  ORCEA is a novel object recognition method applicable for objects describable
by a generative model. The primary goal of ORCEA is to maintain a probability
density distribution of possible matches over the object parameter space, while
continuously updating it with incoming evidence; detection and regression are
by-products of this process. ORCEA can project primitive evidence of various
types (edge element, area patches etc.) directly on the object parameter space;
this made possible by the study phase where ORCEA builds a probabilistic model,
for each evidence type, that links evidence and the object-parameters under
which they were created. The detection phase consists of building the joint
distribution of possible matches resulting from the set of given evidence,
including possible grouping to signal/noise; no additional algorithmic steps
are needed, as the resulting PDF encapsulates all knowledge about possible
solutions. ORCEA represents the match distribution over the parameter space as
a set of Gaussian distributions, each representing a concrete probabilistic
hypothesis about the object, which can be used outside its scope as well. ORCEA
was tested on synthetic images with varying levels of complexity and noise, and
shows satisfactory results.
