Abstract: A gesture recognition method, a gesture recognition device, an electronic device and a computer-readable storage medium are provided. The gesture recognition method includes steps: obtaining a reference feature vector set including M gesture categories and a gesture category set including M reference feature vectors, where each of the reference feature vectors is obtained by performing vector fusion on initial feature vectors of N sample images of each of the gesture categories, and the initial feature vectors are obtained by performing hand feature extraction on the sample images; performing hand feature extraction on the image to be recognized to obtain a gesture feature vector; and determining a target gesture category of the image to be recognized based on similarities between the gesture feature vector and the M reference feature vectors.

Abstract: An array substrate has a hole-opening region, a display region surrounding the hole-opening region, and a hole-peripheral region located between the hole-opening region and the display region. The array substrate includes a substrate and a driving circuit layer located on the substrate. The driving circuit layer includes a constant-voltage conductive ring and a plurality of signal lines. At least some of the plurality of signal lines are target signal lines. The target signal lines each include a lead portion and a winding portion connected thereto. The lead portion extends from the hole-peripheral region to the display region, and the winding portion is located in the hole-peripheral region and is disposed along an edge of the hole-opening region. The constant-voltage conductive ring is located in the hole-peripheral region and is disposed around the hole-opening region. The winding portion is located between the constant-voltage conductive ring and the hole-opening region.

Abstract: A ground extraction method for 3D point clouds of outdoor scenes based on Gaussian process regression, including: (1) obtaining the 3D point cloud of an outdoor scene, (2) building the neighborhood of the 3D point cloud, (3) calculating the covariance matrices and normal vectors of the 3D point cloud, (4) classifying the 3D point cloud according to its neighborhood shape, (5) extracting the initial ground Gs, (6) segmenting the initial ground, (7) 2D Gaussian process regression, (8) finding the neighborhood NLGk of each ground fragment LGk, and (9) extracting the final ground Ge.