13results about How to "Discriminating" patented technology

The invention discloses a chromosome segmentation method, system and device based on chromosome clusters and a medium, and belongs to the technical field of biomedicine and artificial intelligence in bioinformatics, and the segmentation method comprises the steps: carrying out the labeling processing of a chromosome original image, sequentially constructing a chromosome cluster recognition data set for different tasks, and training a chromosome cluster recognition network; constructing a chromosome cluster classification data set, and training a chromosome cluster classification network; constructing a chromosome cluster segmentation data set, and training a chromosome cluster segmentation network; inputting a chromosome original image, and performing reasoning by using the trained chromosome cluster recognition network, chromosome cluster classification network and chromosome cluster segmentation network in sequence to obtain a chromosome segmentation result; according to the invention, the chromosome can be automatically and accurately identified and segmented from the observation image; and by using the architecture based on the chromosome cluster, the probability of missing detection of the chromosome is reduced, and the downward compatibility of the network to the image resolution is high.

The invention discloses a joint detection tracking method and device, electronic equipment and a storage medium, and relates to the technical field of target tracking, and the method comprises the steps: carrying out the training through intra-frame direct matching and inter-frame indirect matching, calculating the self-comparison loss, and obtaining a discrimination representation; adjusting the matching similarity by using the consistency of cross-frame matching and continuous matching, calculating cross comparison loss, and extracting features beneficial to target association; meanwhile, shielding, disappearing and reappearing targets are considered, fuzzy targets are matched again, fuzzy comparison loss is calculated, and the certainty of follow-up target association is improved; synthesizing the self-comparison loss, the cross-comparison loss and the fuzzy comparison loss, and calculating the total loss; and tracking and identifying the target according to the total loss. According to the joint detection tracking method and device, the electronic equipment and the storage medium provided by the invention, the target in the camera range can be detected and tracked under the condition that a large amount of labeled data is not used.

The invention relates to the technical field of artificial intelligence, and provides a medical image domain generalization method based on a diffusion probability model, and the method comprises the following steps: S1, obtaining image data needed by training; s2, extracting image structure information; s3, building a diffusion generation model network suitable for the image data size and characteristics; s4, using the extracted structure information as prompt information, and prompting and controlling a diffusion generation model learning process; and S5, using a diffusion generation model to realize data distribution transfer of structure information maintenance. Through the technical scheme of the invention, the diffusion generation model conforming to the medical image data is designed, and the image data structure information is extracted as a prompt, so that cross-image modal data conversion maintained by the structure information is realized, meanwhile, the diversity of training data is increased, the purpose of domain generalization in a reasoning stage is achieved, and the reasoning efficiency is improved. Therefore, the downstream segmentation task precision for various image modalities is improved, and rapid and accurate diagnosis is assisted.

A method for extracting spatiotemporal features, a method for identifying traffic and a device thereof, which relate to the field of traffic analysis of optical fiber communication networks. The method comprises: for a pair of uplink and downlink real-time traffic data in a communication device, all packets obtained within each time interval T constitute a flow pulse, and according to the uplink and downlink cache packet information, the packet information of w dimensions is statistically analyzed using features of H dimensions, and the spatial features T within each time interval T are obtained. i , n spatial features T i Constructed into a timing sample ST n , Timing Sample ST n The dimension is W×H×n. The present invention only extracts spatial features for flow pulses and filters out the longer intervals between flow pulses, so it can quickly reduce the dimension of large-scale traffic data, especially encrypted traffic data; and can dynamically construct non-sparse discriminative traffic statistical spatial time series features, and the extracted traffic features have high discriminative and dimensional compression capabilities.

The invention relates to the technical field of computer vision, and provides a gait recognition feature recognition method based on multi-angle conversion, and the method comprises the following steps: obtaining to-be-recognized video data, and extracting a segmentation map of a pedestrian gait sequence; judging a pedestrian angle in the pedestrian gait sequence based on the trained angle classification model; when the proportion of the pedestrian being the body is smaller than a set value, inputting the non-body sequence diagram into a VAE generation model for angle correction; the VAE generation model comprises an encoder, a decoder and a softmax classification layer which are connected in sequence; and combining the corrected body sequence image with a body sequence in the pedestrian gait sequence to obtain a gait sequence, and performing feature extraction to obtain a gait recognition result. According to the multi-angle conversion method provided by the invention, the gait images at different visual angles are converted to a unified visual angle, so that the influence of the visual angle difference on the recognition precision is reduced, and the robustness of gait feature recognition is improved.

This invention provides a method, system, and storage medium for discriminating surface electromyography (EMG) signals in chronic low back pain based on a Fourier analysis neural network. First, a dataset is acquired, containing several EMG signals and their corresponding ground truth labels. The EMG signals are preprocessed to obtain preprocessed EMG signals. These preprocessed EMG signals are then input into a constructed Fourier analysis-based neural network to obtain a predicted label for each EMG signal. A loss function is constructed based on the ground truth labels and predicted labels, and the Fourier analysis-based neural network is trained to obtain a trained Fourier analysis-based neural network. The EMG signal to be detected is then input into the trained Fourier analysis-based neural network to obtain the corresponding predicted label. This invention, by constructing and training a Fourier analysis-based neural network, aims to extract discriminative latent periodic features, achieving automated discrimination of abnormal patterns in EMG signals of chronic low back pain, thus achieving more robust, generalizable, and efficient discrimination.

