567 results about "Image prediction" patented technology

SOLUTION: A semi-supervised medical image segmentation method based on a visual language model includes the steps of: obtaining a medical image; inputting an unlabeled image and a text description into a visual language model, and obtaining a text-guided mask based on obtained dense image embedding and text embedding; inputting a labeled image into a student model, and calculating supervised loss by using obtained labeled image prediction; respectively inputting the unlabeled image into the student model and a teacher model to obtain unlabeled image prediction and a pseudo label, merging the text-guided mask with the pseudo label, and calculating semi-supervised loss by using the merged pseudo label and unlabeled image prediction; and performing medical image segmentation by using a trained student model on the basis of the supervised loss and the semi-supervised loss.EFFECT: A target segmentation region can be accurately identified by using advantages of text descriptions.SELECTED DRAWING: Figure 1

The invention discloses a soil heavy metal inversion method and system integrating satellite remote sensing and near-end sensing, and the method comprises the steps: collecting a soil sample, and measuring the soil heavy metal content and a soil visible light-near infrared spectrum; obtaining a time sequence multispectral image of a research area, calculating a spectral index, and selecting and screening bare soil pixels through a threshold value to obtain a bare soil image; performing spectrum correction on the bare soil image; obtaining a joint dictionary and a sparse coefficient through sparse representation and dictionary learning, and reconstructing a hyperspectral image of the bare soil image; converting the one-dimensional spectral data into a two-dimensional spectrogram by using continuous wavelet transform, extracting spectral features in combination with a 2D-CNN algorithm, and constructing a soil heavy metal inversion model; and using the trained inversion model to predict the soil heavy metal content of the research area based on the reconstructed hyperspectral image. According to the method, satellite remote sensing and near-end sensing are integrated to obtain a large-scale accurate soil heavy metal content distribution map, deep features are extracted in combination with a 2D-CNN algorithm, and the inversion model precision and model efficiency are improved.

The invention provides an automatic defect labeling method and device, electronic equipment and a readable storage medium, and the method comprises the steps: segmenting a specified defect for a target image based on a first model for an input reference image and an example of a reference image mask, and obtaining a target image prediction mask; and judging whether the prediction mask of the target image belongs to a real defect or not through a multi-layer perceptron, and reserving the part belonging to the real defect, thereby realizing automatic marking of the defect in the target image. Wherein the first model is obtained by adding a feature encoder in a context reference segmentation algorithm DINOV model, and the feature encoder is used for extracting the similarity of defects in the reference image and the target image. According to the method, the defects of the target image can be labeled in different industrial scenes, the problem of background over-killing in defect labeling is reduced, and meanwhile, the time required for subsequent manual defect labeling inspection is shortened.

The invention relates to a multi-mode ultra-short-term photovoltaic power generation power prediction system and method, through setting an image sequence prediction module, historical image features are extracted step by step based on a gating loop algorithm, and compared with other methods, the method is more suitable for processing sequence data, so that more accurate historical motion features can be obtained. In addition, the image sequence prediction module also adopts a method based on a diffusion model to predict a future sky image, and the diffusion model has the characteristics of accurate predicted image and clear generated image compared with other sky image prediction models. Moreover, by arranging a multi-modal prediction module, feature fusion is carried out through a self-attention coding and decoding algorithm, features from different modals can be better fused in a self-attention mode, and meanwhile, a cross-attention layer can integrate historical features and predicted sky image features to obtain a more accurate generated power prediction result.

The invention discloses a distributed photovoltaic cluster power prediction method and device based on multi-modal fusion. The method comprises the following steps: acquiring historical photovoltaic data and historical photovoltaic images of a photovoltaic region to be predicted; analyzing a time sequence relationship in the historical photovoltaic data, extracting photovoltaic time sequence characteristics, and giving a first prediction result in combination with the data time sequence prediction model; extracting spatial features in the historical photovoltaic image, reconstructing the historical photovoltaic image, and giving a second prediction result in combination with the image prediction model; and in combination with a preset fusion weight, fusing the first prediction result and the second prediction result to obtain a target prediction power, and completing power prediction of the distributed photovoltaic cluster, thereby effectively capturing the influence of sudden weather events on photovoltaic power generation, improving the accuracy of conventional cloud picture data when coping with complex and changeable cloud layer motion, and improving the prediction efficiency of the distributed photovoltaic cluster. Therefore, the accuracy of power prediction is improved.

