1513 results about "Image manipulation" patented technology

The application provides a lightweight neural network architecture search method based on flatness, which is used for reducing the time cost and computing resources of searching the neural network architecture. From the perspective of predicting the network generalization, the application provides a method for comparing the networks by taking the flatness of the candidate network at the initial time as an evaluation index. The method comprises the following steps: determining an image processing task and determining a data set; determining a network search space; selecting a certain number of neural network architectures in the search space; verifying the effectiveness of the evaluation index; calculating the flatness of the neural network architecture, and sorting the selected architectures according to the flatness; selecting the architecture with the maximum flatness as the optimal architecture; and training the optimal architecture to complete the image classification task. The application can select an architecture with high accuracy in a small time consumption.

It assists with setting operations and processing to ensure that the settings are appropriate for the intended use. [Solution] The image forming apparatus operates according to the setting value for each setting item. The image forming apparatus stores a setting management table that holds setting items corresponding to the purpose and the setting values ​​for those setting items. The image forming apparatus refers to the setting management table according to the input purpose and updates the setting item of the image forming apparatus with the setting value of the setting item corresponding to the purpose.

This utility model discloses a capsule testing device, belonging to the technical field of capsule testing machines. Specifically, it relates to a multi-purpose, fully compatible capsule testing machine, comprising a feeding hopper, a rotary feeding device, two sets of testing stations, a conveying device, a synchronization device, a testing system, a rejecting system, a finished product collection device, and a control system. This utility model is compatible with the testing of three major types of materials: empty capsules, filled capsules, and soft capsules, and can accommodate the testing needs of different capsule models. It is easy to clean and maintain, and can operate stably and uniformly, improving the accuracy and stability of testing results. This project adopts machine vision inspection principles and high-speed image processing technology to perform fully automatic, blind-spot-free testing of capsule products. It quickly models and manages different product types, counts and statistically analyzes the test results, and automatically rejects defective products. It features fast testing speed, high testing accuracy, comprehensive technical service, strong upgrade capabilities, and simple and easy-to-use equipment.

This application relates to the field of computer vision and image processing technology, and discloses a large-model pattern recognition method and system based on deep learning. The method includes: acquiring a temporal image sequence containing a target to be recognized; extracting the geometric topological features of the target in each frame of the image; tracking the target across frames to obtain a temporal numerical set of geometric topological features evolving over time; statistically analyzing the fluctuation parameters of the temporal numerical set within a preset temporal interval, and selecting geometric topological features that meet preset stability conditions from the fluctuation parameters as the essential features of the target; constructing a target pattern representation based on the essential features of the target, and matching the target pattern representation with a pre-stored pattern to determine the recognition result. This application significantly improves the robustness and generalization ability of the recognition model in complex dynamic scenarios such as material changes, illumination changes, pose changes, and even the emergence of new categories, effectively reducing the false positive rate and false negative rate.

This invention relates to the field of image processing and discloses a method, apparatus, device, and storage medium for generating petal effect images. The method includes: upon detecting a generation command, generating a first petal model according to a function corresponding to the generation command; coloring the petal regions of the first petal model to obtain a second petal model; generating a first image based on the second petal model and a preset image; generating a random translation matrix, a random rotation matrix, and a random scaling matrix when the first image is obtained; multiplying the random translation matrix, random rotation matrix, and random scaling matrix to obtain a random transformation matrix; and transforming the first image into a second image carrying random petal effects according to the random transformation matrix. This invention reduces the generation cost of petal effect images.

To solve such a problem that it is difficult to detect a region of a target category from an object image with high accuracy.SOLUTION: The above-mentioned problem can be solved by an image processing device comprising: an image division unit which acquires two or more small image pieces from an object image; a key region decision unit which decides two or more key regions from the object image; a local processing unit which acquires an identification value of one or more key regions in the small image piece by using one or more pieces of useful key region information for each small image piece and acquires an identification representative value of the one or more identification values; a reliability processing unit which executes the first inspection on whether or not a region corresponding to each of one or more key regions exists in each of one or more target reference images and the second inspection on whether or not a region corresponding to each of the one or more key regions decided by the key region decision unit exists in each of one or more other reference images to acquire the reliability of each of one or more key regions; a boarder line decision unit which decides a boarder line by using an identification representative value and reliability; an image constitution unit which constitutes an image in which the boarder line is clearly shown with respect to the object image; and an output unit which outputs the image.SELECTED DRAWING: Figure 2

An image data set is obtained (110), the set being associated with parameter(s) representative of image capture characteristic(s) (e.g. exposure) for the set and comprising pixel intensity values repr

The present disclosure relates to the field of image processing, and particularly relates to an interface management system, method and storage medium. The system comprises: a display comprising a graphical user interface, the graphical user interface comprising a display interface and a toolbar, the display interface being capable of displaying any one of a main application interface and a plurality of third-party application interfaces, and the toolbar comprising an identifier corresponding to an interface of at least one of the main application interface and the plurality of third-party application interfaces; and a controller configured to, based on a first user operation, open any one of the third-party application in the display interface, or based on a second user operation from the toolbar, control the display interface to switch between the main application and any one of the third-party application or the plurality of third-party applications. The present disclosure provides a user-unaware and highly integrated third-party application page embedding solution, and achieves convenient switching and management of the main application and the plurality of third-party application interfaces.

