5931 results about "Sample image" patented technology

A method for aligning a sample to an array is provided. An image of sample image of a sample can be received by a data processor. The sample image having a first resolution. An array image including an overlay of an array with the sample and an array fiducial can be received by the data processor. The array image having a second resolution lower than the first resolution of the sample image. The sample image can be registered to the array image by aligning the sample image and the array image. An aligned image can be generated based on the registering. The aligned image can include an overlay of the sample image with the array. The aligned image can be provided by the data processor. A method for detecting fiducials associated with an array is provided. Systems and non-transitory computer readable mediums performing the method are also provided.

The present disclosure relates to the technical field of artificial intelligence, and provides a multimodal large language model training method, a correlation calculation method, and a label generation method. The multimodal large language model training method comprises: on the basis of a sample text feature vector, a sample image feature vector, and a sample first multimodal feature vector which are obtained by processing image information and text description information of a sample commodity by a pre-trained multimodal large language model, training the pre-trained multimodal large language model to obtain a multimodal large language model; and processing a sample search word, the image information and the text description information on the basis of the multimodal large language model to obtain a sample search word feature vector, a first multimodal feature vector and a sample second multimodal feature vector, and training the multimodal large language model to obtain a trained multimodal large language model. The trained multimodal large language model of the present disclosure can simultaneously learn image features related to the search word and the text description information, and thus the generated second multimodal feature vector is more accurate.

A method for aligning a sample to an array is provided. An image of sample image of a sample can be received by a data processor. The sample image having a first resolution. An array image including an overlay of an array with the sample and an array fiducial can be received by the data processor. The array image having a second resolution lower than the first resolution of the sample image. The sample image can be regisered to the array image by aligning the sample image and the array image. An aligned image can be generated based on the registering. The aligned image can can include an overlay of the sample image with the array. The aligned image can be provided by the data processor. A method for detecing fiducials associated with an array is provided. Systems and non-transitory computer readable mediums performing the method are also provided.

Embodiments of the disclosure relate to unsupervised learning with synthetic data and attention masks. According to example embodiments of the disclosure, a plurality of sample images are generated by providing a plurality of text prompts into a trained generative model, respectively. For a sample image of the plurality of sample images, at least one attention map is obtained from a generative model, the at least one attention map being determined by the generative model for generating the sample image, an attention map indicating visual elements of an object within the sample image. Training of a target model is performed according to unsupervised learning at least based on the plurality of sample images and attention maps for the plurality of sample images, the target model being configured to perform an image processing task.

Provided is a method for training a scene recognition model. The scene recognition model includes a core feature extraction layer, a global information feature extraction layer, an LCS module of at least one level with an attention mechanism, and a fully-connected decision layer. The method includes: acquiring parameters of the core feature extraction layer and the global information feature extraction layer by training based on a first scene label of a sample image and a standard cross-entropy loss; training a weight parameter of the LCS module of each level, based on a loss value acquired by performing a pixel-by-pixel calculation on a feature map output from the LCS module of each level and the first scene label of the sample image; and acquiring a parameter of the fully-connected decision layer by training based on the first scene label of the sample image and the standard cross-entropy loss.

The invention relates to a PCB defect detection method and system based on a YOLOv8 network, and belongs to the technical field of target detection. Collecting and preprocessing a PCB sample image data set for training a PCB defect detection model; inputting a to-be-detected image into the trained defect detection model to obtain a detection result; the to-be-detected image is generated by collecting and processing image data of the to-be-detected PCB; wherein the defect detection model comprises a trunk network, a neck network and a head network, and the trunk network extracts features through a mobile reverse bottleneck convolution module and a C2f module to generate an intermediate feature map; and the neck network performs splicing fusion on the middle feature map to generate a spliced feature map, the head network performs re-weighting on the spliced feature map through a convolution kernel attention fusion module to generate a third feature map, and then the detection module outputs a detection result. The method not only improves the precision of the defect detection model in detecting the features of the tiny target, but also improves the detection speed and generalization ability, and reduces the calculation cost and complexity at the same time.

