Selective and focused invariant recognition using distributed associative memories (DAM)

A method of 2-D object recognition based on the Moore-Penrose distributed associative memory is presented. Using known relationships between DAMs and regression analysis, the selectivity of the association weights is improved in an iterative way be discarding from further consideration response vectors deemed to be insignificant. Such selectivity allow the system to focus on the significant associations and to reduce crosstalk effects. The same formalism that allows the significance of the association weights to be computed also provides for a reject option. Experiments incorporating the proposed method onto an invariant recognition system prove the feasibility and benefits of the recognition scheme