We encounter point pattern matching problems in such diverse fields as machine vision, astronautics, document processing, computational biology and computational chemistry.
In machine vision this can be used to match corners and junction extracted by some detectors from pairs of stereoscopic images in order to reconstruct a tri-dimensional scene.
Branch and bound algorithm
The goal is to find the affine transformations(rotation and translation) permitting to map one point set (A) on the other (B)(obtained by feature extractors such as corner and junction detectors).
 It is used to find all possible solutions available to the problem.
 It traverse tree by DFS(Depth First Search).
 It realizes that it has made a bad choice & undoes the last choice by backing up.
 It search the state space tree until it found a solution.
 It involves feasibility function.
 It is used to solve optimization problem.
 It may traverse the tree in any manner, DFS or BFS.
 It realizes that it already has a better optimal solution that the pre-solution leads to so it abandons that pre-solution.
 It completely searches the state space tree to get optimal solution.
 It involves bounding function.