| (1) Configuration |
| (a) Set the parameters HMS, HMCR, PAR, BWmin, BWmax and NI. |
| (2) Initial population. |
| (a) Build the harmony memory (HM) where each individual |
| consists 4 random non-repeating indices from 1 to . |
| (b) Compute homography (hypothesis ) by using the indices from . |
| (c) Calculate the fitness value as the matching quality of the constructed homography |
| considering the whole available data . Such fitness value is calculated by using a |
| new objective function defined as: |
| , (A) |
| where represents the quadratic error produced by the th correspondence |
| considering the hypothesis whereas is the penalty associated with the mismatch |
| magnitude. Such error corresponds to the mismatch generated by the evaluation of . |
| (3) Iterations . |
| (a) Generate a new harmony (candidate solution) as follows: |
| |
| for ( = 1 to ) do |
| if ( < HMCR) then |
| Select randomly a number where |
| = |
| if ( < PAR) then |
| where |
| end if |
| if |
| |
| end if |
| if |
| |
| end if |
| else |
| , where |
| end if |
| end for |
| (b) Compute homography by using the indices from . |
| (d) Calculate the fitness value as the matching quality of the constructed |
| homography considering the whole available data . Such fitness value is |
| calculated by using the objective function described in (A). |
| (e) Update the HM as if |
| (4) Estimation result |
| (a) The best estimation consists of the parameters computed by using the indices from |
| the best element of HMin terms of its affinity, so that . |
