1734 results about "Image conversion" patented technology

The invention provides a talent evaluation management method and system based on AI intelligence, and relates to the technical field of artificial intelligence analysis, and the method comprises the steps: S1, converting a handwritten resume image into structured text data through OCR image recognition; converting the interview record into a dialogue text with a time sequence mark through voice transfer; performing format analysis on the electronic document to extract original text content; performing coding standardization processing on the structured text data, the dialogue text and the original text content to generate a standardized text data set containing semantic tags; and S2, performing context semantic coding on the standardized text data set, performing node alignment and semantic disambiguation processing on professional terms through a domain knowledge graph, and generating a text feature vector containing an entity association relationship. According to the method, through multi-source data integration, semantic deep analysis and dynamic matching calibration, the talent ability is accurately evaluated, the result is scientific and quantitative, and intelligent and interpretable talent evaluation and decision support is provided.

The invention discloses a thermal imaging temperature rise trend early warning system based on time-space sequence prediction, and particularly relates to the technical field of thermal imaging data prediction and early warning. The thermal imaging temperature rise trend early warning system comprises an image conversion module, a fluctuation feature extraction module, an edge prediction module, an anomaly characterization module and a prediction decision module; a temperature dynamic change rate and gradient intensity are calculated, edge model prediction is carried out based on a fluctuation index combination, when the fluctuation index combination does not exceed a stable interval, a lightweight deep network model deployed at a thermal imaging acquisition end is called, and when the fluctuation index combination exceeds the stable interval, a prediction decision module determines whether to switch to a high-order multi-modal model; the space-time information extraction capability is improved by constructing the temperature evolution data body, the prediction path is dynamically controlled based on the fluctuation index combination, and the prediction stability and efficiency are improved; and the abnormal activation index and the prediction offset index are combined to realize adaptive switching of model calling, so that the accuracy and adaptability of the early warning system are enhanced.

Disclosed in the present invention are a hyperspectral calculation imaging method and system based on multi-domain fusion of spatial, spectral and frequency domains, and a medium. The method of the present invention comprises: using a two-dimensional offline discrete cosine transform (DCT) to convert an RGB image Y into a frequency domain to obtain a frequency domain feature map Yfreq; extracting a frequency information image (I) from the frequency domain feature map Yfreq; using a two-dimensional offline inverse discrete cosine transform (IDCT) to transform the frequency information image (I) into a spatial domain to obtain a frequency information image Xfreq of the spatial domain; and fusing the frequency information image Xfreq of the spatial domain into the spatial-spectral domain features of the RGB image Y to generate a hyperspectral image (II). The present invention aims to solve the problems of poor detail information and low reconstruction accuracy of hyperspectral images in existing hyperspectral calculation imaging, and realizes high-fidelity reconstruction of a target spectrum.

The invention provides a CT (Computed Tomography) image super-resolution reconstruction method based on a generative adversarial network. The method comprises the following steps of: 1, collecting high-resolution image data and corresponding low-resolution image data; 2, establishing a CycleGAN model comprising a first generator, a second generator, a first discriminator and a second discriminator; a third step of training a CycleGAN model by using the collected high-resolution image data and the corresponding low-resolution image data so as to optimize parameters of the CycleGAN model; and a fourth step of performing image conversion on the to-be-enhanced medical image which is input into the trained CycleGAN model.

Disclosed is a method for training a diffusion model for image-to-image translation, which is performed by a computing device. The method may include: obtaining an image of a target domain; sampling random noise from a distribution of a source domain; and training a diffusion model that translates an image of the source domain to the image of the target domain based on the sampled noise.

The invention discloses a large model defense method based on multi-view image-text conversion, and relates to the technical field of artificial intelligence security. The method comprises the steps of obtaining an image-text dialogue prompt word text, extracting syntactic structure parameters and semantic parameters of statements, splicing the syntactic structure parameters and the semantic parameters into a multi-modal tensor, and expanding a spliced vector into a plurality of structure vectors according to semantic relationship density to form a semantic structure vector group. According to the method, semantic density splitting and image space mapping of the multi-modal tensor are utilized, the cross-modal correlation analysis capability is enhanced, the image-text mixed attack path is accurately identified, and the entity mapping relation between the pixel gradient and the regional mask is combined, so that two-way verification of image-text semantic consistency is realized, and semantic fault vulnerabilities of single-modal detection are eliminated; a path intensity and instruction word dynamic weight fusion mechanism is adopted, the abnormal risk level of a statement structure is quantified, the adaptive limitation of a static rule to semantic variation is broken through, and the illegal content output probability is reduced.

