A Quantum Multilayer Self Organizing Neural Network for Object Extraction from a Noisy Background
Siddhartha Bhattacharyya, Pankaj Pal, Sandip Bhowmik · 2014
Proper extraction of objects from a noisy perspective is an upheaval task in the computer vision research community. Several intelligent research paradigms have been focused on this aspect over the years. Notable among them is the multilayer self organizing neural network (MLSONN) architecture assisted by fuzzy measure guided back propagation of errors. In this article, we propose a quantum version of the MLSONN architecture which operates using single qubit rotation gates. The proposed QMLSONN architecture comprises three processing layers viz., input, hidden and output layers. The nodes of the processing layers are represented by qubits and the interconnection weights are represented by quantum gates. A quantum measurement at the output layer destroys the quantum states of the processed information thereby inducing incorporation of linear indices of fuzziness as the network system errors used to adjust network interconnection weights through a proposed quantum back propagation algorithm. Results of application of the QMLSONN are demonstrated on a synthetic and a real life spanner image with various degrees of Gaussian noise. A comparative study with the performance of the classical MLSONN architecture reveals the time efficiency of the proposed QMLSONN architecture.