Robustness of Regional Matching Scheme over Global Matching Scheme

Analyzing the effect of concentrated noise on a typical decision-making process of a simplified two-candidate voting model, we have demonstrated that a local approach using a regional matching process is more robust and stable than a direct approach using a global matching process, by establishing that the former is capable of accommodating a higher level of noise than the latter before the result of the decision overturns. To extend the theory to imagery analysis, we pose a conjecture that our conclusion on the robustness of the regional matching processes remains valid not only for the simpler vote counting schemes but also for practically more important decision-making schemes in image analysis which involve dimension-reducing transforms or other features extraction processes such as principal component analysis or Gablor transforms. Two convincing experimental verifications are provided, supporting not only the theory by a white-black flag recognition problem on a pixel-by-pixel basis, but also the validity of the conjecture by a facial recognition problem in the presence of localized noise typically represented by clutter or occlusion in imagery.