The invention provides a model pre-training method and device for a multi-language task, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining a multi-modal training data set which comprises a plurality of pieces of sample text data and a plurality of pieces of sample image-text pair data with multi-language contents, and covers a universal field and a preset business field; based on a plurality of sample image-text pair data, performing comparative learning training of alignment of visual features and text features on the initial model, freezing model parameters of a decoding module and adjusting model parameters of a visual encoder and a projection module in the training process until a first ending condition is met; based on the multiple pieces of sample image-text pair data and the multiple pieces of sample text data, constraint training of content understanding is carried out on the initial model meeting a first ending condition, and model parameters of a visual encoder, a projection module and a decoding module are adjusted in the training process until a second ending condition is met to obtain a target model; the information extraction capability of the model in the specific field can be remarkably improved.

The invention provides a zero sample image anomaly detection method and device, and belongs to the field of computer vision and image anomaly detection. The method comprises the steps that an image of a to-be-detected target is input into a preset zero sample image anomaly detection network, the network comprises an image encoder and a text encoder, and through multi-mode prompt learning, the image encoder outputs local visual features of the image, and the text encoder outputs text features capable of being learned and prompted to be embedded; and calculating the cosine similarity of the local visual features and the text features and interpolating to obtain a similar graph, and measuring the similarity to obtain an abnormal graph so as to obtain an abnormal detection result of the input image. According to the method, the flexibility of feature representation and the generalization ability of prompt are enhanced by constructing multi-modal prompt learning, the complexity of manual design of prompt engineering can be reduced, and a better anomaly detection prompt can be constructed, so that the effect of a zero sample anomaly detection model is improved.

The invention provides a circulating tumor cell recognition model training method and system based on deep learning, and the method comprises the steps: obtaining a multi-channel pathological sample image set of circulating tumor cells, carrying out the cross-modal feature extraction of a multi-channel pathological sample image, generating a fusion feature vector containing the morphological features and microenvironment features of the cells, and carrying out the recognition of the circulating tumor cells through the fusion feature vector. A deep convolutional network with a multi-stage training strategy is constructed, network parameters of the deep convolutional network in the training process are verified and adjusted by adopting a hierarchical cross validation mechanism, and finally a final recognition model is generated based on the verified and adjusted network parameters. The final recognition model is configured to receive the clinical pathology image stream and output localization coordinates and classification confidence of circulating tumor cells. According to the method, a circulating tumor cell recognition result can conform to a cell space distribution rule and has clinical pathological diagnosis interpretability, and the problems of high false positive rate and insufficient cell subtype discrimination caused by neglecting of microenvironment interaction in a traditional method are solved.

A method and apparatus for training a text-graph model, a device, and a storage medium, relating to the artificial intelligence. The method comprises: on the basis of a training set of graph-text sample pairs, performing iterative training on a model to be trained, to obtain a text-graph model; in a training process: selecting a graph-text sample pair from the training set of graph-text sample pairs, the graph-text sample pair comprising a sample image and a description text, and the sample image comprising at least two objects; obtaining mask images corresponding to the at least two objects and associated object class names, the mask images being used for distinguishing position regions of the objects in the sample image; inputting the sample image and the description text to a model to be trained, to obtain an image prediction noise of the sample image, and inputting at least two mask images and the associated object class names to the model to be trained to obtain object prediction noises respectively associated with the at least two mask images; and constructing a loss function on the basis of the image prediction noise and the object prediction noises, and by using the loss function, adjusting a parameter for the model to be trained.

The invention relates to the technical field of industrial defect detection, and discloses a memory matching industrial defect detection method based on adaptive feature fusion, and the method comprises the steps: generating a plurality of unknown defect samples through an enhanced image-level defect simulation strategy, and helping a model to effectively distinguish a normal mode from an abnormal mode; the method comprises the following steps: extracting multi-scale features of a to-be-detected sample image, a normal sample image and a defect sample image, designing an adaptive hierarchical memory bank architecture, embedding the multi-scale features into a grid memory bank in a layered manner, further improving the reasoning speed through an adaptive core set sampling strategy, and combining multi-scale feature fusion and a defect positioning optimization technology to obtain a defect positioning algorithm. And the difference between the multi-scale features and the normal mode is fully utilized, so that the accuracy of defect detection and positioning is remarkably improved. Compared with the prior art, the defect detection precision and the positioning accuracy can be improved, and meanwhile, the real-time detection requirement can be met.

