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The third step detects edge-like features in the EPI. Currently these are positive and negative 
gero-crossings [Marr 1980] of the difference of Gaussians in the plane of the EPI. The zero- 
crossings indicate places in the EPI where there is a sharp change in image intensity, typically 
at surface boundaries or surface markings. Figure 15 shows the features detected in the EPI. 
These are drawn here in cartesian image coordinates, but recall that the analysis maps these 
(knowing the camera principal point and attitude) to lines of sight. Figure 16 shows the . 
representation of these features in the space of lines of sight. 
  
  
  
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Fig. 15. Edge features in EPI Fig. 16. Lines of sight representation 
The fourth step fits linear segments to the lines of sight (actually, to their Euclidean projections). 
It does this in two passes. The first pass partitions the connected lines of sight at sharp corners 
by analyzing curvature estimates along the path. The second pass applies Ramer's algorithm 
[Ramer 1972] to recursively partition the remaining segments into linear pieces. Figure 10 shows 
the line segments derived from the edges in Figure 15. 
The fifth step builds a description of the line segments that links together those that are 
collinear. The intent is to identify sets of lines that belong to the same feature in the world. 
By bridging gaps caused by occlusion, the program can improve its estimates of the features’ 
locations as well as extract clues about the nature of the surfaces in the scene. The dashed 
lines in Figure 10 show those linear features that are linked together. Line intersections indicate 
temporal occlusions. For each intersection, the feature with the smaller slope is the one that 
occludes the other. 
The sixth step computes the x-y-z locations of the world features corresponding to the EPI 
features. The world coordinates are uniquely determined by the location of the epipolar plane 
associated with the EPI and the equation of the feature line in that EPI. To display these 
three-dimensional locations, the program plots the two-dimensional coordinates of the features 
in that particular epipolar plane. Figure 17 shows the epipolar plane coordinates for the features 
shown in Figure 10. The shape and size of each ellipse depicts the error associated with the 
feature’s location (these are determined by the eigenvalues and eigenvectors of the covariance 
matrix of the linear fit). 
  
  
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Fig. 17. (x, z) locations, 99% confidence ellipses Fig. 18. Free space for 2 features 
125