19 results about "Difference-map algorithm" patented technology

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.

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 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 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 present application belongs to the technical field of image data processing, and discloses a projection picture adaptive fusion and splicing method and system, which comprises the following steps: acquiring preset calibration data and current frame overlapping images of an overlapping area of adjacent projectors, wherein the calibration data comprises a brightness distribution graph, a standard fusion weight graph and a normalized definition difference graph; performing texture analysis on the overlapping images to obtain image frequency characteristic values; combining the definition difference graph and the frequency characteristic values to determine a weight offset requirement value; applying an offset to the standard fusion weight under the constraint that the brightness variation does not exceed a preset tolerance, generating a dynamic fusion weight graph through spatial smoothing processing, and outputting the projection signals of the projectors according to the weighted rendering. According to the present application, the fusion weight is adaptively adjusted according to the image texture characteristics, the weight is biased towards the projector with higher definition in the high-frequency texture area, and the brightness uniformity is ensured through brightness constraint and spatial smoothing.

A computer-implemented method can specify a source version and a target version of an application programming interface (API) and a target programming language; and retrieve a difference graph connecting from a source knowledge graph characterizing the source version of the API to a target knowledge graph characterizing the target version of the API. The difference graph includes one or more revision edges representing changes of the API between the source version and the target version. The method can install one or more function packages written in the target programming language and associated with the one or more revision edges; and run the one or more function packages to update the API from the source version to the target version.

The application relates to the technical field of software testing, and discloses a test requirement analysis and use case generation method and system based on AI, which comprises the following steps: acquiring a new and old version requirement document set, constructing a corresponding logic flow graph, generating a logic increment difference graph through graph isomorphism matching and determining an overall influence area; acquiring a stock test case set and mapping the stock test case set into a stock use case path set, identifying an affected path, executing path geometric deformation and expansion, and generating a final evolution path; finally, calculating evolution path comprehensive energy loss, determining a use case evolution state, obtaining a final test case set, and establishing a use case evolution pedigree table; the application realizes activation and evolution of stock test assets, and reduces the maintenance cost of government software regression testing.

This invention provides a method for detecting overlapping regions in 3D SAR, including collecting and processing image area data; identifying overlapping regions in each image within the area data and merging initial overlapping vector maps; using an overlapping homogeneity coefficient to compare and filter images in the planar dimension to obtain a final overlapping vector map; calculating elevation difference maps by combining processed interferometric DSM data products and point cloud DSM data products within the image area data; calculating slope difference maps by combining processed interferometric DSM data products and point cloud DSM data products within the image area data; and comprehensively verifying the final overlapping vector map, elevation difference map, and slope difference map to obtain the final overlapping region. This invention identifies and verifies overlapping regions in both planar and elevation dimensions, uses an overlapping homogeneity coefficient to filter non-overlapping regions in the 3D SAR DOM data products, and proposes a comprehensive weighting method to calculate the elevation difference between interferometric DSM data products and point cloud DSM data products to determine overlapping regions.

This invention discloses a polarimetric SAR change detection method and system based on a Siamese attention complex convolutional neural network. It addresses the issues of poor quality difference maps generated by speckle noise in PolSAR images, and the problem that current real-number networks for SAR image change detection can disrupt the integrity of the complex structure of PolSAR data. This invention utilizes a Siamese network architecture to construct a more robust multi-scale feature difference map to suppress the influence of speckle noise. Furthermore, it combines a complex network to maintain the integrity of the PolSAR data structure and reduce polarimetric information loss. A corresponding complex attention module is also designed to enhance the identification of changed regions. Finally, the change detection results are obtained by decoding the multi-scale feature difference map. By using a Siamese attention complex convolutional neural network, the invention enhances the utilization of polarimetric information while suppressing the influence of speckle noise, thereby improving the accuracy of PolSAR change detection.

The application discloses a kind of fog wide field of view video image change detection method and device, method includes: based on color attenuation priori to single image is defogged, obtains clear image after defogging;By arctangent ratio operator and extreme pixel ratio operator, clear image after defogging is handled, respectively obtain two difference maps;Combining image energy features and Laplacian pyramid, the two difference images obtained are adaptively fused to obtain fused difference map and further denoising is combined with median filtering;To denoised fusion difference image, difference between change and unchanged pixel caused by sensor noise is compressed to obtain final difference map;Based on the nearest neighbor relationship of pixel, the EN-K-Means clustering algorithm of clustering prototype is initialized to carry out clustering analysis on final difference map to obtain final binary image.The device includes: processor and memory.The present application can effectively reduce the occurrence of false alarm.

The application discloses a SAR image change detection method based on saliency detection and channel enhanced attention network. Firstly, two difference maps are generated through a logarithmic ratio operator and saliency detection, and an enhanced saliency fusion difference map is obtained through multiplication fusion; then a clustering algorithm is used for pre-classification, and training samples and test samples are selected based on the classification results; finally, a CEAN network containing an EMA module and an ASPP module is constructed for feature learning and final classification. The application enhances the change region features through saliency detection, realizes accurate feature focusing through the EMA module, captures multi-scale context information through the ASPP module, effectively improves the accuracy and robustness of SAR image change detection, and is particularly suitable for change detection tasks in complex scenes.

This invention discloses a real-time landslide disaster monitoring method that integrates temporal pixel differences, belonging to the field of data processing technology. The method includes: establishing a reference image and performing geometric registration and radiometric normalization on the real-time image; generating a difference map pixel by pixel, and marking candidate points based on adaptive thresholds set according to local texture and noise; performing connectivity analysis on the candidate points, and marking suspected landslide areas using area and intensity thresholds; detecting and dynamically correcting isolated abrupt pixels, and establishing a database of repeatedly occurring isolated points to trigger cross-validation; calculating a depth map through binocular vision or monocular motion recovery, and generating a depth difference map by comparing it with historical depth maps; fusing the pixel difference map and the depth difference map to generate a deformation intensity map, and inputting it into a multi-agent reinforcement learning framework to dynamically optimize UAV flight path planning. The progressive perception from pixel to three-dimensional space, adaptive thresholds, multi-source fusion, and dynamic resource scheduling significantly improve the accuracy and real-time performance of landslide monitoring.

This invention discloses a visual inspection method and system compatible with multiple workpiece models, relating to the field of machine vision inspection technology. The method includes acquiring the original image of the workpiece at the inspection station; preprocessing to extract its shape contour information; matching the contour with a feature template library to identify the workpiece model; loading preset standard features and detection area definitions associated with that model; locating the corresponding image sub-region in the original image; extracting multi-scale features to obtain actual feature data; comparing each feature with preset standard features to generate a feature difference map; analyzing the feature difference map to determine if the workpiece has defects; and generating a visual inspection result containing the workpiece model, defect location, and defect type. This method can automatically adapt to the inspection of multiple workpiece models, accurately locate the detection area and extract multi-scale features, complete defect determination through the feature difference map, and output complete inspection result information.