A small pressurized water reactor fault diagnosis method based on a CNN-LSTM hybrid network comprises the following steps: firstly, acquiring sensor signals in a pressurized water reactor control system under a normal working condition, introducing corresponding faults at different positions according to characteristics of various typical faults, and acquiring corresponding sensor signals under different working conditions; discrete sampling processing is carried out as a training set and a test set; preprocessing the fault training set by adopting a signal-image conversion method, inputting obtained image data into a CNN-LSTM diagnosis model and training, and then verifying the diagnosis accuracy and robustness of the model based on test set data; parameters in the diagnosis model are continuously adjusted, so that an optimal diagnosis model is obtained; according to the technical means of combining the CNN network for extracting image features and the LSTM for extracting time sequence data fault features, the defects of a traditional fault diagnosis method in the aspects of diagnosis efficiency, accuracy, effect and the like are overcome; the invention further comprises a system, equipment and a storage medium for implementing the method.

The invention provides a mathematical problem solving method and device based on a multi-modal large model and electronic equipment, and relates to the technical field of artificial intelligence. The method comprises the following steps: determining a mathematical element image of a mathematical problem; inputting the mathematical element image into an image coding model to obtain an image vector output by the image coding model; the image coding model is obtained by training based on a sample mathematical element image and a positive sample text description and a negative sample text description corresponding to the sample mathematical element image; inputting the image vector into a self-adaptive module to obtain an image conversion coding vector output by the self-adaptive module; the adaptive module is obtained based on training of a sample image vector and a sample text vector; determining question stem characters of the mathematical question, and inputting the question stem characters and the image conversion coding vector into the large language model to obtain a prediction answering process output by the large language model; the large language model is obtained based on sample question stem characters, sample image conversion coding vectors and sample answering process training, and the mathematical problem solving capability of the multi-modal large model can be improved.

The invention provides a low-illumination image enhancement method, and belongs to the technical field of image processing. The method comprises the following steps: converting an original RGB image into an HSV color space to obtain a chroma channel component, a saturation channel component and a brightness channel component; enhancing the saturation channel component and the brightness channel component to obtain a saturation channel enhanced component and a brightness channel enhanced component; fusing the saturation channel enhancement component, the brightness channel enhancement component and the chroma channel component to obtain a synthesized RGB image; and performing noise reduction processing on the synthesized RGB image to obtain an enhanced RGB image. Through the method provided by the invention, the brightness balance, the detail retention degree and the color naturalness of the output image are remarkably improved; therefore, the problems of overexposure, noise amplification, detail loss and the like which are easily caused by unbalanced brightness distribution under low illumination are solved.

A natural language test case for an application is automatically converted into a test script by a test script generator. During the generation of the test script, the test script generator may access a blueprint of the application. The blueprint may include a time-evolving model of the application, which may include a set of translations and a state machine model of the application. The state machine model may include a state space and a set of state transitions. During the test script generation process, the blueprint may be continually updated to include new knowledge of the application. The test script generator may communicate with an artificial intelligence (AI) engine in order to determine the semantic similarity between two phrases, convert images into textual description, and perform tasks requiring the use of a large language model (LLM).

The invention relates to the technical field of image conversion, and provides a bidirectional fusion image style migration method and system based on content and style decoupling, and the method comprises the steps: obtaining a style image set and a content image, respectively extracting a common style feature and a content feature, carrying out the bidirectional fusion of the style feature and the content feature through a cross attention mechanism, and carrying out the bidirectional fusion of the style feature and the content feature. Generating two-way fusion features after fusion; extracting a text feature based on the obtained text description, splicing the text feature with the bidirectional fusion feature to generate a condition control vector as guide information for image generation, executing a denoising inverse diffusion process guided by the condition control vector on the obtained target image, and finally decoding to obtain a stylized image; based on bidirectional feature fusion of styles and contents and a text-guided diffusion generation mechanism, an image style migration effect with delicate style expression, clear semantic structure and strong controllability is realized.