The invention discloses a diffusion model-based text graph and diffusion model training method, device and equipment. The method comprises the following steps of: obtaining a sample picture, an annotation box of an instance, local text description and global text description; noise is added through a diffusion process; selecting a training sample picture and dividing into a plurality of blocks; performing interactive attention calculation by using a cross attention module of the diffusion model to obtain attention scores of the local text description / global text description on each block; determining that the text description to which the block belongs is the local text description of the instance to which the block belongs or is empty; inputting the plurality of blocks of the training sample picture, the text description to which each block belongs, the global text description and the attention score of each block to the block into a diffusion model for feature extraction, de-noising and splicing the block feature map, and adjusting the parameters of the diffusion model. According to the method and the device, the text graph model capable of accurately controlling the multiple target instances is provided, and the generated image is higher in quality, richer in content and more customized.

The invention relates to a training method and device of a safety equipment detection model, equipment and a storage medium. The method comprises the following steps: acquiring a sample image, inputting the sample image into a to-be-trained safety equipment detection model, performing preliminary feature extraction by a trunk module in the model to obtain initial image features, inputting the initial image features into a plurality of depth separable convolution modules with preset self-attention mechanisms in the model, and obtaining a plurality of depth separable convolution modules with preset self-attention mechanisms; a feature map is obtained through a depth separable convolution module, a corresponding attention weight is obtained through a self-attention mechanism, a weighted feature map is obtained through the information, then the weighted feature map is input into a feature fusion module of a model for self-adaptive fusion, a fused feature map is obtained, and a prediction detection result is obtained according to the fused feature map. And finally, according to the difference between the results, performing model training on the to-be-trained safety equipment detection model to obtain a trained safety equipment detection model. By adopting the method, the detection precision and the detection performance of the model can be improved.

The invention provides a zero sample image anomaly detection and positioning method based on a pre-training vision-language model, relates to the technical field of image anomaly detection and positioning, and aims to train a learnable adapter and a learnable text prompt by using a universal vision anomaly detection data set. A normal state text prompt and an abnormal state text prompt are constructed, and a normal state text representation and an abnormal state text representation are respectively obtained. In the aspect of image features, characterization of different levels of an image in a picture encoder is obtained, and image features of neighbor information of different scales are aggregated through window sizes of different scales and are adjusted by an adapter. And finally, calculating the similarity between the text-image features, and obtaining a probability score that the image is classified as abnormal by using a normalization mode. According to the invention, the general abnormal data set is utilized to train the learnable text prompt and the learnable adapter, so that the problem of how to carry out image anomaly detection when the training sample cannot be acquired is solved, the pre-training model can adapt to the target detection data set, and zero sample detection and positioning are effectively carried out.

The invention discloses a zero sample image classification method and device, and belongs to the field of image recognition. According to the method, a category description document is generated through a large language model, global and local feature information of an image and the document is obtained through a visual encoder and a text encoder, then decoupling is performed through a visual semantic decomposition module and a text semantic decomposition module, and visual semantic embedding and text semantic embedding are generated; through joint training of the modules, optimizing total loss composed of local-to-semantic mapping variance loss, multi-semantic diversity loss, decomposition semantic alignment loss and local semantic alignment loss; and the training is used for image category prediction in the reasoning stage after completion of training. According to the invention, the difficulty of zero sample image classification can be reduced, and the classification accuracy and efficiency are improved.

The invention provides an image fusion method and system based on two-stage adversarial training and edge perception, and belongs to the field of image processing. The method comprises the following steps: selecting an infrared-visible light sample image pair data set, and combining paired images to obtain an initial combined image; constructing a fusion feature generation adversarial model comprising a bidirectional interaction encoder, a double-branch decoder and a discriminator, inputting the initial merged image into the bidirectional interaction encoder, and enhancing features through a multi-scale feature enhancement and high and low frequency feature decomposition module; and obtaining multi-scale fusion features, inputting the multi-scale fusion features into a double-branch decoder, and fusing and reconstructing infrared and visible light image features to form fusion features. And inputting the image into a discriminator, training the model according to a two-stage training strategy until the loss function converges, and fusing the to-be-fused and merged image based on the trained model. By designing a two-stage training strategy and introducing an edge feature enhancement mechanism, fine processing of a frequency domain is realized, and feature extraction and fusion effects and quality of a fused image are improved.

