Transformation parameter estimation using parallel output based neural network

One of the challenging tasks in image registration is to estimate transformation parameters automatically and efficiently. In this paper, we propose a task decomposition based parallel trained neural network to estimate transformation parameters as well as order of transformations. This parameter estimation problem can be divided into several subproblems like rotation, translation and scaling estimation. Each subproblem or module consists of decomposed input datasets, as well as a part of the output vector. Each module is trained in parallel for some specific and fixed input–output vector pattern. Feature vectors are used as input dataset of the proposed neural network. 2D PCA (two dimensional principal component analysis) feature extraction technique is used to build feature vector. This modular technique requires effectively less computation time in comparison to non-modular network. Moreover, this technique can robustly estimate different transformational parameters. The added advantage of this technique is that it can identify order of the transformation. Experimental results justify the effectiveness of the proposed technique.