The invention discloses a universal multi-modal target tracking method based on space-time propagation and modal cooperation, and belongs to the technical field of computer vision. The method comprises the following steps: converting RGB and X modal images into a token form, and constructing initial features in combination with modal specific time tokens; the method comprises the following steps of: extracting a multi-level enhanced feature through a Transform encoder and a Mama collaborative prompt block; generating discriminative fusion features by using a gating fusion and context sensing module; a time-guided attention mechanism is adopted to strengthen search area features, and a result is output through a tracking prediction head; and transmitting the fusion time token as historical information to the next frame, and dynamically updating the template by combining a long-short time template updating strategy. According to the method, complementarity and space-time dependence between modes are effectively mined, tracking robustness and generalization ability in a complex scene are improved, and the method is suitable for various mode combination tasks such as RGB-D, RGB-T and RGB-E.

The invention provides a method for generating multi-dimensional landform vector data based on remote sensing image semantic segmentation, and belongs to the technical field of remote sensing image ground object recognition and artificial intelligence, and the method comprises the steps: training a landform recognition deep learning model based on a remote sensing image, adjusting a pre-training weight to optimize an image encoder, and generating a classification result; the landform recognition deep learning model comprises an encoder formed by a multi-layer convolutional network and a decoder of an up-sampling and feature fusion architecture; extracting a single-band label corresponding to each landform type from the classification result, and generating a binary raster image with the single-band labels; and converting the binary raster image with the single-band label into a vector diagram, and combining the vector diagram with the elevation information to generate multi-dimensional geographic data.

The invention discloses an image-text question and answer method and device based on a multi-modal large model and a thinking chain and a medium, belongs to the technical field of natural language processing, and aims to solve the technical problem of how to improve the reasoning ability, the answer accuracy and the interpretability in a complex image-text question and answer task. According to the technical scheme, the method comprises the steps of collecting an image and a question text related to the image; the method comprises the following steps of: preprocessing an image, and converting the preprocessed image into an image token sequence imgtoken through a pre-trained image encoder ViT; the method comprises the following steps: preprocessing a question text related to an image, encoding the preprocessed question text, and converting the preprocessed question text into a question text token sequence; and splicing the image token sequence imgtoken and the question text token sequence query token into a multi-modal input representation.

The invention provides an OCR (Optical Character Recognition) method and system for high-precision table data structuring. The method comprises the following steps: converting an original image into a grayscale image and preprocessing the grayscale image; extracting a table edge structure line and filling a fracture part; detecting longitudinal and transverse straight lines in the preprocessed image, calculating intersection points, and determining a table row-column structure; dividing a cell region and positioning to generate a cell coordinate matrix; pixels in the cells are divided into a frame influence area and an effective data area, and the frame influence area executes neighborhood mean filtering and weighted fusion operation; performing end-to-end detection on characters and symbols in the effective data area, and outputting an OCR recognition result with coordinates; and dynamically generating a table structure template based on the cell coordinate matrix and an OCR recognition result, speculating a strategy matching field type through a rule, processing and merging cell missing data based on adjacent cell information, and outputting structured data. The reliability and accuracy of the OCR technology are improved, and the requirement of automatic information processing for high-precision data extraction is met.

The invention discloses a deep learning-based multimodal image fusion method for soft tissue photoacoustic / ultrasound imaging. Steps: an ultrasound-photoacoustic imaging device acquires photoacoustic and ultrasound images of human soft tissue and performs size normalization processing; an input spatial transformation module converts the images to the YCbCr space; an input pre-convolution module modifies the number of data channels; an input multi-scale feature extraction module extracts salient features from the source images; an input filter prediction module derives multi-scale filters; and an input filter fusion and adaptive enhancement module combines the input source images to obtain the final fused result. The invention has superior fusion performance compared to several traditional fusion methods and deep learning-based fusion methods, and more importantly, it exhibits excellent real-time performance. Furthermore, various modes of photoacoustic / ultrasound fusion extension experiments have verified the effectiveness of the method proposed in the invention.

