期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2000
卷号：XXXIII Part B5/1
页码：299-304
出版社：Copernicus Publications
摘要：Hand posture and gesture recognition integrates sensorial fusion, supervised model-based learning and motion planning. Data acquisition of changing postures in known environments requires the development of intelligent systems based on flexible models which can be updated maintaining properties (incidence and order) of knuckles regarding to phalanxes. An essential characteristic of this model is the modularity that allow us: to identify hand postures based on geometric information, use implicit anato mic and physiological hierarchy of an anthropomorphic three-fin gered hand and finally identify postures with neural fields. In this paper we have selected an approach based in visual inputs only. These visual inputs correspond to a symbolic representation of the skeleton. Classification and interpretation process are controlled in terms of symbolic hybrid models able to integrate geo metric information (meaningful for free obstacle navigation of the artificial hand) with neural fields. Geometric informatio n is relative to node positions (some of them represent knuckles) and segments (representing visible boundaries of phalanxes). Neural fields provide autonomous decision mechanism fro m acquisition and processing of non-linear activation/inhibition processes depending on stimula
