15-10-2010, 05:02 PM
This article is presented by:
M. Hari Krishnan
R. Viswanathan
Abstract
In this paper, we propose an alternative scheme to crisp image processing algorithms, especially when subjective or very sensitive parameters or concepts related to the image need to be measured or defined. It involves an image fuzzification function,fuzzy operators and an optional defuzzification function. The Applicability of the scheme is illustrated in three applications,image binarization,edge detection and geometric measurements. This paper also attempts to formulate a mathematical model for a fuzzy image processing approach to provide a guidance to perform fizzy image processing and also applications of fuzzy logic in the development of image processing.
Introduction
Image processing changes the nature of an image to make it appear sharper. It uses in applications of medicine, product quality, astronomy, remote sensing, national security autonomus system and industrial applications. [1]Image processing algorithms require modelling of complex systems, which require processing of information with high degree of uncertainty and subjectivity concepts like brightness, edges, uniformity, measurements etc. [2]The concepts related to image analysis contains a certain amount of uncertainty. Due to the uncertainty present on object edges, the decision whether a pixel belong to the background or to the object is nontrivial. Results of crisp based algorithm are not sufficient. So the new development is needed. Fuzzy techniques are very much useful in the development of new algorithms. The fuzzy technique is an operator which is to simulate at a mathematical level the compensatory behavior in the process of decision making or subjective evaluation. So the incorporation of fuzzy logic in to the development of image processing and analysis opened a research area in the image processing field. [3- 5]Fuzzy logic allows one to model uncertainty and subject concepts in a better form than certainty models.
For more information about this article,please follow the link:
http://ripublicationafm/afmv5n1_7.pdf
M. Hari Krishnan
R. Viswanathan
Applications of Advanced Fuzzy Logic Techniques in
Fuzzy Image Processing Scheme
Fuzzy Image Processing Scheme
Abstract
In this paper, we propose an alternative scheme to crisp image processing algorithms, especially when subjective or very sensitive parameters or concepts related to the image need to be measured or defined. It involves an image fuzzification function,fuzzy operators and an optional defuzzification function. The Applicability of the scheme is illustrated in three applications,image binarization,edge detection and geometric measurements. This paper also attempts to formulate a mathematical model for a fuzzy image processing approach to provide a guidance to perform fizzy image processing and also applications of fuzzy logic in the development of image processing.
Introduction
Image processing changes the nature of an image to make it appear sharper. It uses in applications of medicine, product quality, astronomy, remote sensing, national security autonomus system and industrial applications. [1]Image processing algorithms require modelling of complex systems, which require processing of information with high degree of uncertainty and subjectivity concepts like brightness, edges, uniformity, measurements etc. [2]The concepts related to image analysis contains a certain amount of uncertainty. Due to the uncertainty present on object edges, the decision whether a pixel belong to the background or to the object is nontrivial. Results of crisp based algorithm are not sufficient. So the new development is needed. Fuzzy techniques are very much useful in the development of new algorithms. The fuzzy technique is an operator which is to simulate at a mathematical level the compensatory behavior in the process of decision making or subjective evaluation. So the incorporation of fuzzy logic in to the development of image processing and analysis opened a research area in the image processing field. [3- 5]Fuzzy logic allows one to model uncertainty and subject concepts in a better form than certainty models.
For more information about this article,please follow the link:
http://ripublicationafm/afmv5n1_7.pdf