The invention relates to an industrial defect sample generation method, which comprises the following steps of: processing a defect sample image to obtain condition information; cutting out a to-be-generated defect area from the normal sample, and carrying out image processing to obtain a target generation area; combining the target generation area and the condition information to generate a defect generation graph; and combining the defect generation graph with the cutting area of the normal sample to obtain a final defect image. According to the industrial defect sample generation method provided by the invention, local defect generation is realized, and an image instance enables a diffusion model to pay more attention to the defect instead of background generation. And meanwhile, the diffusion model is generated based on the background mask, so that the generated defect graph is consistent with the background of a normal sample, and the influence of a chromatic aberration problem caused by background inconsistency is reduced.

Provided in the present application are a model training method, a defect detection method and related apparatuses. The embodiments of the present application can be applied to various scenarios such as computer vision. The model training method trains an initial detection model by means of using a first sampled image set containing some of images that have been used for training and a full newly-added second training image set, and thus, compared with using full historical training data and full newly-added training data to train an initial model, more saves time and reduces the GPU hour consumption; adaptive evaluation of corresponding first weights of model parameters restricts updating of the model parameters with respect to historical training data, so as to solve the problem of knowledge forgetting caused by only using full newly-added data for model fine-tuning, thus improving the learning capability of the detection model; and an optimized detection model obtained by using the model training method is used to detect defects in an image for detection, thus improving the effect and accuracy of defect recognition.

The invention relates to the technical field of image segmentation, in particular to a plant salt tolerance response modeling prediction method and system based on a time sequence image. The method comprises the following steps: preprocessing acquired plant sample image data; performing image segmentation on the preprocessed image data by using a plant semantic segmentation model based on U-Net; a plant salt tolerance response prediction model based on TimeSform is constructed; and predicting the plant salt tolerance response grade by using the plant salt tolerance response prediction model. According to the time sequence image-based plant salt tolerance response prediction modeling method provided by the invention, a prediction process integrating image acquisition, preprocessing, dynamic feature extraction and depth time sequence modeling is constructed, so that the efficiency, precision and automation level of plant salt tolerance phenotype recognition are remarkably improved.

The invention discloses a few-sample image quality evaluation method and device based on a visual language model, and relates to the technical field of image quality evaluation. Gradient adjustment meta-prompt image quality evaluation (GRMP-IQA) is introduced, meta-knowledge from various image distortions is merged by adopting a double-layer gradient optimization process through meta-prompt pre-training, so that text refining and initialization of visual prompt are realized, the adaptive capacity of a CLIP model to an IQA task is enhanced, and the accuracy of the image quality evaluation is improved. The sensitivity of prompt initialization to various IQA scenes is reduced; and then correcting the deviation introduced by excessive attention of semantic content of a visual-language model (CLIP) through quality perception gradient regularization, and ensuring that the image quality is evaluated more accurately, so that the pre-trained CLIP model adapts to a BIQA task with a small number of training samples.

The invention provides a desertification monitoring grading and vegetation extraction method and system, and relates to the field of desertification remote sensing monitoring and ecological assessment, and the method comprises the steps: selecting a multi-temporal satellite image and an unmanned aerial vehicle image which cover a full research region according to a preset condition, and carrying out the data preprocessing of the multi-temporal satellite image and the unmanned aerial vehicle image; the method comprises the following steps: constructing a desertification difference index by fitting a feature space of a vegetation index and a surface albedo by using a multi-temporal satellite image, grading the desertification degree of a research area to obtain a grading result, and locking a key monitoring area in the research area according to the grading result; a vegetation sample image of a key monitoring area is obtained from an unmanned aerial vehicle image, HSL color space conversion and hue optimization processing are carried out on the vegetation sample image, and a normalized vegetation index based on HSL is constructed, so that vegetation information of the key monitoring area is finely extracted, a key desertification disaster area is effectively positioned, and the accuracy of the desertification disaster area is improved. And vegetation information in the region is finely extracted.

