185 results about "Image classifier" patented technology

The invention provides an AI-based three-dimensional medical image intelligent labeling method and system. The accuracy and efficiency of medical image labeling are remarkably improved. By combining an image classifier of preliminary knowledge learning with a basic lesion labeling network, knowledge information from different sources can be synthesized, and a more comprehensive and reliable lesion semantic knowledge point sequence is generated. Furthermore, through three types of training image generation strategies, complementarity among different networks is effectively utilized, the recognition capability of common lesion areas is enhanced, and lesion areas which are respectively and uniquely found are explored, so that diversity and representativeness of training data are enriched. Finally, deep knowledge learning is carried out on the basic lesion labeling network based on the diversified training data, the recognition precision and generalization ability of the generated target lesion labeling network are remarkably improved, the workload of manual labeling of doctors is relieved, and the medical image analysis process is accelerated.

An image acquisitor acquires an image of a lumen imaged by an endoscope. An image classifier classifies the acquired lumen image as one of a plurality of types. When the lumen image is classified as a first type, an operation detail determinator determines an insertion operation detail of the endoscope using an insertion operation selection model generated by machine learning. When the lumen image is classified as a second type, the operation detail determinator determines the insertion operation detail of the endoscope using an algorithm for determining the insertion operation detail.

An examination time period evaluator acquires a time evaluation index related to a predetermined examination step (e.g. a net observation step) in an endoscopic examination. An image classifier acquires an implementation record of the endoscopic examination. An examination time period classifier calculates an actual time, i.e. a time required for the predetermined examination step, based on the implementation record. A display controller may display the evaluation index and the actual time on a display apparatus. The display controller may further display a comparison result between the evaluation index and the actual time on the display apparatus.

An apparatus for diagnosing a medical condition including at least a processor and a memory, the memory containing instructions configuring the at least a processor to receive a medical image that is the result of a medical imaging procedure and train a medical image classifier and a medical report classifier. Training the classifiers includes receiving training data, including a plurality of prior medical images and a plurality of prior medical reports, training the medical image classifier and the medical report classifier, and optimizing both classifiers using a common loss function. The memory further containing instructions configuring the at least a processor to generate a label for the medical image, including inputting the medical image into the medical image classifier and receiving the label as output from the medical image classifier.

Embodiments relate to a method and an electronic device for modifying a user interface of the electronic device. The method includes detecting, on the electronic device, a selection of a multi-keypad configuration among a plurality of multi-keypad configurations. Based on the selection of the multi-keypad configuration, a plurality of virtual keys are displayed on the user interface. The method includes receiving, on the user interface, a user touch gestures to configure one or more of the plurality of virtual keys to generate a virtual keyboard. The method includes updating machine learning models, image classifier models, and memory of the electronic device to update and store the generated virtual keyboard including the configuration of the one or more of the plurality of virtual keys to initiate user authentication in real time.

A network security system that classifies webpages and uses the classification of the webpages to enforce relevant security policies is disclosed. To classify a webpage, a screenshot of at least a portion of the webpage is captured. An embedding engine generates a subject image embedding of the screenshot. The subject image embedding is classified by an image classifier that includes an index of training image embeddings each having a label of their classification and an associated approximate nearest neighbors model trained to identify the label of the closest training image embedding to the subject image embedding and a score representing their similarity. The webpage is classified based at least in part on the score and the label from the image classifier. The network security system applies security policies to requests from client devices that identify the webpage as its destination based at least in part on the webpage classification.

A technique for a UAV includes: acquiring an aerial image of an area below a UAV that includes one or more instances of an object; analyzing the aerial image with an image classifier to classify select pixels of the aerial image as being keypoint pixels associated with keypoints of the object; grouping the keypoint pixels into one or more groups each associated with one of the instances of the object, wherein first keypoint pixels of the keypoint pixels are grouped into a first group of the one or more groups associated with a first instance of the one or more instances of the object; generating an estimate of a relative position of the UAV to the first instance of the object based at least upon a machine vision analysis of the first keypoint pixels; and navigating the UAV into alignment with the first instance based upon the estimate.