A platform for architectural drawing generation and approval which allows for easy generation of 3D architectural images from text descriptions, question and answer templates, and / or sample images for user or designer review and approval, and conversion of those 3D architectural images to 2D architectural drawings for permitting and construction. In an embodiment, the system and method involve generating structured requirements via a question and answer structured template, generating 3D images from a combination of the structured requirements and sample images provided by a user, and generating 2D architectural drawings from the 3D architectural images. In an embodiment, the 2D architectural drawings can be submitted for automated code compliance checking via one or more architectural modeling engines.

The invention provides a tight gas micro-crack detection method and system based on seismic data, and relates to the technical field of tight gas micro-crack detection, and the method comprises the steps: obtaining seismic wave data, and obtaining the preprocessed seismic wave data through wavelet transform denoising, compressed sensing reconstruction and median filtering smoothing processing. Using a preset multi-dimensional feature extraction model to extract micro-crack related features from the preprocessed data, including crack edge, curvature, frequency and similarity features, and constructing a multi-dimensional feature matrix; and based on the feature matrix, modeling and training are carried out by adopting a convolutional neural network model of a spatial attention mechanism added with a channel, and then the crack probability of each to-be-detected position is predicted. And carrying out smoothing, denoising and connectivity analysis on the crack probability image through image conversion and optimization processing to obtain a final crack detection result. According to the invention, the precision of micro-crack detection can be effectively improved.

The invention relates to the field of data transmission, in particular to a multimode optical fiber image transmission method, system and device and a medium. The method comprises the steps of acquiring an original image of a target area through image acquisition equipment of the power transmission line operation robot; converting the original image into an optical signal and transmitting the optical signal through a multimode optical fiber to generate a speckle image with spatial disturbance characteristics; inputting the speckle image into a neural network model constructed based on a sliding window self-attention mechanism for image reconstruction; the reconstructed image is output to a power transmission line state analysis system for real-time monitoring; the sliding window self-attention block calculates local self-attention features in windows with fixed sizes through window multi-head self-attention and sliding window multi-head self-attention mechanisms which are alternately arranged, and feature interaction between adjacent windows is achieved through a sliding window mechanism. According to the method, the anti-interference capability and the dynamic environment adaptability are improved, the calculation complexity is reduced, and the model generalization capability is improved.

The invention discloses an intelligent pallet fork correction method based on visual inspection, and relates to the technical field of visual inspection, a real-time image of a pallet fork is collected through a visual inspection system, the real-time image is converted into a gray level image matrix, the gray level image matrix is compared with a pre-stored design image matrix pixel by pixel, a difference matrix is generated, and the difference matrix is used for correcting the pallet fork. According to the difference matrix, the pose offset of the pallet fork is calculated, the deformation compensation amount of the pallet fork is calculated through a compensation amount estimation model based on the pose offset in combination with the material parameters of the pallet fork, whether the deformation compensation amount exceeds a preset compensation amount threshold value or not is judged, and if yes, a control instruction of the pallet fork is generated based on the deformation compensation amount; and the adaptability of the system under different working environments and load conditions is enhanced.

The invention discloses an unmanned aerial vehicle infrared thermal imaging method and device for cold leakage detection of a low-temperature storage tank, and relates to the field of automatic inspection and detection of storage tanks, and the method comprises the steps: determining the inspection range and detection distance of an unmanned aerial vehicle based on geometric parameters, a cold leakage frequency region and environment parameters of a site; calculating the distance between the shooting points and the vertical coverage height of single-circle flight, further dividing flight elevation layering, and calculating the number of the shooting points of each circle of flight; establishing a three-dimensional model; collecting a visible light image and an infrared thermal imaging image of each shooting point; the method comprises the following steps: performing multi-dimensional correction and temperature image conversion on an acquired infrared thermal imaging image, performing image registration on a temperature image with a temperature scale and a visible light image, performing cold leakage area identification to obtain a cold leakage area, performing quantitative calculation to obtain three-dimensional positioning of a cold leakage position and a cold leakage area, and performing cold leakage area identification on the cold leakage area. And generating a visual detection result, a detection report and a maintenance suggestion. According to the invention, automatic detection and accurate analysis of the cold leakage area of the low-temperature storage tank can be realized.

