Title :
The EM algorithm for multiple object recognition
Author_Institution :
Electrotech. Lab., Ibaraki, Japan
Abstract :
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
Keywords :
normal distribution; object recognition; probability; EM algorithm; attention window; expectation-maximization algorithm; image plane; multiple object recognition; probability density function; weight probabilities; Concrete; Image recognition; Laboratories; Maximum likelihood estimation; Object recognition; Parameter estimation; Probability density function; Probability distribution; Random variables; Shape;
Conference_Titel :
Neural Networks, 1995. Proceedings., IEEE International Conference on
Conference_Location :
Perth, WA
Print_ISBN :
0-7803-2768-3
DOI :
10.1109/ICNN.1995.487742