The present invention relates to a method for determining density values for specific tissues of the head and neck based on artificial intelligence. The method of the present invention includes an operation in which a computerized tomography (CT) image obtainer obtains a CT image of the head and neck of a patient from CT apparatuses of a plurality of different manufacturers, an operation in which a voxel image extractor extracts a plurality of voxel images for each density value section from the obtained CT image and merges the extracted plurality of voxel images, and an operation in which an image-of-interest classifier classifies an image of interest by inputting the merged voxel image into an image-of-interest selection model and estimating a position value.

Attention guided images for updating predictions made by an image classifier model are generated by extracting feature maps from a last convolutional layer or other layers of the image classifier model, where the image classifier model generates the feature maps in response to being provided as input an image and computing as output a prediction of a diagnosis. A concept weighted feature map is generated based on the extracted feature maps and coefficients of a concept class associated with an image class of the image. A weighted average feature map is generated by implementing channel wise pooling by averaging across channels for the concept weighted feature map. An attention guided image is generated by implementing element wise multiplication of the weighted average feature map with the image. An updated prediction of the diagnosis is generated by the image classifier model based on the attention guided image.

The invention discloses a maximum information coefficient-based feature selection method and system for image classification, electronic equipment, a storage medium and a program, and belongs to the technical field of electronic information. The method comprises the following steps: acquiring an image data set to be subjected to image classification; calculating a redundancy matrix and a maximum information coefficient according to the image data set; constructing a target function model based on the redundancy matrix and the maximum information coefficient; performing feature calculation on the image data set by using the target function model to obtain a feature score; sorting the feature scores according to a set condition, and selecting target features to form a feature subset; and training an image classifier by using the feature subset, and inputting a test set of the image data set into the trained image classifier to obtain a classification result. According to the method, the features with high correlation with the target are selected by maximizing the mutual information between the features and the classification information, unnecessary features are reduced by controlling the redundancy between the features, the feature redundancy and the new classification information are balanced, and the picture classification precision is improved.

A method for training an image classifier for multi-task image classification. The method includes providing a set of deep learning models pre-trained on an initial image dataset and fine-tuning the set of pre-trained deep learning models on n target datasets of images, wherein each target dataset of images is associated with one task of a plurality of tasks and wherein the set of pre-trained deep learning models is iteratively fine-tuned on one of the target datasets of images from 1 to n and the set of pre-trained deep learning models is re-initialised between each iteration. The method also includes extracting features associated with each of the plurality of tasks from the set of pre-trained deep learning models iteratively fine-tuned on the n target datasets of images, combining the extracted features from each of the fine- tuned set of deep learning models to generate a fused feature set for the plurality of tasks, and training one or more machine learning classifiers on the fused feature set of the extracted features.

The invention belongs to the field of deep neural networks and image classification, and particularly relates to an image classification adversarial training improvement method based on boundary sample enhancement. Comprising the following steps: step 1, preparing an image sample set; 2, designing a GAN network architecture suitable for boundary sample generation; step 3, training a GAN network suitable for boundary sample generation; and step 4, image classifier adversarial training participated by the boundary samples. The method can effectively restrain the stability of the decision boundary of the image classification model, enables the model to carry out finer feature learning in a critical region, and prevents the boundary from excessively shifting due to adversarial disturbance. And meanwhile, the model can still maintain stable and reliable feature expression when being disturbed. According to the method, the robustness of the model is improved, the damage to the precision of a natural sample is reduced, and better accuracy-robustness balance and stronger generalization ability are realized.