The invention relates to a textile product defect identification method based on improved YOLOv11. The method comprises the following steps: acquiring a textile product defect image data set; performing pretreatment; dividing into a training set and a verification set; the method comprises the following steps: introducing MConv into a YOLOv11 backbone network, adding a CCIAP module behind a C2PSA module, and applying BiFPN in a path aggregation network; performing prediction through YOLO Head to obtain N prediction feature maps; the overall loss of the network is calculated, and network parameters are optimized through back propagation; predicting the verification set image through a network to output AP values of various categories; repeating the above steps to obtain a trained YOLOv11 network; and detecting the test image or video by using the trained detector to obtain a detection result. According to the method, the MConv is introduced into the YOLOv11 network to enlarge the receptive field, the CCIAP module is added behind the C2PSA to improve the feature extraction capability, and the BiFPN is applied to the Neck layer to enhance the feature fusion capability, so that the target detection precision is improved and the real-time detection of textile product flaws is realized under the condition that the reasoning speed is not influenced.

The invention discloses a cross-view image geographic positioning method based on dynamic threshold pseudo tag self-training learning, and the method specifically comprises the following steps: introducing a difficult sample feature mining method, dynamically adjusting the loss weight of a sample according to the change of similarity, and building a dynamic difficult sample triple loss model; the method comprises the following steps: dynamically adjusting a confidence threshold value of a sample by adopting an index moving average weighting method, iteratively training and screening an unlabeled sample, namely a pseudo label, establishing a pseudo label self-training mechanism of a dynamic threshold value, mining and utilizing non-paired data, and solving the problem of high manual labeling cost; a reference image most similar to a query image is found through image retrieval, and the offset of a query position is predicted. Experiments on CVUSA and CVACT data sets show that as the distance threshold increases, the accuracy of the cross-view image geographic positioning method based on dynamic threshold pseudo tag self-training learning presents a stable rising trend, and the cross-view image geographic positioning method based on dynamic threshold pseudo tag self-training learning is superior to other methods under the same threshold condition.

One embodiment of the present invention sets forth a technique for simplifying a trained machine learning model. The technique includes determining a first set of images associated with a first output class predicted by the trained machine learning model. The technique also includes generating a first aggregated representation of the first set of images, wherein the first aggregated representation includes a first plurality of representative pixel values for a plurality of pixel locations included in the first set of images. The technique further includes generating a simplified representation of the trained machine learning model that includes a first mapping of the first aggregated representation to the first output class, wherein the first mapping indicates that the trained machine learning model predicts the first output class for one or more input images.

The invention relates to the technical field of five-wheel continuous casting low-oxygen copper, in particular to a copper rod continuous casting process parameter optimization-based microstructure prediction method, which comprises the following steps of: formulating an adjusting factor set based on a comparison result of a crystallization line position of a copper casting blank and a standard crystallization line position; obtaining a microstructure image and inputting the microstructure image into the feature comparison model to obtain an anomaly set; inputting the production elements and the abnormal set into a parameter optimization model, and screening out a plurality of adjustment schemes; setting a reference factor to perform serialized adjustment on the adjustment scheme, and casting a copper casting blank according to the previous adjustment scheme; various passive data of the cast wheel are obtained, and a real-time matrix is obtained; the production elements and the real-time matrix are input into a microscopic image prediction model, and microstructure prediction is carried out; through construction of a feature comparison model and a parameter optimization model and application of a real-time matrix and a microscopic image prediction model, the automation level of the production process is improved, and a scientific basis is provided for standardization of process parameters.

