ImageSegmentationAlgorithmBasedonQuantumImmuneProgramming.docVIP

PAGE 6 Image Segmentation Algorithm Based on Quantum Immune Programming Xiaojun Bi Guifang JinCollege of Information and Communications EngineeringCollege of Information and Communications EngineeringHarbin Engineering UniversityHarbin Engineering UniversityHarbin, Heilongjiang Province, China Harbin, Heilongjiang Province, China  bixiaojun@ harbinkcf@  Abstract -A quantum immune programming is presented by combining quantum theory with the clonal selection and immune mechanism in artificial immune system. Based on the thresholding image segmentation principle, the quantum immune programming is applied in the optimization of threshold value of grey image segmentation. The simulation of testing image segmentation proves its superiority. Index Terms – immune programming, quantum algorithm, image segmentation. I. INTRODUCTION Image segmentation, an important step in detecting and identifying objects, remains an old complex problem with wide perspective. The threshold method based on maximal entropy which can be regarded as an optimal problem is a simple and practical one among various image segmentation methods. Evolutionary Algorithm (EA) is an effective method in solving optimization problems for its great robustness and parallelism. However, it is proved that EA has some disadvantages such as torpid convergence, poor effect and prematurity for the randomness of evolution. To deal with the complexity and huge computation in image segmentation, the quantum chromosome in Quantum Computing (QC) is introduced in this paper. And the crossover and mutation operator characterized of QC are constructed to quicken up convergence speed and ensure global astringency. In order to take full advantage of the highly effective parallel of QC, we adopt the clone select and immune mechanism of artificial immune system (AIS) to establish a nove