**5. Conclusion**

In this paper, FIS has been presented. The three edge strength values used as fuzzy system inputs were fuzzified using Gaussian membership functions and sigmoid function. Fuzzy ifthen rules are used to modify the membership to one of low, medium, and high classes. Finally, Mamdani defuzzifier method is used to form the final edge image. By the simulation results, it can be concluded that though more computationally expensive than Sobel and LoG operators, the FIS system is superior in greater robustness to detect edge, and also advantage in edge detection exactly and noise removed clearly.

### **6. References**


**13** 

*India* 

**Type-2 Fuzzy Logic for Edge** 

**Detection of Gray Scale Images** 

Abdullah Gubbi and Mohammad Fazle Azeem *Department of Electronics and Communication Engineering* 

Image processing is characterized by a procedure of information processing for which both the input and output are images, such as photographs or frames of video [Jain'86]. Most image processing techniques involve treating the image as a two-dimensional signal and applying standard signal processing techniques to it.[webpage1] Fuzzy Image Processing (FIP) is a collection of different fuzzy approaches to image processing. Nevertheless, the following definition can be regarded as an attempt to determine the boundaries. Fuzzy image processing includes all approaches that understand, represent and process the images, their segments and features as fuzzy sets. [webpage2]The representation and processing of the images depend on the selected fuzzy technique and on the problem to be

The mathematical concepts behind fuzzy reasoning are very simple. Fuzzy logic is a more intuitive approach without the far-reaching complexity. Fuzzy logic is flexible. With any given system, it is easy to layer on more functionality without Starting again from scratch.

In real world everything is imprecise if you look closely enough, but more than that, most of the data which appear to be precise are imprecise after careful inspection. Fuzzy reasoning builds this understanding for the system rather than tackling it onto the end (precise).

Fuzzy logic relies upon the experience of experts who already have familiarity and

A fuzzy system can be created to match any set of input-output data. This process is made particularly easy by adaptive techniques like Adaptive Neuro-Fuzzy Inference Systems

solved [Jang'95]. Here is a list of general observations about fuzzy logic:

*Fuzzy logic is conceptually easy to understand.* 

*Fuzzy logic can be built on top of the experience of experts.* 

*Fuzzy logic can model nonlinear functions of arbitrary complexity.* 

understanding about the functionality of systems.

(ANFIS), which are available in Fuzzy Logic Toolbox.

*Fuzzy logic is tolerant to imprecise data.* 

**1. Introduction** 

*PA College of Engineering, Mangalore, Karnataka* 