The invention provides a visual classification method and device based on adaptive multi-resolution feature fusion, electronic equipment and a storage medium, and relates to the technical field of computer vision. The method comprises the following steps: acquiring an initial feature of an image through a classification model, and processing the initial feature by adopting an adaptive pooling layer based on meta-learning in the classification model to obtain a low-resolution image feature; inputting the initial features and the low-resolution image features into a feature extractor, and executing different classification constraint tasks to respectively obtain high-resolution classification features and low-resolution classification features; in the classification model, fusing the low-resolution classification features and the high-resolution classification features to obtain fused features; and transmitting the fusion features into a fusion classifier in the classification model to obtain a visual classification result of the image. According to the method, the adaptive pooling layer is obtained by using the meta-learning strategy, the low-resolution feature most complementary to the high-resolution feature can be selected, and the discriminative feature of the fine-grained image is obtained.

The application relates to a gait recognition network training method and system and a gait recognition method. The gait recognition network training method comprises the following steps: obtaining M groups of training data, one group of training data comprising two gait energy maps; horizontally dividing the gait energy maps into N parts; simultaneously extracting block features of the N parts of the gait energy maps through a basic convolution layer; sending the block features into a second convolution layer for processing; constructing a loss function based on the output results of the convolution layers, and supervising and training the gait recognition network through the loss function. Each image is horizontally divided and subjected to block convolution, so that even if the legs are blocked, the features of other parts are still distinguishable, and therefore the pedestrian gait recognition accuracy can be improved; the two images in each group are spliced, and block convolution is further performed on the spliced features, so that the loss function can make the features more discriminative, thereby improving the recognition accuracy of the model.

The present application relates to the technical field of pedestrian re-identification, and discloses an RGB-D cross-modal pedestrian re-identification method, system and medium based on joint cross attention, which first constructs joint RGB-D semantic representation and alignment processing to realize effective interaction between modes; a multi-stage cross attention learning strategy is introduced to gradually learn the features of each mode at multiple stages, and the interaction between modes at each stage is only realized through cross attention, and only in the last stage, the depth features of the visible light image and the visible light features of the depth image are connected, the intra-modal and inter-modal relationships are gradually modeled, and therefore more robust fused multi-modal pedestrian feature representation is obtained. Finally, the mean-max feature representation of the RGB-D mode is used to construct a joint representation at the semantic level, guide the obtained fine-grained cross-modal interaction features, and realize alignment at the semantic level. Furthermore, the heterogeneity of the cross-modal features is fitted, and more robust multi-modal pedestrian feature representation is obtained.

The present invention relates to the technical field of facial expression recognition of facial images, and in particular to an expression recognition model, method and system combined with facial key point feature fusion. The present invention proposes a training method for an expression recognition model combined with facial key point feature fusion, wherein the facial feature extraction module is a pre-trained model with fixed parameters; the expression feature extraction module, the feature fusion module and the expression classification module constitute the part to be trained of the basic model. During the model training process, only the part to be trained is trained. In the present invention, in a method based on deep learning, the pre-trained facial feature extraction module is used to extract facial features from facial images to improve attention to facial details, thereby reducing the sensitivity of the model to image scale and helping to promote the distinctiveness between samples between classes and the invariance of differences between samples within a class.

The invention discloses a pedestrian dangerous action recognition method based on feature enhancement, which comprises the following steps: acquiring and preprocessing a pedestrian image to be recognized through an input module to obtain initial image data, and designing a foreground enhancement module (FEM), the method can automatically highlight a key area in a worker action feature in a video monitoring scene, and reduces background noise and environmental interference. Compared with an existing method that the features of the whole image are directly input into the network, the method can achieve background suppression in the feature extraction stage, and the motion recognition model can more accurately pay attention to the motion of a worker. The improvement not only improves the accuracy of identification, but also effectively avoids the problems of erroneous judgment and missed judgment caused by a complex background, thereby improving the reliability of industrial safety monitoring.

The present application belongs to the technical field of intelligent video processing, and particularly relates to a future frame anomaly detection method based on meta learning and space-time relationship. The present application method proposes a meta learning module, and a model-based meta learning method enables the model to learn general features from multiple tasks, so that the system can learn more discriminative and generalizable feature representations from data. In different monitoring scenes and abnormal behaviors, the model can also accurately perform anomaly detection without overfitting or underfitting. The present application method introduces the meta learning module into the autoencoder, uses the learning characteristics of the meta learning module to extract, save and update the features extracted by the autoencoder, automatically acquires the feature importance of the input video frame through learning, and assigns important weights to the features that are more worthy of attention, which helps to improve the utilization rate of key features in the input stage of the future frame prediction network.

The invention discloses an indoor line series fault arc detection method based on multi-feature fusion, and the method comprises the steps: collecting a current signal at a main line of an indoor line, carrying out the normalization preprocessing of the current signal, and extracting a time domain, a frequency domain and multiple types of features of the time domain and the frequency domain, so as to comprehensively reflect the non-stationary features of the current signal. For samples under different working conditions, redundant features are removed by using an improved feature selection algorithm in combination with Pearson correlation analysis, and a final feature set with strong discrimination capability is constructed. Furthermore, an extreme learning machine model is introduced, rapid modeling and efficient classification are achieved based on a random initialization weight and a generalized inverse solution method, and high detection precision and real-time performance can be kept under complex working conditions and noise interference. The method has the advantages of being comprehensive in feature expression, efficient in model training, high in detection accuracy and high in robustness, the missing report rate and the false report rate of the indoor line series fault arc can be effectively reduced, and the safety and the reliability of an electrical system are improved.