This invention discloses a multimodal document parsing and evaluation method and system for bidding and tendering audit scenarios. First, it collects the tender documents and bid documents to be audited to obtain a set of documents to be audited. Then, it identifies the document format attributes in the set of documents to be audited to obtain initial heterogeneous corpus. Next, it performs page-level multimodal detection on the initial heterogeneous corpus and obtains detection tags. This invention achieves the function of accurately constructing audit corpus from massive heterogeneous documents using a vision-enhanced hybrid parsing strategy, and then performing multimodal document parsing and evaluation using image processing algorithms. Furthermore, the dual-modal risk assessment model combining hard and soft rules not only solves the problems of low efficiency, incomplete coverage, and easy omission of hidden risks in existing manual review, but also solves the parsing difficulties caused by poor scanned document quality. This not only makes audit compliance review more intelligent and accurate, but also significantly improves the quality and efficiency of the review.

The present application belongs to the technical field of image processing, and specifically relates to a remote sensing image compression and reconstruction method based on feature perception and latent diffusion super-resolution. The method comprises an encoding and compression stage and a decoding and reconstruction stage. The encoding and compression stage performs intelligent analysis, selective compression and data packaging on the original high-resolution remote sensing image, intelligently identifies and separates the feature region and the homogeneous background region in the image through a convolutional neural network, and differentially processes the two regions, with the feature region being recorded at high precision and the homogeneous background region being aggressively down-sampled. The decoding and reconstruction stage recovers a high-resolution reconstructed image from the compressed data packet, and performs reconstruction through a latent diffusion super-resolution model. The model can utilize the semantic context of the image to generate visually natural textures, effectively avoiding overall blurring and other problems caused by traditional interpolation, and thus enabling the image to still well maintain key feature details under high compression ratio.

The present application belongs to the technical field of image processing, and discloses a primary alpha phase efficient segmentation method for small sample data titanium alloy microstructure, comprising the following steps: S1, collecting and preprocessing the titanium alloy microstructure image, forming a training set, a verification set and a test set through artificial labeling and data enhancement, and constructing a microstructure segmentation data set; S2, building a MaterialAlphaSAM segmentation model, which is based on SAM, combined with a field adaptation module and a geometric constraint prompt prior module, so that the model can effectively locate the target area under the condition of few samples, and enhance the segmentation precision and stability; S3, model training and performance verification based on MaterialAlphaSAM; S4, inputting the test set image into the trained model, outputting the pixel-level segmentation result of the primary alpha phase, and further performing quantitative analysis. Under the premise of not introducing large-scale parameters and labeling costs, the present application adaptively generates high-quality prompts consistent with the microstructure semantics and topographic features, and effectively improves the segmentation performance in the few sample scene.

The application relates to the technical field of image processing, and particularly discloses an image analysis method and device, computer equipment and a readable storage medium. The method comprises the following steps: obtaining a saturation channel image according to an original image; extracting a plurality of contours in the saturation channel image; determining target parameter values corresponding to the contours; and determining whether a non-working area exists in the original image according to an analysis result of the target parameter values corresponding to the contours. The saturation channel image can be pre-segmented by utilizing the saturation difference characteristics of different types of ground areas, and further parameter analysis can be performed on the segmented contours in combination with the characteristics or differences between the working area and the non-working area. Thus, whether a non-working area exists in each contour can be identified one by one, the missed judgment of the non-working area in the image can be effectively reduced, the non-working area in the image can be more accurately identified, and the working efficiency and safety of intelligent equipment are improved.

This application discloses a method, system, and device for analyzing the state of the tumor microenvironment based on medical images, relating to the field of medical image processing technology. First, by acquiring medical images and gene expression data of related tissues within those images, the medical images are preprocessed to obtain image patches, and semantic features of these patches are extracted. An algorithm is then used to calculate a predefined immune resistance mechanism that matches the gene expression data as the dominant immune resistance mechanism. This dominant immune resistance mechanism is then set as a supervisory label for the model, and a prediction model is constructed. The model is trained using a large amount of data, and the medical image to be analyzed is input into the trained prediction model to obtain the predicted category of the resistance mechanism. This application can accurately predict the resistance mechanism of pathological tissues based on medical images and their related gene expression data.

This disclosure discloses a file management method, apparatus, electronic device, and storage medium based on a large model, relating to the field of computer science, and particularly to artificial intelligence technologies such as large models, deep learning, and image processing. The method includes: acquiring descriptive information for file management; performing intent recognition on the descriptive information for file management using a large model to obtain a file management intent, wherein the file management intent includes the file to be managed on the terminal and the management action corresponding to the file to be managed; generating an executable file management script based on the file management intent; and executing the file management script to manage the file to be managed.