The invention discloses a face anti-fraud model training method and device based on image-text comparison assisted learning, and belongs to the technical field of artificial intelligence. The method comprises the following steps: identifying the content of each sample image in an image set to obtain a description text corresponding to each sample image; generating text data corresponding to each sample image in combination with the description text and the label; and inputting each sample image in the image set into an image encoder in the multi-modal pre-training model, and inputting text data corresponding to each image into the text encoder in the multi-modal pre-training model, so as to train the multi-modal pre-training model and obtain a face anti-fraud model. According to the method, the image content description text and the face type label are integrated, the features are mixed, the central difference convolution adapter module is introduced to capture the fine features of the face, the features are subjected to semantic alignment through image-text comparison learning and matching, and the generalization ability of the model in different application scenes is improved.

The invention discloses an anomaly detection method, system and device for a multi-mode large model of power equipment and a medium. The method comprises the following steps: acquiring a general anomaly detection vision sub-model, a general text encoder and a general language sub-model of a general power scene; the method is based on a general anomaly detection vision sub-model, a general text encoder and a general language sub-model. And a second sample image of the target power scene, a second prompt text associated with the second sample image, a second anomaly detection problem text associated with the second sample image, a second anomaly detection positioning tag associated with the second sample image and a second anomaly detection answer tag associated with the second sample image. Obtaining a power equipment multi-modal large model of the target power scene; and performing anomaly detection on the to-be-detected image of the target power scene by using the power equipment multi-modal large model of the target power scene. According to the embodiment of the invention, the construction efficiency of the multi-modal large model of the power equipment and the anomaly detection accuracy of the to-be-detected image can be improved.

The invention discloses a small target traffic sign recognition method based on improved YOLOv8, and the method comprises the steps: constructing a data set, selecting sample pictures from the data set, carrying out the labeling and data enhancement, and dividing the data set into a training set and a test set; the YOLOv8 model is improved, and a multi-scale attention mechanism EMA is embedded in a backbone network of the YOLOv8 model; an up-sampling operator in a neck network of the YOLOv8 model is replaced by a DySample up-sampling operator; replacing an original loss function CIoU of the YOLOv8 model with a WIoUv3 loss function; and inputting the training set into the improved YOLOv8 model for training, after training is completed, detecting the detection performance of the improved YOLOv8 model by using the test set to obtain an identification result, and evaluating the performance of the improved YOLOv8 model by using various indexes. According to the small target traffic sign identification method based on the improved YOLOv8, the identification precision and the identification speed of the small target traffic sign can be effectively improved.

A method for performing point-to-point whiteboard parameter ratio correction on a hyperspectral image includes: capturing hyperspectral data of a standard reference whiteboard in advance as white(x,y,w), and storing the records, then captures hyperspectral data of a sample as sample(x,y,w); then selecting an unobstructed and unshaded whiteboard area within a certain range of the hyperspectral data sample(x,y,w) of the test sample, and labeling the area as Area A; calculating a spectral average of the ROI on the sample image as SA(w); calculating a spectral average of whiteboard data in the same position as the ROI as WA(w); dividing the two spectral averages, and obtaining a correction coefficient alpha(w)=WA(w). / SA(w); multiplying the whiteboard parameter ratio correction coefficient alpha(w) by a sample reflectance image matrix after whiteboard parameter ratio correction to obtain a final hyperspectral reflectance image matrix REFL(x,y,w)=alpha(w).*sample(x,y,w). / white(x,y,w).

The invention relates to the field of artificial intelligence (AI), in particular to an electrical stimulation method, an electrical stimulation device, electrical stimulation equipment and a storage medium. The method comprises the steps of obtaining a to-be-perceived image of a target object, inputting the to-be-perceived image into a trained electrical stimulation model to obtain electrical stimulation parameters output by the electrical stimulation model, and performing corresponding electrical stimulation operation on the target object to recover visual perception of the target object on the to-be-perceived image. An electrical stimulation generator in electrical stimulation is obtained by performing first link training and second link training based on a plurality of sample images and corresponding sample neural signals, the first link represents a verification link among electrical stimulation parameters, analog neural signals and reconstructed images, and the second link represents a verification link among the reconstructed images. The second link represents a verification link among the electrical stimulation parameters, the light magical view image and the sample image, so that the electrical stimulation model can greatly optimize the generation process of the electrical stimulation parameters, and the accuracy and generalization of restoring visual perception through electrical stimulation are improved.

