Gonzalo-Tasis, Margarita 
SYMBOLIC MODELSFOR POSTURES RECOGNITION OF A THREE FINGERED ARTIFICIAL 
HAND 
M. Gonzalo-Tasis, R. Pellón, D. Sánchez and J. Finat 
MOBiVA- Group of Advanced Visualization, Dept. of Computer Science, Univ. Of Valladolid, Spain 
marga@infor.uva.es, rpellon@tid.es, dsanchez(@ gmv.es, ifinat@nava.tel.uva.es 
Working Group V/4 
KEY WORDS: Image Understanding, Model-based processing 
ABSTRACT 
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 anatomic and physiological hierarchy of an anthropomorphic three-fingered 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 geometric 
information (meaningful for free obstacle navigation of the artificial hand) with neural fields. Geometric information is 
relative to node positions (some of them represent knuckles) and segments (representing visible boundaries of 
phalanxes). Neural fields provide autonomous decision mechanism from acquisition and processing of non-linear 
activation/inhibition processes depending on stimula. 
1 INTRODUCTION 
Traditional algebro-geometric methods are based in a minimal set of points necessaries to identify postures, (seven 
points [LK95] or six points in [MC97]). Nevertheless, their efficiency from a mathematical viewpoint is very sensitive 
to partial occlusions due to grasping or self-occlusions that arise from motions of an articulated hand. Instead, we work 
with redundant information acquired from a higher number of control points; resulting systems are overdetermined and 
it is necessary optimize these systems to solve them. The optimization process is not easy, it is essential to work with 
flexible data structures that can be able to integrate mobile points and segments. 
Our approach is simple; it is based on a low-level identification of grouped segments verifying metric conditions for 
pose estimation and incidence conditions for invariants detection, that reinforces postures identification. 
Posture recognition for an artificial model can be understood as a mapping between a subset of meaningful features of 
the articulated hand and some model features stored in some database. Hence, the process begins with a descending 
high-level supervised geometric model simulated with OPEN/GL. Next, we use a low-level processing to extract 
meaningful geometric features to be compared (points, segments, or both) and their incidence properties. Finally, we 
identify the nearest mechanical events corresponding to flexioned / extended phalanxes that act as possible attractors for 
evolving postures relative to each finger. This information is symbolically stored following a simplified mechanical 
version of aspect graphs, where each simple mechanical event corresponds to an interchange between an extension and 
a flexion for each knuckle of each finger. 
The states of the system are generated in symbolic representations by extracting information from discontinuities of 
pixel functions. Also, with aprioristic knowledge based in model, it can be obtained invariant geometric and topologic 
descriptions. This qualitative information about states is utilized as coarse reference models working as attractors and 
thus, it must be invariant for the acquired representation. 
  
International Archives of Photogrammetry and Remote Sensing. Vol. XXXIII, Part B5. Amsterdam 2000. 299