The invention discloses a tea leaf fermentation degree determination method with image recognition, which comprises the following steps: shooting tea leaves through a high-definition camera and a camera in different tea leaf processing stages according to different tea leaves to obtain high-definition images of the tea leaves, preprocessing the collected tea leaf images, and determining the fermentation degree of the tea leaves according to the preprocessed tea leaf images. Through denoising, contrast enhancement and color correction technologies, an acquired image is preprocessed, the image is converted into a proper color space for extracting color features in the tea image, and dominant hue and color distribution on the surface of tea are analyzed. The invention relates to the technical field of tea leaf detection, and the method can realize non-destructive, real-time and accurate tea leaf fermentation degree monitoring, and automatically judges the fermentation state of tea leaves by shooting tea leaf images and analyzing visual features of colors, forms and textures of the tea leaves in combination with a machine learning algorithm. Therefore, the automation level and the production efficiency of the tea processing process are improved.

The invention discloses an infrared ship scene generation method and system based on collaborative optimization of a physical prior model and a data-driven algorithm, and a storage medium, and belongs to the technical field of infrared image generation and artificial intelligence fusion. Comprising the following steps: preprocessing an acquired real infrared ship image and an original visible light ship image; different types of ship databases are established through semantic segmentation, and a visible light ship foreground image is obtained; synthesizing a new visible light ship image, and converting the new visible light ship image into a pseudo-infrared image; segmenting the new visible light ship images, classifying the new visible light ship images according to materials, covering the segmented image masks into the corresponding preprocessed real infrared ship images, extracting real infrared gray values of ship parts, establishing a linear regression model, and outputting gray values of ships of different materials; and optimizing the pseudo infrared image, correcting the gray scale of the ship area by using the gray value, and finally generating an infrared ship scene. According to the method, the contradiction between physical consistency and generation efficiency is solved, and the problems of high cost and data shortage of infrared ship image acquisition are solved. Enhancement of infrared ship data can be used for sea ship detection and identification scenes.

The invention relates to a micro-expression recognition method based on double-flow feature fusion, and belongs to the field of computer vision. The method comprises the steps of obtaining N frames of images of a micro-expression video from a start frame to a vertex frame, and calculating an optical flow field between adjacent frames by adopting an optical flow algorithm so as to effectively extract optical flow features of micro-expressions; the method comprises the following steps: acquiring N frames of images of a micro-expression video from a start frame to a vertex frame, converting the N frames of RGB face images into a CIE Lab color space, calculating a pixel difference between two frames of converted Lab images, and extracting pixel stream features of micro-expressions; and inputting the optical flow features and the pixel flow features into a constructed double-flow three-dimensional convolutional network for feature extraction and fusion, and classifying micro expressions. According to the method, the dynamic and subtle changes of the micro-expression are effectively captured by combining the optical flow and the pixel difference characteristics. By integrating spatial and temporal information, richer feature representations are provided. The improved attention mechanism further focuses on fine facial changes, and the accuracy of micro-expression classification is improved.

The invention discloses a heat exchanger welding seam image enhancement processing method, and belongs to the technical field of image processing, and the method comprises the steps: S1, collecting a heat exchanger welding seam original image through a preset collection module, and converting the original image into a grayscale image; s2, a processing module is preset, the gray level image is segmented based on a preset dynamic threshold value, and a welding seam area image and a base metal area image are generated; s3, in the welding seam area image, generating a first enhanced image, a second enhanced image and a third enhanced image through a preset generation module; and S4, extracting a reference edge contour of the weld joint area image from the base metal area image, performing pixel-level fusion on the first enhanced image, the second enhanced image and the third enhanced image, and outputting a weld joint image. According to the method, the weld joint area is divided into the highlight area, the dark area and the middle area to be processed respectively, weld pool texture loss caused by strong reflection is effectively restrained, and the problem that details are lost in a highlight scene in an existing method is solved.