The invention discloses a CLIP-based double-prompt optimized few-sample industrial anomaly detection method. The method comprises the following steps: respectively constructing a learnable normal text prompt template and an abnormal text prompt template by utilizing learnable normal word vectors and abnormal word vectors; performing image enhancement processing and image abnormal synthesis processing on the normal sample image for model training to generate an enhanced image and an abnormal synthesis image; inputting the learnable normal text prompt template and the learnable abnormal text prompt template into a text encoder in the CLIP model to obtain text prompt embedding; respectively inputting the normal sample image, the enhanced image and the abnormal synthetic image into an image encoder in the CLIP model to obtain visual embedding; and the similarity between text prompt embedding and visual embedding is measured to carry out optimization training on learnable text prompts, so that the trained CLIP model is utilized to carry out industrial anomaly detection.

The invention relates to the technical field of identification model construction, in particular to a method for constructing a medicinal material identification model based on an image technology. The method comprises the following steps: acquiring a medicinal material sample image and extracting features, constructing a medicinal material identification map, acquiring an original medicinal material image and performing texture remodeling, generating a standard medicinal material image, performing multi-dimensional feature extraction and fusion, forming comprehensive feature representation, and performing growth extension simulation based on the representation. Carrying out initial identification according to the generated track and the identification map to obtain an initial result, carrying out map matching based on the initial result, carrying out edge comparison, generating edge comparison parameters, carrying out centralized contour reconstruction according to the parameters, carrying out detail texture retrieval to generate a final matching result, and carrying out connection marking to generate corresponding result data. And performing model training by using the result data to generate an intelligent medicinal material identification model. According to the invention, the efficient method for constructing the medicinal material identification model based on the image technology is realized.

The invention provides a pesticide residue detection method and device combined with mass spectrometry, and relates to the technical field of pesticide residue detection.The method comprises the steps that a sample image is recognized, and a sample type and sample morphology characteristics are determined; matching and determining a pretreatment process and a sample interference matrix; detecting according to a pretreatment process; inputting the sample type, the sample morphology characteristics and the detection data set into a pesticide component analysis channel for identification, and outputting a pesticide residue component set; and performing interference correction on the plurality of pesticide concentrations according to the experimental equipment attribute characteristics, the interference matrix type set and the matrix content set, outputting a plurality of corrected pesticide concentrations, and generating a pesticide residue detection report. The technical problem that the accuracy and reliability of pesticide residue detection are poor due to the fact that matrix interference of different types of products cannot be handled in a traditional method can be solved, the interference problem of pesticide residue detection under a complex matrix can be effectively solved, and the accuracy and reliability of pesticide residue detection are remarkably improved.

The invention discloses a deep convolutional neural network-based pineapple maturity nondestructive testing method and system, and relates to the technical field of vegetable and fruit detection. Comprising the following steps: acquiring a pineapple appearance image with known surface texture feature parameters and color feature parameters, and preprocessing the pineapple appearance image to form a sample image; and establishing a convolutional neural network prediction model based on the sample image, inputting a pineapple appearance image and outputting corresponding feature parameters. And collecting an appearance image of a pineapple to be detected, preprocessing the appearance image, inputting the preprocessed appearance image into the prediction model to obtain characteristic parameters, and calculating a maturity judgment coefficient. And detecting the ethylene and carbon dioxide concentrations of the pineapple to be detected, and correcting the maturity judgment coefficient to obtain an accurate judgment coefficient. According to the method, the judgment threshold value is dynamically corrected, the accurate judgment coefficient is compared with the dynamic threshold value, different maturity judgment results are sent out, more accurate and comprehensive maturity evaluation is provided, and more efficient and accurate decision support is provided.