An Image Registration Based On Hierarchic Adaptive Genetic Algorithm

Image registration is one of the basic digital image processing methods , which mainlyregisters two or more digital images , mostly geometrically , obtained at di?erent time , bydi?erent sensors , on di?erent angle of view or on di?erent filming conditions . In recentyears , image registration is studied in many di?erent applying domains , so that it playsa very important role in computer vision , pattern recognition,medical image analysis andremote sensing . Image registration has become an essential part during many studies andresearches.For the general problem of image registration , in case that two image of having o?setrelationship(including translation , rotation , scaling) are waiting for matching image T andthe reference image R , have the following shift relation :The key problem of Image Registration is how to use e?ective methods to evaluate thesimilarity between images and the calculation of transformation parameters . In this paper, I use LTS-Hausdor? distance as the evaluation function , and use Hierarchical adaptivegenetic algorithm to calculate the parameters . This kind of method improve the accuracy ,stability , and e?ciency of Image Registration.LTS(Least Trimmed Square)-Hausdro? distance is combination of the part Hausdor? distance and the average Hausdor? distance . It defined as :H = h×NA , NAis the number of point in the set , h∈[0.6, 0.9] , dB(a)(i) indicate that thei largest distance from point a to point b of set B . This method reduces impact of the noiseand isolated points on the accuracy and stability.Genetic Algorithm base on Darwin's genetic and natural selection calculation model .It is a way to search the optimal solution.This paper combined hierarchical genetic algorithm with adaptive genetic algorithm ,use the high e?ciency of hierarchical genetic algorithm and maintaining diversity of adaptivegenetic algorithm , improve the computation speed.Specifically , in the low-level operations :I make PcandPmcan automatically change with fitness , because the choice of the crossoverprobability P(c) and mutation probability P(m)is the key of impact of algorithm behavior andperformance . It is a direct impact on the convergence of algorithm . The p(c) greater , newindividual generate faster.However , the possibility of Genetic model is damaged is greaterwhen P(c) is too large . High fitness individual structures are destroyed soon ; however , ifP(c) is too small,would slow the search process , as well as stagnation . For the mutationprobability P(m) , If P(m) is too small , it's di?cult to have the structure of a new individual; If P(m) is too large , Genetic Algorithm then becomes a pure random search algorithm . Sowhen the fitness of individual populations of the set tend to line or tended to local optimum ,it makes P(c) and P(m) increased ; when the group fitness scattered , it makes P(c) and P(m)decreased . At the same time , for those group fitness is higher than the average fitness ofthe individual , the solution is be protected to access the next generation ; for those groupfitness is lower than the average fitness of the individual , the solution is swap out . Adaptivegenetic algorithm can help us work out the best P(c) and P(m) .The computing of P(c) and P(m) as follows : fmaxâ†'the largest fitness value in the group ;favgâ†'the average fitness value in each generation ;fâ†'the larger of the two individual to be crossed fitness value ;fâ†'fitness value of individual to varied fitness value。In this case , k1, k2, k3, k4∈(0, 1).In the high-level operations :First of all , it randomly generate N×n(N 2, n 2) samples , they then di-vide into N sub-populations , each sub-population contains n samples , independent ofeach sub-population genetic algorithm to run low-level operations , record them in GAi(i =1, 2,···, N) . It is best to set up sub-population characteristics with a great di?erence , thiscan produce a greater variety of mode .After running to a certain algebraic in each sub-population of low-level genetic algo-rithm , record the results of population in R[1···N, 1···n] , R[i, j](i = 1···N, j = 1···n)indicate the i individual in the result population . At the same time , record the average fitnessof the results population in A[1···N] .High-level genetic algorithm can be divided into the following three steps :1 Selection :Run Selection in array R base on A[1···N] . Some results population is copied becauseof their high average fitness value ; other results population is eliminated because of theirlow average fitness value .2 Crossover :If R[i, 1···n] and R[j, 1···n] are randomly assigned to one , moreover , the crossoveris from the location x , then R[i, x + 1,···, n] and R[j, x + 1,···, n] exchange of the corre-sponding part .3 Mutation :A small number of randomly generated new individuals to be replaced with randomlyselected individual in R[1···N, 1···n] .Thus , the high-level genetic algorithm to run the end of the first round . Genetic Algo- rithm continue to operate in the new R[1···N, 1···n] .According to the above parameters of the optimal solution derived , finish the transla-tion, rotation and stretching transformation .At the last , finish image interpolation using Bi-cubic convolution :...