The present invention enables video compression with which an improvement in recognition accuracy and a reduction in data volume can both be achieved. This video processing system comprises: a compression / decompression unit which compresses a video clip on the basis of a video compression parameter, and decompresses the compressed video clip; a recognition unit to which the decompressed video clip is input, and which outputs a recognition result of the input video clip; an error calculation unit which compares the recognition result of the decompressed video clip with a recognition result serving as a correct answer, and calculates an error index value; and a model generation unit that generates a model for estimating the data volume of the compressed video clip and the error index value on the basis of the video clip and the video compression parameter. The model generation unit generates the model so that the difference between the data volume of the compressed video clip and the estimated data volume is reduced.

The technique disclosed in this invention attenuates spatial crosstalk in sequencing images used for base detection. Specifically, the technique disclosed in this invention accesses an image whose pixels depict intensity emissions from a target cluster and intensity emissions from adjacent clusters. These pixels include a center pixel containing the center of the target cluster. Each of these pixels can be divided into multiple sub-pixels. Based on a specific sub-pixel, among the multiple sub-pixels containing the center pixel of the target cluster, the technique disclosed in this invention selects a sub-pixel lookup table corresponding to that specific sub-pixel from a sub-pixel lookup table library. The selected sub-pixel lookup table contains pixel coefficients configured to maximize the signal-to-noise ratio. The technique disclosed in this invention multiplies these pixel coefficients element-wise with the pixels and determines a weighted sum.

An image forming apparatus includes at least one memory storing instructions, and at least one processor that, upon execution of the stored instructions, configures the at least one processor to operate as: a scanning unit configured to scan a document and generate image data, a display unit configured to display first image data generated by scanning a front side of the document with the scanning unit, wherein the display unit displays an object for treating the first image data as back-side data while displaying the first image data, and a control unit configured to treat second image data generated by scanning a back side of the document with the scanning unit as front-side data and treat the first image data as the back-side data when the object is selected.

Even if the authentication information set on the image processing device is forgotten, it will be possible to easily retrieve the authentication information while maintaining security. [Solution] The image processing device is an image processing device comprising: an authentication means for authenticating whether authentication information entered for login on an information processing terminal matches set authentication information; a receiving means for receiving an operation to acquire the set authentication information via an input device built into or attached to the image processing device; and an output means for outputting the set authentication information when the acquisition operation is received.

This invention discloses a lightweight lesion image segmentation method based on grouped context fusion, specifically relating to the fields of image processing and artificial intelligence. The input image is processed by a convolutional initial layer to extract preliminary features. The segmentation method relies on the MoMNet network, which adopts an encoder-decoder paradigm. The encoder uses the MoM module to extract hierarchical features in all four stages. The convolutional initial layer outputs the extracted preliminary features and passes them to the MoM module, which includes downsampling operations in four stages. The decoder fuses multi-scale information through an upsampling module and skip connections. At the same time, multiple prediction heads are jointly optimized by binary cross-entropy and Dice loss to achieve progressive learning for the image segmentation task.

The application relates to the technical field of table image processing, in particular to a table recognition method and device and a table semantic recognition method and device, which solve the problem of low table recognition accuracy. The table recognition method comprises the following steps: firstly, recognizing characters in a to-be-recognized table image, determining character positions and character contents of the to-be-recognized table image; then, based on the character positions and the character contents, determining boundaries of at least one cell of the to-be-recognized table image and character contents corresponding to the at least one cell respectively; further, based on the character contents corresponding to the at least one cell respectively, performing a character aggregation operation to determine phrases corresponding to the at least one cell respectively; finally, based on the boundaries of the at least one cell and the phrases corresponding to the at least one cell respectively, performing a merged cell operation on the at least one cell to determine a table structure of the to-be-recognized table image and character contents corresponding to cells in the table after the merged cell operation.

This invention relates to the field of image processing technology and discloses a method for target 3D reconstruction and automated model generation. The method includes: acquiring multi-view original point cloud data of the target object, and obtaining a simplified point cloud dataset through voxel filtering and downsampling; dividing the data into smooth regions and sharp edge feature sets based on curvature characteristics, performing smooth fitting and edge-preserving reconstruction respectively, and then generating a composite mesh model through vertex merging and mesh stitching; performing topological structure analysis on the model, detecting and optimizing non-manifold elements and topological holes to obtain an initial mesh model; parametrically mapping and automatically unfolding the texture of the model surface geometric manifold characteristics, and fusing and binding the unfolded texture map with the initial mesh model to finally complete the 3D reconstruction of the target object; this invention can improve the efficiency of target 3D reconstruction and automated model generation.

This invention discloses a method and apparatus for denoising real images, belonging to the field of image processing technology. The method includes training a denoising module using training samples as input to obtain a trained denoising module; acquiring a real image to be denoised; inputting the features of the real image into the trained denoising module; and having the blind spot branch of the trained denoising module denoise the real image, outputting a denoised image. The denoising module includes a parallel non-blind spot branch and a blind spot branch. The non-blind spot branch is a gradient-free non-blind spot branch, outputting a first denoising result; the blind spot branch generates mask features, which are denoised through convolution operations to obtain denoised features, serving as a second denoising result for the blind spot branch; and the first and second denoising results are fused as the output of the denoising module. This invention improves image detail recovery capabilities and achieves higher denoising accuracy and image quality.