Abstract
The accurate description of hand posture plays an important role in the man-machine interaction involved in coordinated assembly. Knuckle image extraction and recognition are of great significance to refine and enrich hand-pose information. These are based on nonparametric density kernel estimation observation sets corresponding to unilateral and bilateral excursion of the hand knuckle gray image. In this paper, sets of pixel positions belonging to the upper- and middle-density intervals are used as two types of image targets. Random clustering and random field multi-classification target modeling are used to learn and estimate the two target distributions of the image. The discriminant field classification learning method is used to fuse the two kinds of target models. A comprehensive representation of the image offset features is obtained. Finally, the knuckle image sample set is used to train the model, and the adaptive threshold is used to identify the hand knuckle image. The results show that the proposed method is feasible.
Highlights
In intelligent manufacturing systems, the development of detection technology with high intelligence and strong environmental adaptability is of great significance to improve production efficiency and enhance the flexibility of manufacturing systems and product quality [1, 2]
Based on the gray-level position data extracted from the nonparametric density kernel estimation results, the probability measure μ~G1jP0 of the offset set belonging to the fixed threshold c in the image domain is learned
According to the nonparametric density kernel estimation result, the image gray position data corresponding to the offset parameter in the selected interval segment are taken as an observation of the random offset image bilateral offset set
Summary
The development of detection technology with high intelligence and strong environmental adaptability is of great significance to improve production efficiency and enhance the flexibility of manufacturing systems and product quality [1, 2]. Based on the gray-level position data extracted from the nonparametric density kernel estimation results, the probability measure μ~G1jP0 of the offset set belonging to the fixed threshold c in the image domain is learned. The number of clusters is described as a random state, and the Gibbs sampling method is used to iteratively study the density structure of the hierarchical probability form under the assumption of the Markov neighborhood. 2.3 Infinite Dirichlet process mixed model based on collapsed Gibbs sampling According to the N observations x 1⁄4 fxigNi1⁄41 of the Dirichlet process mixed model, the hidden variable label zi, the total number of clusters, and the corresponding parameter fθkgKk1⁄41 are inferred. The exact posterior distribution p(π, θ| x) contains the distributions corresponding to all possible category labeling spaces, and it uses a collapsed Gibbs sampling algorithm to implement iterative learning of an infinite clustering mixture model.
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