The invention relates to the field of industrial network security, and particularly discloses a picture network intrusion detection method based on feature selection and data balance, which comprises the following steps of: firstly, acquiring an intrusion detection data set containing non-numeric data and numeric data, performing one-hot coding on the non-numeric data, performing maximum and minimum normalization on the numeric data, and performing one-hot coding on the numeric data; dividing a training set and a test set; performing feature selection on the training set by using a mixed feature selection strategy of a ReliefF filtering method and a Boruta packaging method, and performing resampling processing on the training set by using an SMOTE-ENN mixed sampling method; then, converting the resampled training set and the original test set into grayscale images, and performing training by using an OfficientNet image classifier to obtain a trained model; and finally, the model is used for carrying out classification prediction on gray level images of a test set. The problems of data redundancy, dimension disasters and class imbalance in an IIOT environment are effectively solved, and the accuracy and stability of an intrusion detection system are remarkably improved.

A technique for a UAV includes: acquiring an aerial image of an area below a UAV that includes one or more instances of an object; analyzing the aerial image with an image classifier to classify select pixels of the aerial image as being keypoint pixels associated with keypoints of the object; grouping the keypoint pixels into one or more groups each associated with one of the instances of the object, wherein first keypoint pixels of the keypoint pixels are grouped into a first group of the one or more groups associated with a first instance of the one or more instances of the object; generating an estimate of a relative position of the UAV to the first instance of the object based at least upon a machine vision analysis of the first keypoint pixels; and navigating the UAV into alignment with the first instance based upon the estimate.

The present disclosure provides a method including defining a search space within a first image, which includes defining a plurality of image regions, generating second images corresponding to the image regions (for an image region, the corresponding second image substitutes reference pixels for the portion of the first image outside the image region), and applying an image classification model (trained to disregard the reference pixels) to the plurality of second images. The method further includes performing object detection on the search space using the image classification model.

A device and method for classifying a digital image with an image classifier. The method includes providing a class representing an object, determining, depending on the class, a synthetic digital image that includes the object, training the image classifier to predict the class representing the object depending on the synthetic digital image, and determining a class with the image classifier depending on the digital image. A device and method for training the image classifier, and for determining an image dataset for the training are also described.

An example operation may include at least one of determining, by a transformer encoder trained on annotated image-text data, first latents for a first dataset stored in a memory, and second latents for a second dataset stored in the memory, generating a similarity matrix based on comparisons between the first latents and the second latents, constructing a graph comprising nodes corresponding to the first latents and edges based on pairwise similarity exceeding a threshold, identifying connected components in the graph and selecting, from each component, at least one latent having a highest score from a classifier trained to approximate divergence between the first dataset and the second dataset, forming a reduced dataset comprising the at least one latent, providing the reduced dataset to a model training module, and training an image classifier using the reduced dataset and the second dataset.

The present application relates to the technical field of identity authentication, in particular to a cross-modal identity authentication method based on double storage repository collaborative optimization, by constructing a text and image classifier weight storage repository, the historical modal discriminant information is saved by exponential smoothing, and by cross-modal classification distribution consistency constraint, the current feature obtains another modal historical classification information supervision, and the unstable problem caused by only relying on the current batch is relieved; the reliability weighting and modal balance mechanism are introduced in the identity center construction, the sample reliability is evaluated according to the current modal and another modal historical classification result, and the balanced fusion is weighted and aggregated, so that the modal offset of the identity center is relieved; the identity center storage repository is constructed to save the historical identity center and dynamically update, so that the features are supervised by the current batch and long-term historical identity structure at the same time; by joint optimization of classification supervision, cross-modal distribution consistency, center separation and center contrast constraint, the accuracy and robustness of cross-modal identity authentication are improved.

