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The EM algorithm for multiple object recognition

Proposes a mixture model that can be applied to the recognition of multiple objects in an image plane. The model consists of any shape of modules; each module is a probability density function of data points with scale and shift parameters, and the modules are combined with weight probabilities. The author presents the EM (Expectation-Maximization) algorithm to estimate those parameters. The author also modifies the algorithm in the case that data points are restricted in an attention window.