Full text: XVIIth ISPRS Congress (Part B5)

   
  
   
  
  
   
  
  
  
  
  
   
  
  
   
    
Scale Space 
Object 
Dependent 
Partitioning 
Viewpoint space 
as 3-D space 
Voxel-Based 
Tessellation 
Aspect Graph 
Algorithms 
Object 
Dependent 
Partitioning 
Viewpoint space 
as viewing sphere 
Uniform 
Tessellation | 
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| | General | Eggert, Bowyer, Dyer, 
Polygons Christensen & Goldgof, 1992 
Deformable s 
Cylinders Wilkins, Goldgof & Bowyer, 1991 
Articulated Sallam, Stewman & Bowyer, 1990 
Assemblies Eggert, Sallam & Bowyer, 1992 
Solids of Eggert, 1991 
Revolution Eggert & Bowyer, 1992 
Plantinga & Dyer, 1987, 1990 
General | Stewman & Bowyer, 1988 
Polyhedra Stewman, 1991 
C Plantinga & Dyer, 1987, 1990 
COT SC d | Stewman & Bowyer, 1990 
y Watts, 1988 
General Watts, 1988 
Polygons 
Convex | Gualtieri, Baugher & Werman, 1989 
Polygons 
General 
H—— Polyhedra | Wang & Freeman, 1990 
General Ponce & Kriegman, 1990 
Curved Petitjean, Ponce & Kriegman, 1992 
Rieger, 1990, 1992 
Surface Sripradisvarakul & Jain, 1989 
Quadric | Chen & Freeman, 1991 
Surface 
Solids of | Eggert & Bowyer, 1990 
Revolution Kriegman & Ponce, 1990 
Gigus, Canny & Seidel, 1991 
General Gigus & Malik, 1990 
Polyhedra Plantinga & Dyer, 1987, 1990 
Seales & Dyer, 1990 
Convex | Plantinga & Dyer, 1986 
Polyhedra 
2.5-D Castore, 1984 
Polyhedra Castore & Crawford, 1984 
  
Goad, 1983 
Fekete & Davis, 1984 
Hebert & Kanade, 1985 
Korn & Dyer, 1987 
Ikeuchi, 1987 
Burns & Kitchen, 1988 
Shapiro, 1988 
Chen & Kak, 1989 
Hansen & Henderson, 1989 
Hutchinson & Kak, 1989 
Camps, Shapiro & Haralick, 1991 
Dickinson, Pentland & Rosenfeld, 1992 
Raja & Jain, 1992 
  
Figure 1. Classification of algorithms for aspect graph creation. Algorithms are categorized by model of viewpoint space, 
method of creation and class of object shape allowed. Cited references are in the bibliography. 
2.1 Models of Viewpoint Space 
  
The model of viewpoint space has perhaps had the greatest 
effect on the various algorithms. Two basic models of view- 
point space are commonly used. One is the viewing sphere. 
In this model, the space of possible viewpoints is the surface 
of a unit sphere, defining a 2-D parameter space. The sphere 
is considered to be centered around a model of the object, 
which is located at the origin of the coordinate system. A 
viewpoint on the surface of the sphere defines a line of sight 
vector from the viewpoint toward the origin. This direction 
vector is usually used to create an orthographic projection 
view of the object. It is possible to use perspective pro- 
jection with the viewing sphere model, but this requires an 
assumption of a known viewer-to-object distance. 
A more general model of viewpoint space is to consider all 
positions in 3-D space as possible viewpoints. As in the case 
of the viewing sphere, the object can be considered to be 
located at the origin of the coordinate system. Specifying 
a direction vector for the line of sight and a focal length 
for the imaging process allows the creation of a perspective 
projection view of the object. This normally would require 
potentially a 7-D parameter space to describe the viewing 
process. However, a simplifying assumption is made such 
that an aspect is concerned with all potential features seen 
from a given viewpoint, if the line of sight is directed ap- 
propriately. Thus only three parameters are necessary to 
specify the viewpoint position, and the viewer will possess 
a 360? field of view in all directions. Later, during the pose 
estimation portion of the object recognition task, the exact
	        
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