The invention discloses a training method and device of an image classifier, relates to the field of data processing, and is used for solving the problem of low accuracy of model recognition image classification in an industrial scene. The training method comprises the following steps: for a first training stage, inputting any first sample data into an initial model to obtain a first classification result output by a main classifier of the initial model and a second classification result output by an auxiliary classifier of the initial model, jointly adjusting parameters of a feature extractor, a main classifier and an auxiliary classifier in the initial model through the first classification result and the second classification result; for a second training stage, inputting any second sample data into the initial classifier obtained in the previous training stage to obtain a third classification result output by the main classifier and a fourth classification result output by the auxiliary classifier; and adjusting parameters of the feature extractor according to the third classification result and the fourth classification result. Through the scheme, the accuracy of the image classifier can be improved in an industrial scene.

The application discloses an image classification method and device. The method comprises the following steps: performing semantic-guided attention weight processing on an image to be classified, extracting attribute visual primitive features and object visual primitive features, and processing the features by using an image classifier to obtain a target classification result corresponding to the image. The image classifier comprises fine-grained attribute prototypes and fine-grained object prototypes. Each fine-grained attribute prototype is calculated based on attribute visual primitive features extracted from an image sample combined with corresponding attribute semantic features and object semantic features, and the fine-grained object prototypes are calculated in the same way. Based on this, each attribute semantic feature or object semantic feature has a plurality of fine-grained attribute prototypes or fine-grained object prototypes corresponding thereto, the visual diversity of each attribute semantic feature and object semantic feature is improved, and the accuracy of image classification is improved.

Computer-implemented method for running an image classifier with an artificial neural network, characterized in that an output quantity (608) of the artificial neural network is determined, wherein the output quantity is determined (606) from input data for the artificial neural network and from an activation function of the artificial neural network and characterizes a classification of the input data, wherein the activation function is at least piecewise continuously derivable and monotonically increasing, wherein the activation function has at least three fixed points, wherein the number of fixed points of the activation function is either finite and odd or countable and discrete, and wherein the derivative of the activation function is alternately monotonically increasing and monotonically decreasing in regions between adjacent fixed points.

The application discloses a glioma IDH mutation state noninvasive prediction system based on a machine learning algorithm, relates to the technical field of medical image processing, and comprises a data acquisition module, a data processing module, a feature extraction module, a single nuclear magnetic sequence image classifier training module, a multi-nuclear magnetic sequence image classifier training module, a model evaluation module and a prediction module; the system acts on conventional nuclear magnetic sequence image data and FW sequence image data, FA sequence image data and MD sequence image data obtained by processing DTI sequence image data, and combines a machine learning algorithm to construct a prediction model, so that the extraction and analysis of features are more reliable and accurate, the limitation of DWI sequence image data in analyzing an IDH mutation state is solved, and the accuracy of the established prediction model is ensured.

The present invention belongs to the field of brain-computer interface technology application technology, and discloses a brain-computer information fusion classification method and system for shared subspace learning, wherein the brain-computer information fusion classification method includes a training phase and an inference phase; wherein the training phase utilizes paired images and brain response data, optimizes the shared subspace model parameters of images and brain responses through a comparative learning strategy of positive and negative sample sampling, and trains an image classifier; the inference phase extracts image features for classification, and achieves the application goal of the entire brain-computer information fusion classification system. The brain-computer information fusion classification system for shared subspace learning of the present invention can train shared subspaces end-to-end, achieve efficient transfer of brain cognitive information, and improve the performance of image classification tasks in complex open scenarios; through the application of "brain out of the loop", it improves efficiency and stability in real-world applications, and has broad application prospects under the new paradigm of brain-computer information collaboration.

The present invention belongs to the technical field of image processing, and discloses a method, a system, a medium, a device and a terminal for detecting a strong light area in an image, including: after preprocessing the image acquired by a camera to complete pixel gray value normalization, performing block processing on the image, calculating the image gray value features, and performing dimensionality reduction processing on the image features by using the PCA method; inputting the image features into an image classifier model to determine the types of strong light and non-strong light in the image; for the image classified as the strong light type, using the gray feature information of the image block to determine the position and size of the strong light area in the image, and completing the detection and positioning of the strong light area in the image. The present invention combines a machine learning image classification algorithm with a traditional image processing method, improves the detection efficiency and accuracy of strong light images, and completes the positioning of the strong light area in the image. The present invention uses the PCA principal component analysis method to optimize the image features and simplifies the operation process of the model.

