Abstract
Abstract
Edge is one of the most important features of an image because it recognizes the world through images and understands the position and contour of objects in images through edges. Edge monitoring of depth image is the basis of object recognition and location. The contour curve calculates the feature points and maps them to the original contour space. The original contour is matched by a sequence of markers. At the initial stage of recognition, the dissimilar targets and different gestures are eliminated quickly. The candidate list of targets is generated for accurate matching to improve the recognition efficiency. The curve is a nonlinear extension of the first principal component, emphasizing the search for the “middle” through the distribution of data, starting from an arbitrary point of the image, estimating the pixel points near the point and obtaining the gradient value. If the point satisfies the characteristics of the edge point, then the point detour depth traverses to find edge points that are perpendicular to the direction of the gradient and marks the visited points. And satisfy the self-consistent smooth curve without parameters, these curves give an overview of the data. Determine the degree of approximation; when the judgment has converged to the target contour, continue to converge and penetrate the concave area of the target contour.