In this dissertation, several GA-based approaches for fractal image compression and active contour model are proposed. The main drawback of the classical fractal image compression is the long encoding time. Two methods are proposed in this dissertation to solve this problem. First, a schema genetic algorithm (SGA), in which the Schema Theorem is embedded in GA, is proposed to reduce the encoding time. In SGA, the genetic operators are adapted according to the Schema Theorem in the evolutionary process performed on the range blocks. We find that such a method can indeed speedup the encoder and also preserve the image quality. Moreover, based on the self-similarity characteristic of the natural image, a spatial correlation genetic algorithm (SC-GA) is proposed to further reduce the encoding time. There are two stages in the SC-GA method. The first stage makes use of spatial correlations in images for both the domain pool and the range pool to exploit local optima. The second stage is operated on the whole image to explore more adequate similarities if the local optima are not satisfactory. Thus not only the encoding speed is accelerated further, but also the higher compression ratio is achieved, because the search space is limited relative to the positions of the previously matched blocks, fewer bits are required to record the offset of the domain block instead of the absolute position. The experimental results of comparing the two methods with the full search, traditional GA, and other GA search methods are provided to demonstrate that they can indeed reduce the encoding time substantially. The main drawback of the traditional active contour model (ACM) for extracting the contour of a given object is that the snake cannot converge to the concave region of the object under consideration. An improved ACM algorithm is proposed in this dissertation to solve this problem. The algorithm is composed of two stages. In the first stage, the ACM with traditional energy function guides the snake to converge to the object boundary except the concave regions. In the second stage, for the control points which stay outside the concave regions, a proper energy template are chosen and are added in the external energy. The modified energy function is applied so as to move the snake toward the concave regions. Therefore, the object of interest can be completely extracted. The experimental results show that, by using this method, the snake can indeed completely extract the boundary of the given object, while the extra cost is very low. In addition, for the problem that the snake cannot precisely extract the object contour when the number of the control points on the snake is not enough, a GA-based ACM algorithm is presented to deal with such a problem. First the improved ACM algorithm is used to guide the snake to approximately extract the object boundary. By utilizing the evolutionary strategy of GA, we attempt to extract precisely the object boundary by adding a few control points into the snake. Similarly, some experimental results are provided to show the performance of the method.
| Identifer | oai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0101107-235955 |
| Date | 01 January 2007 |
| Creators | Wu, Ming-Sheng |
| Contributors | Tsu-Tian Lee, Juhng-Perng Su, Jyh-Horng Jeng, Tsung-Ying Sun, Jeng-Yih Juang, Jer-Guang Hsieh |
| Publisher | NSYSU |
| Source Sets | NSYSU Electronic Thesis and Dissertation Archive |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0101107-235955 |
| Rights | withheld, Copyright information available at source archive |
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