Input and output mapping sensitive auto-associative multilayer perceptron for computer interface system based on image processing of laser pointer spot

In this paper, we propose a new auto-associative multilayer perceptron (AAMLP) that properly enhances the sensitivity of input and output (I/O) mapping by applying a high pass filter characteristic to the conventional error back propagation learning algorithm, through which small variation of input feature is successfully indicated. The proposed model aims to sensitively discriminate a data of one cluster with small different characteristics against another different cluster's data. Objective function for the proposed neural network is modified by additionally considering an input and output sensitivity, in which the weight update rules are induced in the manner of minimizing the objective function by a gradient descent method. The proposed model is applied for a real application system to localize laser spots in a beam projected image, which can be utilized as a new computer interface system for dynamic interaction with audiences in presentation or meeting environment. Complexity of laser spot localization is very wide, therefore it is very simple in some cases, but it becomes very tough when the laser spot area has very slightly different characteristic compared with the corresponding area in a beam projected image. The proposed neural network model shows better performance by increasing the input-output mapping sensitivity than the conventional AAMLP.