X 2 vision system for feature detection and information combination
E-ren Chuang, David B. Sher · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1992
A statistical vision system is proposed for feature detection and evidence combination. It has been successfully applied to locating segments and polygons in images. Each feature is modeled by a random vector X with a multivariate normal distribution, denoted by X approximately N((mu) X, (Sigma) x). After the transformation f(X): (X - (mu) x)t(Sigma) x-1(X - (mu) x), this model becomes a random variable (rv) with (chi) 2 distribution, then (chi) 2 test is applied to measure the similarity between data and the expectation vector of each model. Multiple statistics from the tests of local features, such as edges and corners, are combined by summation into statistics for large features such as segments and polygons. This is justified because the sum on a set of independent (chi) 2 random variables is also a (chi) 2 random variable, and the geometric meaning of the sum is equal to the integration of these addends. Therefore, information is coherently combined by summation and (chi) 2 tests are consistently applied throughout this vision system for feature detection.