A text-to-image model training method, apparatus, and computer-readable storage medium for enhancing text-to-image generation through object-aware training. The method trains a text-to-image model using cyclic iterative training with sample image and text pairs. Training involves selecting image-text sample pairs containing multiple objects, obtaining corresponding mask images and object class names that distinguish location regions of the objects, and inputting both the sample image with description text and the mask images with object class names into the model. The method obtains image predicted noise and object predicted noises, constructs a loss function based on these predictions, and performs parameter adjustment accordingly. This approach enables improved object-level understanding in text-to-image generation models.

The invention relates to the technical field of aviation composite material detection, and discloses an aviation composite material terrace multi-dimensional flatness digital imaging acceptance system. The system comprises a feature acquisition module, an imaging parameter calibration module, an environment adaptation adjustment module, a deviation threshold setting module, an imaging prediction module and an acceptance feedback control module. The feature acquisition module analyzes the multi-dimensional flatness difference to obtain a feature distribution state value; the imaging parameter calibration module screens an optimal angle and resolution combination to obtain an imaging calibration parameter set; the environment adaptation adjustment module matches the environment factors and the imaging combination to obtain an environment adaptation parameter set; the deviation threshold value setting module sets a threshold value to obtain a deviation load threshold value; the imaging prediction module deduces an imaging change trend to obtain an imaging distribution prediction value; the acceptance feedback control module analyzes errors and adjusts parameters to obtain an acceptance regulation and control scheme. According to the system, through cooperation of multiple modules, the accuracy and adaptability of aviation composite floor flatness acceptance inspection are improved, and many problems existing in traditional detection are solved.

The embodiment of the invention provides a training method of an image denoising model, an image processing method and an image processing system. The training method of the image denoising model comprises the following steps: acquiring a sample standard image and a sample noise-added image; generating a sample reference image according to the sample standard image, inputting the sample noise-added image and the sample reference image into an initial image denoising model to obtain an initial prediction image, and determining noise mode information by the initial image denoising model according to coding feature information of the sample noise-added image and the sample reference image; denoising the sample noise-added image according to the noise mode information; adjusting model parameters according to the initial prediction image and the sample standard image to obtain a reference image denoising model; the sample noise-added image is input into a reference image denoising model, a target prediction image is obtained, and the reference image denoising model predicts noise mode information according to the sample noise-added image; and adjusting model parameters according to the target prediction image and the sample standard image to obtain an image denoising model.

The invention discloses a multispectral satellite cloud picture prediction method based on motion stripe decoupling. The method comprises the following steps: carrying out normalization preprocessing on multi-channel satellite observation data; utilizing a motion branch model to extract motion features based on a displacement field, iteratively updating a prediction frame in an autoregressive distortion-correction pipeline, and keeping physical consistency in combination with atmospheric dynamics and smoothness constraint; a texture branch model is utilized to sequentially pass through a high-fidelity encoder, long memory state space modeling and a high-fidelity decoder, time sequence texture features are extracted, and cloud picture details are kept; the motion features output by the motion branches and the texture features output by the texture branches are input into a gating fusion module, adaptive weighting of the features is achieved through convolution and a gating mechanism, and fusion features are output; and carrying out reverse normalization processing on the fusion features to obtain satellite cloud picture prediction results at a plurality of moments in the future. According to the method, the spatial texture fidelity of cloud picture prediction can be improved while the physical interpretability is ensured, and high-precision satellite cloud picture prediction is realized.

The invention discloses an object surface defect segmentation method based on small samples, and the method specifically comprises the steps: collecting images of different types of object surface defects and corresponding segmentation masks, constructing a small sample data set, and dividing the small sample data set into a training set and a test set; constructing training and testing tasks, and respectively extracting a corresponding support set and a query set for each task; constructing and training a defect segmentation network, wherein the defect segmentation network comprises a query prior mask generator, a prior-guided bidirectional feature interaction module, a prototype-loss compensation module and a context-aware attention-guided feature aggregation decoding module; and after training is completed, inputting the support set and the to-be-tested query image into the converged defect segmentation network, and outputting a query image prediction result. Experiments prove that the method realizes remarkable performance improvement on an FSSD-12 data set, and shows that the method has obvious advantages in a small sample defect segmentation task.

