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Uma, N.
- Advanced Fuzzy Intuitionistic Logic Techniques in Image Processing
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Authors
P. Rajarajeswari
1,
N. Uma
2
Affiliations
1 Department of Mathematics, Chikkanna Arts College, Tirupur, Tamil Nadu., IN
2 Department of Mathematics, SNR Sons College, Coimbatore, Tamil Nadu., IN
1 Department of Mathematics, Chikkanna Arts College, Tirupur, Tamil Nadu., IN
2 Department of Mathematics, SNR Sons College, Coimbatore, Tamil Nadu., IN
Source
Indian Journal of Computational & Applied Mathematics, Vol 1, No 1 (2013), Pagination: 9-20Abstract
This paper is an attempt of proposing the processing approach of Intuitionistic fuzzy technique in image processing. As Intuitionistic fuzzy sets is a suitable tool to cope with imperfectly defined images, the properties, basic operations, entropy measures, histogram representation, similarity measure, distance measure of the Intuitionistic fuzzy techniques and Intuitionistic fuzzy inference method are presented here.Keywords
Intuitionistic Fuzzy Sets, Image Processing, Entropy, Histogram, Similarity Measure, Distance Measure, Inference Method
References
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- Biochemical Evaluation of Fish Grown in Sewage Water
Abstract Views :192 |
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Authors
Affiliations
1 Sri Avinashilingam Home Science College for Women, Coimbatore-43, IN
1 Sri Avinashilingam Home Science College for Women, Coimbatore-43, IN
Source
The Indian Journal of Nutrition and Dietetics, Vol 20, No 6 (1983), Pagination: 186-189Abstract
Increasing urbanisation culminates in the generation of voluminous domestic waste waters. The potential waste waters in the urban communities of India has been estimated to be of the order of 27,500 million litres per day.- A Similarity Measure Based on Min and Max Operators for Intuitionistic Fuzzy Multi Sets
Abstract Views :295 |
PDF Views:2
Authors
Source
International Journal of Innovative Research and Development, Vol 3, No 5 (2014), Pagination:Abstract
In this paper, we introduce a new similarity measure between two Intuitionistic Fuzzy Multi sets. The proposed method focuses on min and max operators of the membership functions. As this method meets all the properties of the similarity measure, it can be applied to any decision making problems, medical diagnosis, engineering problems, pattern recognition, etc.
