accuracy of the traffic road change detection model and gives it strong generalization ability, while reducing dependence on labeled data and improving detection efficiency.

This invention discloses an automated detection method and system for heterogeneous remote sensing images (optical and SAR). First, optical and SAR images from different time phases are preprocessed. Then, a heterogeneous change detection model based on a pseudo-twin channel feature extraction encoder is constructed, and multi-level features of the optical and SAR images are encoded in parallel. A semantically knowledge-guided multi-path feature enhancement module strengthens both shallow and deep features. Subsequently, a progressive fusion decoder is used to fuse the multi-scale features output from the encoding stage step by step. A difference map refinement module fully mines the complementary information and differences between the optical and SAR images, accurately locating and identifying change areas, and generating the final change detection result. This invention effectively improves the accuracy, recall, and overall accuracy of heterogeneous remote sensing image change detection, and exhibits good robustness and scalability.

A computer-implemented method for comparing digital 3D dental models is disclosed, the method comprising: receiving a first digital 3D dental model representing a dental condition at a first time; receiving a second digital 3D dental model representing the dental condition at a second time, the second time being later than the first time. Further, the method comprises generating a difference map based on the first and second digital 3D dental models, wherein generating the difference map comprises: obtaining values of geometric differences between the first and second digital 3D dental models; identifying a maximum value of the geometric differences from the values of the geometric differences; generating a color scale comprising a plurality of discrete colors associated with the values of the geometric differences, wherein colors in the plurality of discrete colors are separated by a color scale threshold, and wherein the color scale threshold is generated based on the maximum value of the geometric differences; and assigning the plurality of discrete colors to the values of the geometric differences. The method further comprises displaying the difference map to visually highlight the values of the geometric differences between the first and second digital 3D dental models.

The application discloses a high-quality change detection method and device based on difference feature iterative enhancement, comprising the following steps: determining the influence of feature difference quality on change detection results by noise simulation interference; extracting multi-scale convolution features by using U-Net; constructing an IDET module to generate difference information from two input multi-scale convolution features, and iteratively refining to obtain high-quality feature difference maps of each scale; inputting the high-quality feature difference maps of each scale into a coarse-to-fine strategy constructed by multiple convolution modules to generate change maps of each scale, and then fusing the change maps of each scale to generate a final change map. The device comprises a processor and a memory. The application optimizes feature differences, suppresses invariant regions, and obtains high-quality change maps.