The invention belongs to the technical field of medical image processing, and particularly relates to a glioma segmentation method based on a multimodal fusion network guided by anatomical symmetry, which comprises the following steps of: jointly inputting an FLAIR image, a T2 image, a T1 image and a T1c image of the same glioma into a trained image segmentation model, and outputting a predicted segmentation image by the trained image segmentation model, the prediction segmentation image is a glioma MRI image with three segmentation areas obtained through prediction, and the three segmentation areas are an edema area, an enhanced tumor area and a necrosis area respectively; the image segmentation model comprises an encoder, a jump connection part and a decoder; the encoder comprises an ASG module, the jump connection part comprises an IMP module, and the decoder comprises a CMF module. Through a three-module cooperation mechanism, the performance of tumor localization, cross-modal fusion, subregion segmentation and the like is improved, and a reliable image basis is provided for glioma operation plan formulation, prognosis evaluation and personalized treatment decision.

The invention discloses a satellite cloud picture sequence prediction method combining deformable convolution and pyramid pooling, and relates to the field of deep learning. The method comprises the following specific steps: (1) preprocessing satellite cloud picture sequence data and dividing into a training set and a test set; (2) building a satellite cloud picture sequence prediction model of a basic encoder-translator-decoder structure; (3) combining a motion perception loss function and an L2 loss function to supervise the model for training; and (4) predicting a satellite cloud picture sequence image. According to the method, a deformable volume operator based on dynamic sparseness is used in an encoder and a decoder to replace the traditional convolution operation, so that the network can adaptively select proper parameters according to the space structure of a cloud picture, softmax normalization in deformable convolution space aggregation is removed, and the robustness of the network is improved. The memory access is optimized to accelerate the running speed; a pyramid pooling structure is used in the translator, so that the calculation process can be simplified, and spatial-temporal characteristics of different scales in the cloud picture sequence can be captured; meanwhile, a motion perception loss function and a traditional L2 loss function are combined to supervise model training, and motion information between adjacent frames of a satellite cloud picture sequence can be further obtained. The method is not only suitable for all satellite cloud picture sequence images, but also can be applied to sequence image prediction in other complex scenes.

The invention discloses a knowledge distillation-based model training method and electronic equipment. The method comprises the following steps: performing feature extraction on a first sample image through a plurality of teacher models to obtain foreground supervision sub-features and background supervision sub-features of N local category objects; performing feature extraction on the first sample image through a first student model to obtain first foreground features and first background features of the N local category objects; performing foreground knowledge distillation on the first student model according to the foreground supervision sub-feature and the first foreground feature, and performing background knowledge distillation on the first student model according to the background supervision sub-feature and the first background feature; the first student model after foreground knowledge distillation and background knowledge distillation is a second student model; and performing image prediction on the second sample image through the second student model, and adjusting model parameters of the second student model according to a prediction result of image prediction. According to the invention, on the premise of a limited number of samples, the student model with high concurrency capability and high precision can be distilled based on a plurality of teacher models.

An image prediction method, apparatus, and system, a device, and a storage medium are provided. The method includes: (401) obtaining a split mode of a current node, where the current node is an image block in a coding tree unit in a current image; (402) determining, based on the split mode of the current node and a size of the current node, whether the current node satisfies a first condition; and (403) when it is determined that the current node satisfies the first condition, performing intra prediction on all coding blocks belonging to the current node, to obtain predictors of all the coding blocks belonging to the current node.

The invention discloses a component defect detection method and device, electronic equipment and a storage medium. The method comprises the following steps: constructing a three-dimensional model of a component; collecting laser ultrasonic data of the three-dimensional model, and collecting a thermal imaging image and a polarized light image of the assembly; based on the laser ultrasonic data, the thermal imaging image and the polarized light image, predicting to obtain a first defect category and a first defect position corresponding to a defect area of the assembly; based on the defect category and the defect position of the defect area, reconstructing three-dimensional defect data of the component; and on the basis of the three-dimensional defect data, predicting to obtain an expansion direction and an expansion range of the defect area.

