3results about How to "Distinctive features" patented technology

Embodiments of the present application relate to the technical field of human-computer interaction, and disclose a method and device for determining an interactive gesture and an electronic device. The method comprises the following steps: first, target component detection is performed; then, gesture detection and recognition are performed in a local area (around a boundary box) near the target component. In addition, for N gesture detection boxes (multiple users) in a current video frame, M target gesture detection boxes are selected therefrom to perform hand component positioning and gesture recognition processing, which can reduce detection time consumption and improve real-time performance. On this basis, the interactive gesture is determined according to the gesture detection result of the current video frame and the gesture detection results of a plurality of historical video frames, that is, gesture recognition is selectively performed on the user in each frame in a plurality of continuous video frames to determine the interactive gesture, which can reduce the computing power, reduce the time consumption, improve the real-time performance, and improve the accuracy and stability of the interactive gesture. That is, in the multi-user human-computer interaction scene, the interactive gesture can be accurately and timely determined.

The application discloses a kind of based on double filtering cooperation's remote sensing image scene classification method and system, in the method, for the problem that existing technology is difficult to give consideration to background noise suppression and key feature edge structure preservation when processing remote sensing image, a kind of double filtering cooperation optimization module is presented.The module in this paper introduces Gaussian filter smoothing channel to suppress unstructured high-frequency background noise, while introducing Gaussian Laplace edge enhancement channel to accurately capture and strengthen key geometric structure information such as feature contour;Through feature fusion, position coding, state space model and double activation gating mechanism, the multi-scale feature map is optimized and reconstructed to generate high signal-to-noise ratio and clear structure scene representation.The module is integrated into the remote sensing image scene classification model, and trained using focal loss, which can significantly improve the classification accuracy and robustness in complex texture interference scene.