A brief survey on image segmentation based on quantum inspired neural network. Issue 2 (23rd February 2023) - Pal, Pankaj; Bhattacharyya, Siddhartha; Platos, Jan; Snasel, Vaclav
Information retrieval is a rudimentary approach which is highly solicited for image analysis research. In classical image analysis, image processing recovers the objects from the noisy images in an efficient way at the cost of substantial time complexity. Quantum computation assisted algorithms play a vital role to extract objects from binary, grey, pure or from true colour images using quantum computing principles which reduces the time complexity while improving the segmentation efficiency. These algorithms take recourse to the quantum superposition principle in achieving the task. A neuro-biological network architecture comprises of different nodes corresponding to the image pixels which are converted to the equivalent qubit neurons in the quantum inspired versions. The ability of information retrieval capability from the images by means of qubit neurons is a much-talked affair now-a-day. In this paper, the authors attempt to present a brief survey on image segmentation techniques using the state-of-the-art techniques including quantum inspired neural networks.