The Application Of MEP In Image Registration

This thesis mainly divides into three parts. The first part for the research background introduction produced the GP developing process: from non-linear GP (genetic programming) to linear GP and introduced the GEP (Gene Expression Programming) biology background, the method, the research present situation in detail. The second part for the MEP (Multi-Expression Programming) algorithm research, emphatically introduced the MEP encoding method and the basic flow, and carried on the analysis to it. In this foundation, we proposed the improvement MEP algorithm and did the detailed numerical experimentation to this algorithm. The third part for the MEP algorithm application practice, applied the proposed algorithm to the image registration, compared and the results between the new and the traditional method, finally demonstrated our algorithm displaying good performance. Our main work and innovation as follows:1. We have conducted the research to the MEP unique encoding method and the corresponding evolutionary operation. Compared with other GP, MEP has many merits, such as encompassing many expressions, effectively using codes, no useless codes, without needing to transform to the tree structure, and safeguarding well on good sub- structure.2. We have made the improvement to the MEP deficiency. For the search space in MEP and other GPs is too large and unable to carry on the valid search, this thesis proposed different composite function templates and graduation strategies in view of the different questions. Thus this way reduced the useless search space, enhanced the search efficiency. With some test examples to the improvement MEP algorithm, the experimental results indicated that the improvement MEP algorithm is valid.3. We have utilized the improved MEP algorithm to the image matching question. This thesis has first summarized the image matching question, the significance of image matching, the transformation type, the matching process based on the control point and so on. After analyzing advantages and disadvantages on the existing model, we gave the detailed process about the improvement MEP algorithm applying on the image matching question, did the experiment, andobtained the good result. This is so far the first time utilizing the MEP algorithm to the image matching question.4. We have realized the improvement MEP algorithm software by designing the software frame and the essential algorithm codes.