8 results about "Visual Pattern Recognition" patented technology

The invention provides an intraoperative gauze counting and tracking method based on visual perception, and relates to the technical field of visual pattern recognition. A collaborative perception framework integrating multispectral physical fingerprint recognition, visual space-time tracking and adaptive entangled state filtering is constructed; the core problem of target identity confirmation and persistent tracking in the prior art is solved. High-robustness fluorescent fingerprints are introduced to serve as identity anchor points, deep coupling and mutual correction of physical identities and spatial-temporal trajectories are achieved through an adaptive fusion algorithm, and finally absolute identity confirmation and high-precision and high-robustness continuous tracking of each target are achieved. According to the method, the recognition accuracy in a seriously polluted and shielded environment is improved, error accumulation in a long-term tracking process is effectively inhibited, and the self-adaptive capability and reliability of the whole system in a dynamic complex scene are enhanced.

The invention provides a ship power system intelligent monitoring and early fault diagnosis method and system based on multi-modal image recognition. The method comprises the following steps: S1, constructing a three-dimensional digital twin base model of monitored power equipment; s2, multiple types of operation time sequence signals of the monitored power equipment are collected; s3, converting the acquired at least one operation time sequence signal into a two-dimensional feature image; s4, mapping and fusing the two-dimensional feature image and a parameterized data stream generated based on the operation time sequence signal to a corresponding spatial position of the three-dimensional digital twin base model, and generating a fused visual training picture used by a machine learning model; and S5, training a visual AI model by using the generated fusion visual training picture, and performing state recognition and fault diagnosis on the fusion visual training picture generated in real time by using the trained model. According to the invention, a complex equipment state monitoring problem is converted into a visual mode identification problem, and ship power equipment monitoring and early, accurate and automatic fault diagnosis are realized.

This invention provides a visual perception-based method for intraoperative gauze counting and tracking, belonging to the field of visual pattern recognition technology. By constructing a collaborative perception framework integrating multispectral physical fingerprint recognition, visual spatiotemporal tracking, and adaptive entangled state filtering, this invention solves the core challenges of existing technologies in target identification and persistent tracking. By introducing highly robust fluorescent fingerprints as identity anchors and utilizing an adaptive fusion algorithm to achieve deep coupling and mutual correction between physical identity and spatiotemporal trajectory, absolute identification of each target and high-precision, highly robust continuous tracking are ultimately achieved. This improves the identification accuracy in severely polluted and occluded environments, effectively suppresses error accumulation during long-term tracking, and enhances the adaptive capability and reliability of the entire system in dynamic and complex scenarios.

This invention discloses a robust event-driven gait recognition method, system, device, and storage medium based on event flow, relating to the fields of event vision, computer vision, pattern recognition, and intelligent security technologies. The method includes renormalizing spatial displacement, temporal displacement, and edge length according to a unified scale and robustness scale, recalculating edge attributes after each pooling, introducing a motion intensity index to assist in determining edge reliability, employing continuous reweighting instead of direct edge deletion, and using motion consistency, radius validity, orientation validity, and entropy constraints to weakly supervise edge confidence. A graph convolutional backbone network is used to extract spatial graph features for each time slice, and temporal relationships are jointly modeled through difference and similarity branches. The additive angular interval loss and dynamic center loss are jointly optimized. This invention improves message passing stability, enhances anti-disturbance robustness, overcomes intra-class fluctuations such as cross-viewpoint and low-light conditions, and possesses excellent potential for edge device deployment.

The invention provides a passenger vehicle passenger emotion judgment method and system based on video monitoring, and belongs to the technical field of computer vision, mode recognition and artificial intelligence, and the system comprises a video / audio collection module, a modal confidence estimation sub-module, a modal time alignment module, a feature extraction module, and a modal fusion and emotion judgment module. A deep neural network is adopted for fusion, and interaction and weighted fusion between different modal features are achieved; and finally, inputting the fused feature vector into a classifier, and outputting the emotion category of the passenger and the corresponding confidence coefficient by the classifier. According to the method, the facial expressions and the body language features of the passengers are comprehensively analyzed through the multi-modal fusion technology, and the voice features can be selectively combined, so that real-time and accurate judgment on the emotion of the passengers in the passenger vehicle is realized.

The invention provides a multi-scale feature alignment method based on a Wasserstein distance, and the method comprises the steps: constructing a feature space mapping relation, calculating the Wasserstein distance through employing an improved Sinkhorn iterative algorithm, and carrying out the feature alignment optimization in combination with a self-adaptive step length strategy and entropy regularization constraint. By adopting the method, compared with the prior art, the feature alignment precision is improved by 85.1%, the processing speed is improved by 73.1%, and the alignment precision of 95.6% is still kept under 20% noise interference. The method has the technical effects of high calculation efficiency, excellent alignment precision and strong anti-noise capability, and is suitable for feature alignment tasks in the fields of computer vision, pattern recognition and the like.

The application provides a pantograph and catenary arc visual detection method, and belongs to the technical field of computer vision pattern recognition and target detection. A four-stage feature down-sampling module built by using a self-attention mechanism models the global context of an image at each stage, captures the global features of a target and models long-range semantic dependency relationships, and aggregates features and position information from the entire input domain. Meanwhile, the encoder and the decoder are connected through a skip layer connection mode, the low-dimensional shallow semantic information at each stage in the down-sampling is fused with the high-dimensional deep semantic information at each stage in the up-sampling, the detection precision is improved, and the data requirement is significantly reduced. The multi-dimensional global feature fusion network is trained by using a training data set and a stochastic gradient descent method, and the serialized image features are first gradually extracted by four cascaded self-attention modules in the encoder to obtain high-dimensional deep features, and four features with different dimensions are generated. The method is used for pantograph and catenary arc visual automatic detection.