  The paper has established and verified the theory prevailing widely among
image and pattern recognition specialists that the bottom-up indirect regional
matching process is the more stable and the more robust than the global
matching process against concentrated types of noise represented by clutter,
outlier or occlusion in the imagery. We have demonstrated this by analyzing the
effect of concentrated noise on a typical decision making process of a
simplified two candidate voting model where our theorem establishes the lower
bounds to a critical breakdown point of election (or decision) result by the
bottom-up matching process are greater than the exact bound of the global
matching process implying that the former regional process is capable of
accommodating a higher level of noise than the latter global process before the
result of decision overturns. We present a convincing experimental verification
supporting not only the theory by a white-black flag recognition problem in the
presence of localized noise but also the validity of the conjecture by a facial
recognition problem that the theorem remains valid for other decision making
processes involving an important dimension-reducing transform such as principal
component analysis or a Gabor transform.
