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### Tree Search Methods

Basic approach:

• Nodes of the tree represent a scene to model primitive (e.g. edge, surface) match.
• Let Tree have m branches at each node that correspond to model primitives.
• Let level of tree represent a model primitive.
• The level and node position specify a match pair.
• Use a depth first tree search to find a match.
• One traversal through tree gives a match list -- a possible match of scene to model features.
• Representation called a search or interpretation tree. Fig. 51 Interpretation tree

How do we perform matching?

• At each level of the tree, one of the edges from the scene is matched with each of the m possible edges in the model.
• Each node has m children representing taking the match proposed so far together with all possible matches for the current scene edge.
• A transformation representing the match so far can be maintained.
• Search tree in depth first manner and pruned by rejecting interpretations that fail to satisfy current match.
• The search space is large -- possible combinations for n scene primitives.
• A lot of computations?

We can reduce the computational overheads by employing some local geometric constraints to prune the tree further.

These are:

• Cheap to compute and employ.
• applied before the transformation test.

For Edges we could employ:

Distance Constraint
-- The length of the sensed edge must be less than or equal to the length of the model edge under consideration.
Angle Constraint
-- The angle between two adjacent sensed edges must agree with that between the two corresponding matched model edges.
Direction Constraint
-- Let represent the range of vectors from any point on sensed edge a to any point on sensed edge b. In an interpretation which respectively pairs sensed edges a and b with model edges i and j, this range of vectors must be compatible with the range of vectors produced by i and j.

For Surface we could employ:

• Angles between planes.
• Area of planes.
• Invariants.
• Measures of curvature.   Next: Relaxation Labelling Methods Up: Model Based Recognition Previous: Model Based Recognition David Marshall 1994-1997