The invention discloses a remote sensing big data processing method and system based on partitioning and parallel machine learning. The processing method comprises the steps of obtaining remote sensing big data; based on a preset rule, partitioning the remote sensing big data to obtain a plurality of sub-region data; inputting the plurality of sub-region data into a pre-constructed remote sensing image prediction model in parallel to obtain each piece of sub-remote sensing image information corresponding to each piece of sub-region data, the remote sensing image prediction model comprises a plurality of sub-models, and each piece of sub-region data is selected based on a preset screening rule to obtain a plurality of pieces of sub-remote sensing image information corresponding to each piece of sub-region data; selecting one sub-model from the plurality of sub-models for independent training and prediction; and integrating each piece of sub remote sensing image information corresponding to each piece of sub region data to obtain a target remote sensing image. Multiple sub-region data are input into the sub-model in parallel, a large amount of data can be processed, the overall operation efficiency is improved, spatial heterogeneity can be better captured, and the remote sensing image prediction precision is improved.

The invention belongs to the technical field of steel performance intelligent analysis, and particularly relates to a high-strength and high-toughness rock drilling tool steel performance intelligent analysis method based on machine learning. The method comprises the following steps: firstly, collecting chemical components, process, microstructure images and performance data of rock drilling tool steel; then, a process-component prediction model and a microstructure image prediction model are constructed, and relations between component-process and microstructure characteristics and performance are mined respectively; then, the proportion of each component of the microstructure is obtained by combining finite element simulation with a genetic algorithm; predicting performance by using an XGBoost algorithm, comparing real indexes, and constructing an error feedback mechanism optimization model; and finally, solving an optimal parameter combination meeting a strength-toughness index by means of a particle swarm optimization algorithm. According to the method, multi-source data and multiple intelligent algorithms are fused, the defects of a traditional analysis method are effectively overcome, collaborative optimization of chemical components, the heat treatment process and the microstructure form is achieved, and the analysis precision and the production efficiency are improved.

The invention relates to a dynamic fine tuning method and device for a visual detection model, a storage medium and equipment, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining an original image, inputting the original image and question prompt information corresponding to the original image into a preset large visual language model, and obtaining multiple pieces of candidate response information; according to an image prediction result in each piece of candidate response information, constructing a dynamic derivable reward function corresponding to each piece of candidate response information; determining a current reward value of each piece of candidate response information according to the dynamic derivable reward function, and generating a model optimization strategy according to a preset strategy optimization function and the current reward value; and performing dynamic fine adjustment on the preset large visual language model based on the model optimization strategy to obtain the visual detection model. The training efficiency of the visual detection model is improved.

The invention provides a crack image segmentation method, system and device for a large building surface. The method comprises the following steps: acquiring a building surface original image of a to-be-detected target area; extracting a multi-dimensional feature vector of the original building surface image through a heterogeneous network formed by a convolutional neural network and a Transform decoder branch, and generating an image prediction result; carrying out dynamic adjustment based on an image prediction result, and carrying out constraint by adopting a consistency supervision mechanism to obtain an optimized prediction label; pixel-level contrast learning is carried out on the multi-dimensional feature vector and the optimized prediction label, and a final crack segmentation result is obtained; and reasoning based on the final crack segmentation result to obtain a crack segmentation probability graph. According to the method, automatic identification and accurate segmentation are carried out on the surface cracks of key infrastructures such as a dam through an artificial intelligence algorithm, the crack detection precision is improved under the condition of limited labeled data, the model robustness is enhanced, the facility operation and maintenance efficiency is optimized, and the structure safety is improved.

The invention relates to the technical field of image processing, and discloses a stomach tumor image segmentation method and system based on a hybrid model, a terminal and a storage medium, and the method comprises the steps: carrying out the resampling of a stomach tumor image, carrying out the normalization processing of voxels, and obtaining a compressed image set of each target object; performing iterative optimization on the compressed image set by using a generative adversarial network to obtain a target false image set, inputting the target false image set into an encoder of a target segmentation network, fusing the target false image set to obtain dimension fusion information, screening multiple pieces of expert data through a gating mechanism, and generating a corresponding fusion weight; and training the tumor segmentation model by utilizing expert data so as to predict the compressed image set and output an image prediction result. According to the method, iterative optimization is carried out on the image, finally, a sample with better quality is input into the model to participate in training, finally, the generalization ability and robustness of the model can be improved on the premise of not enhancing the labeling cost, and the accuracy of a prediction result is improved.

