  This paper presents a method for analysis of the vote space created from the
local features extraction process in a multi-detection system. The method is
opposed to the classic clustering approach and gives a high level of control
over the clusters composition for further verification steps. Proposed method
comprises of the graphical vote space presentation, the proposition generation,
the two-pass iterative vote aggregation and the cascade filters for
verification of the propositions. Cascade filters contain all of the minor
algorithms needed for effective object detection verification. The new approach
does not have the drawbacks of the classic clustering approaches and gives a
substantial control over process of detection. Method exhibits an exceptionally
high detection rate in conjunction with a low false detection chance in
comparison to alternative methods.
