We propose an effective method, flexible log-polar image (FLPI) to represent quantum images sampled in log-polar
coordinate system. Each pixel is represented by three qubit sequences and the whole image is stored into a normalized
quantum superposition state. If needed, a flexible qubit sequence can be added to represent multiple images. Through
elementary operations, both arbitrary rotation transformation and similarity evaluation can be realized. We also design an
image registration algorithm to recognize the angular difference between two images if one is rotated from the other. It is
proven that the proposed algorithm could get conspicuous improvement in performance.
Swarm intelligence-based image thresholding segmentation algorithms are playing an important role in the research field of image segmentation. In this paper, we briefly introduce the theories of four existing image segmentation algorithms based on swarm intelligence including fish swarm algorithm, artificial bee colony, bacteria foraging algorithm and particle swarm optimization. Then some image benchmarks are tested in order to show the differences of the segmentation accuracy, time consumption, convergence and robustness for Salt&Pepper noise and Gaussian noise of these four algorithms. Through these comparisons, this paper gives qualitative analyses for the performance variance of the four algorithms. The conclusions in this paper would give a significant guide for the actual image segmentation.