The era of new human-computer interaction has accelerated, and gesture recognition is one of the development trends of human-computer interaction system in the future. The emerging graph neural network can capture the...
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The era of new human-computer interaction has accelerated, and gesture recognition is one of the development trends of human-computer interaction system in the future. The emerging graph neural network can capture the interdependence between instances and infer the complete information of the image based on local features, which is helpful to the recognition of human gestures. Therefore, the combination of graph neural network and convolutional neural network (CNN) is applied to the research of gesture recognition and it has important research significance. This paper uses the Mixup method to perform data enhancement processing on the collected image data set, applies the pyramid pooling method to the EigenPooL model to achieve efficient capture of image features and selects the Sigmoid function and the PReLU function as the activation function of the network model to meet the model's requirements, such as long time training and strong fitting ability. This paper introduces the structure and algorithm of EigenPooL model in detail. The algorithm uses hypergraph learning method instead of simple graph learning method. On the gesture picture test set, the average accuracy of the algorithm is 86.50%, the recall rate is 94.87% and the average detection time per frame is 421 ms.
In the advanced computer vision era, Convolutional Neural Network (CNN) plays a pivotal role in image processing, as they excel at automatically extracting important patterns, and structures, for accurate analysis acr...
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In the advanced computer vision era, Convolutional Neural Network (CNN) plays a pivotal role in image processing, as they excel at automatically extracting important patterns, and structures, for accurate analysis across diverse domains. However, achieving higher accuracy often leads to intensifying computational and timing demands. To address the challenge, this research introduces a novel dual feature extraction methodology. This approach is implemented using two distinct feature extraction modules, employed at different stages of the model: 1) Edge Gradient-Dimensionality Reduction (EGDR) module which encapsulates the extraction of pixel edge gradient features from the raw input frame, leading to a dimensionality reduction by a factor of 0.5;2) Subtle Local Feature Extraction (SLFE) pooling algorithm module, prioritizes the extraction of local and subtle features over maximum or average feature content. The combination of these two stages proves particularly effective in enhancing classification accuracy while minimizing computational overhead and training duration. Subsequently, comprehensive training, validation, and testing were conducted on a selected multi-class chest computed tomography medical image dataset using various state-of-the-art CNN architectures such as VGG-16, InceptionV3, ResNet50 to identify the most suitable model for further experimentation with the proposed method. The proposed CNN-SLFE framework with EGDR module achieved a significant reduction of 17.94% in computational time compared to non-EGDR module, and concurrently enhanced the classification accuracy with an improvement factor of 1.17 compared to existing CNN frameworks with EGDR module.
A ‘pooling sets’ type of algorithm is developed and shown to be valid for computing an isotonic regression function for a general quasi-order. The method is direct and intuitive. The algorithm works best when the qu...
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A ‘pooling sets’ type of algorithm is developed and shown to be valid for computing an isotonic regression function for a general quasi-order. The method is direct and intuitive. The algorithm works best when the quasi-order is complex and the objective function is nearly isotonic. An example is worked out in detail.
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