The disclosed method for classifying microorganisms contained in a sample comprises: preparing a slide of the sample; acquiring at least one digital image of the slide; and implementing, with a computer, a model for predicting the class of the microorganisms depending on the acquired image. According to the invention, said image is subdivided into sub-images and each sub-image is sub-divided into patches, and: A. for each patch, a microorganism feature extractor is applied, the feature extractor forming a convolutional part of a first convolutional neural network trained on patches annotated individually with at least one class; B. for each sub-image, a second neural network connected to the extractor is applied, the second neural network comprising an upstream pooling layer and one or more downstream layers comprising a layer for predicting at least one class, and being trained on training sub-images globally; and C. for the acquired image: F.a. calculating a feature vector calculated for the sub-images; F.b. applying a model for predicting at least one class for the microorganisms.

The invention discloses a newborn fundus image classification method and imaging method based on multi-modal data. The newborn fundus image classification method comprises the steps that existing newborn fundus image data are acquired and processed to construct a training data set; selecting a plurality of neonatal fundus images for text labeling; extracting text features and extracting corresponding image features in an offline state; training a text feature generator based on a pre-trained image encoder and a pre-trained text encoder; constructing a neonatal fundus image classification initial model comprising an image prediction module, a pseudo text prediction module and a fusion module, and training to obtain a trained neonatal fundus image classification model; and classifying actual neonatal fundus images by using the neonatal fundus image classification model. According to the method, neonatal fundus image classification based on multi-modal data can be realized, the reliability is higher, and the accuracy is better.

To reduce latency.SOLUTION: An information processing apparatus has detection means and region determination means. The detection means detects characteristics from an image of a real space captured by an imaging apparatus. The region determination means predicts, based on the detected characteristics, a region having characteristics with respect to an image captured next by the imaging apparatus, and determines only the predicted region having characteristics, in the image captured next by the imaging apparatus, as a region to be transmitted to the detection means.SELECTED DRAWING: Figure 1

A method performed by an electronic apparatus, includes: obtaining position information of an object in an input image; predicting trailing blur state information of the input image, based on the position information and the input image; and obtaining an output image by performing processing on the input image, based on the predicted trailing blur state information.

The invention provides an image authenticity determination method and device, equipment, a medium and a program product, and relates to the technical field of image processing. The method comprises the following steps: acquiring an original image; performing feature extraction processing on the original image from different dimensions to obtain image features of corresponding dimensions; carrying out feature fusion processing on the image features extracted from different dimensions based on a self-adaptive weight fusion mechanism to obtain multi-modal features with dynamically adjusted weights; and inputting the multi-modal features into a trained image prediction model for prediction processing, and outputting a tampering probability value for tampering the original image. The embodiment of the invention is used for solving the defect of low image authenticity judgment accuracy in the prior art, effectively integrating the information of three dimensions of the spatial domain, the frequency domain and the statistical features of the original image, and predicting the tampering probability value of the original image on the basis of feature fusion, so as to determine the authenticity of the original image according to the probability value, thereby improving the authenticity judgment accuracy of the original image. And the authenticity discrimination accuracy is greatly improved.

The invention relates to the technical field of artificial intelligence and computer vision, in particular to a time sequence remote sensing image prediction method and related equipment, and the method comprises the steps: firstly obtaining historical multi-frame remote sensing images of a target area, and constructing a time-space sequence data set; the model is a ViT-Informer model, and a ViT spatio-temporal feature extractor captures spatio-temporal correlation features among images through a self-attention mechanism to generate a spatio-temporal joint coding sequence; the Informer time sequence predictor adopts a probability sparse attention mechanism to process long sequence dependence, and multi-step recursive prediction is achieved. The model can effectively model a remote sensing image spatio-temporal evolution law, and a prediction image at a specified moment in the future is generated through iterative reasoning.