Network risk control neural rule update method and device based on feature pattern consistency constraint. The method includes: S1. Determine the neural rules to be updated, mainly including the neural rules in the deep image classifier based on the convolutional neural network; S2. Randomly strengthen the input data of the neural rules within a well-constrained range; S3. Run the neural rules and calculate the loss based on the feature pattern consistency constraint algorithm and the classification result; S4. Optimize the loss through backpropagation to achieve the update of the neural rules. The present invention can complete the update of neural rules for network risk control on the premise of introducing a small additional computational overhead, provides a neural rule update method with better network risk control effect, and can more effectively improve the security and robustness of the target network; provides a flexible neural rule update method for network risk control that can be implemented in a plug-and-play manner at any usage stage of the network, including the initial stage and the ex-post stage.

A computer-implemented method for ascertaining at least one systematic error during the classification of images into at least one image category by a classification algorithm. The method includes: providing a list of image features and respective value indications for each of the image features; applying combinatorial testing to ascertain a predetermined sequence of test cases, which each include a subcombination of the image features and / or value indications included in the list; generating at least one image file using a text-into-image generation algorithm, the file including a synthetic image assigned to the predetermined image category and has the image feature and the value indication; classifying the generated, synthetic image by means of the classification algorithm into at least one of the plurality of image categories; and ascertaining the misclassification value by comparing the image category classified by the classification algorithm with the predetermined image category.

This invention provides a final layer training method and apparatus for a learning model that utilizes text as a substitute for images, reduces annotation costs for data group information where class sets and pseudo-attribute sets are matched, does not require the collection of separate image datasets balanced between data groups, and mitigates bias in image classifiers. [Solution] The final layer learning method of a learning model, performed by the final layer learning device of a learning model, includes the step of inputting training text into the second learning model and training the final layer of the first learning model, based on a projection model that connects the first embedding space of the first learning model and the second embedding space of the second learning model.

A universal model-based modeling and detection method and system for defect detection in panel production lines, a device, and a storage medium. The method comprises: step 1, collecting panel production line sample data and performing defect annotation to obtain a sample data set; step 2, using the data set for modeling and training to obtain a localization model; step 3, on the basis of the data set, cropping images each containing a defect and classifying the images to form defect image data sets; step 4, using the defect image data sets for modeling and training to obtain an image classifier, and cascading the image classifier with the localization model; and step 5, on the basis of factory attributes and manufacturing process attributes, mapping defects to specific defect names by means of the image classifier and the localization model. In the method, accurate defect name mapping can be performed according to specific production environments and requirements. The overall solution reduces communication and checking time, increases sales, and also improves the applicability and accuracy of detection results.

To provide a device and method for classifying a digital image with an image classifier (202).SOLUTION: A method comprises the steps of: providing (302) a class (206) representing an object; determining (304), according to the class (206), a synthetic digital image (208) that comprises the object; training (402) an image classifier (202) to predict the class (206) representing the object according to the synthetic digital image (208); and determining (504) a class with the image classifier (202) according to the digital image. A device and method for training the image classifier, and determining an image dataset for the training are also provided.SELECTED DRAWING: Figure 5

The present invention relates to a computer-implemented method 1000 for identifying patterns (23) that correlate with correct image classifications (52) and incorrect image classifications (52) using a scene graph (12). [Solution] For a set of images (11), this method obtains a scene graph (12) from the images, classifies the images using a pre-trained classifier (5), groups the scene graphs (12') according to whether the classification is correct, extracts representative subgraphs within each group, thereby revealing patterns associated with the classification (52).