**7. Conclusion**

The problem of image processing and edge detection under fuzziness and uncertainty has been considered. The role of fuzzy logic in representing and managing the uncertainties in these tasks was explained. Various fuzzy set theoretic tools for measuring information on grayness ambiguity and spatial ambiguity in an image were discussed along with their characteristics. Some examples of edge detection, whose outputs are responsible for the overall performance of a recognition (vision) system, were considered in order to demonstrate the effectiveness of these tools in providing both soft and hard decisions. Gray information is expensive and informative. Once it is thrown away, there is no way to get it back. Therefore one should try to retain this information as long as possible throughout the decision making tasks for its full use. When it is required to make a crisp decision at the highest level one can always throw away or ignore this information. The significance of retaining the gray information in the form of class membership for soft decision is evident. Uncertainty in determining a membership function in this regard and the tools for its management were also stated. Finally a few real life applications of these methodologies are described.

The proposed technique used fuzzy if then rules are a sophisticated bridge between human knowledge on the one side and the numerical framework of the computers on the other side, simple and easy to understand. To achieve a higher level of image quality considering the subjective perception and opinion of the human observers.