The invention discloses a multi-sensor fusion processing method, perception method and equipment based on LiDAR point cloud pseudo image conversion, a multi-view projection strategy is adopted to convert a preprocessed LiDAR point cloud into an aerial view, a front view and a side view, the aerial view, the front view and the side view are subjected to feature coding and then fused through a SENet attention mechanism to generate a multi-view fusion pseudo image, and a large amount of space information is reserved. Meanwhile, in order to effectively improve the multi-sensor feature fusion efficiency, after a visual image is preprocessed, an improved CNN network is adopted to extract the features of a LiDAR pseudo image and the visual image, feature fusion is achieved through a cross-modal attention mechanism, attention weights are generated by calculating a similarity matrix, and the feature fusion efficiency is improved. And after the features are enhanced, the features are fused in a channel splicing and element-level adding mode. The method can effectively improve the precision and robustness of automatic driving environment perception, enhances the performance in a complex scene, and is suitable for tasks such as target detection and semantic segmentation in automatic driving.

The invention relates to a medical image generation method and device based on a migration model, computer equipment and a storage medium, and aims to ensure data quality and consistency and lay a foundation for model training by acquiring and preprocessing a medical image report data set and generating an initial medical image and corresponding initial pathological text data. And constructing a text-to-image generation model based on a diffusion model, and preliminarily realizing text-to-image conversion. The generation model is finely adjusted by using initial data, and the generation precision and adaptability are improved. And analyzing the multiple pieces of target pathological text data, generating a target medical image by using the optimization model, and verifying the validity of the model in practical application. According to the technical scheme, transfer learning and a diffusion model are combined, the problems of medical data finiteness and distribution inconsistency are solved, the medical image generation effect and application value are improved, and important technical support is provided for clinical research and medical diagnosis.

The invention relates to a computer vision and digital image processing technology, in particular to an image defogging method of a multi-scale convolutional neural network based on dark channel prior. Estimating the initial transmissivity and atmospheric light of the foggy image based on a dark channel prior method; the foggy image is converted into a gray level image, a gray level threshold value is selected to be used for distinguishing a sky area and other areas, and the initial transmissivity after screening is obtained; optimizing the initial transmissivity through a multi-scale convolutional neural network by adopting a coarse optimization stage and a fine optimization stage in sequence to obtain refined transmissivity; and reconstructing a fogless image according to the refined transmissivity by adopting an atmospheric scattering model. According to the method, the advantages of a physical model and deep learning are fused, the problems of supersaturation, edge artifacts, color distortion and the like in a sky region in a traditional method are solved, the PSNR (Peak Signal to Noise Ratio) of a defogged image is remarkably improved (up to 23.30), the SSIM (Subscriber Identity Module) (up to 0.978) and the like, and a robust visual enhancement scheme is provided for scenes such as automatic driving and traffic monitoring.

The invention relates to the field of soil substance detection, and discloses a soil plastic detection method and system based on multi-modal fusion and deep learning, and the method comprises the following steps: obtaining one-dimensional hyperspectral data of a to-be-detected soil sample; converting the one-dimensional hyperspectral data based on an image conversion algorithm to obtain two-dimensional image representation; performing feature extraction on the one-dimensional hyperspectral data based on a first preset algorithm to obtain spectral features; performing feature extraction on the two-dimensional image representation based on a second preset algorithm to obtain image features; performing multi-view probability fusion on the spectral features and the image features to obtain fusion features; and inputting the fusion features into a pre-trained double-path attention residual convolutional network model for classification to obtain a detection result of the micro-plastics in the to-be-detected soil sample. According to the method, the detection accuracy is improved, the model robustness is enhanced, and low-concentration detection is realized.

The invention relates to the technical field of image processing, and particularly discloses a mobile terminal real-time image style migration method based on a lightweight convolutional network, and the method comprises the steps: obtaining an original image inputted by a user, and converting the original image into standardized image tensor data through a pixel normalization and dynamic partitioning strategy; performing feature coding on the image tensor data by using a lightweight convolutional neural network, and extracting a multi-scale feature map layer by layer through a deep separable convolutional layer and a bottleneck layer structure; based on dual-channel feature coding, dynamically performing weighted fusion of style features and content features through multi-scale convolution kernel parallel deployment and learnable parameters, and generating a fused feature map; according to the method, the pixel normalization and dynamic partitioning strategy is introduced, the image partitioning size is adaptively adjusted, the calculation efficiency and the feature retention degree are balanced, the calculation burden is reduced while the high image quality is ensured, and the model calculation amount is reduced by adopting the lightweight convolutional neural network and the depth separable convolution in combination with the bottleneck layer structure.