Journal Article10.1016/0167-8655(90)90006-N
Fuzzy thresholding: mathematical framework, bound functions and weighted moving average technique
C. A. Murthy,S. K. Pasl +1 more
TL;DR: The variation in membership function is seen to be restricted by bound functions, thus enabling the method of segmentation more flexible but effective and can be viewed as a weighted moving average technique, greyness ambiguity being the weights.
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About: This article is published in Pattern Recognition Letters. The article was published on 01 Mar 1990. The article focuses on the topics: Membership function & Thresholding.
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Citations
Survey over image thresholding techniques and quantitative performance evaluation
Mehmet Sezgin,Bulent Sankur +1 more
TL;DR: 40 selected thresholding methods from various categories are compared in the context of nondestructive testing applications as well as for document images, and the thresholding algorithms that perform uniformly better over nonde- structive testing and document image applications are identified.
A review on image segmentation techniques
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TL;DR: Attempts have been made to cover both fuzzy and non-fuzzy techniques including color image segmentation and neural network based approaches, which addresses the issue of quantitative evaluation of segmentation results.
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Image thresholding using type II fuzzy sets
TL;DR: A new thresholding technique is introduced which processes thresholds as type II fuzzy sets and a new measure of ultrafuzziness is also introduced and experimental results using laser cladding images are provided.
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A local fuzzy thresholding methodology for multiregion image segmentation
TL;DR: A new multiregion thresholding methodology is presented to overcome the common drawbacks of thresholding methods when images are corrupted with artifacts and noise, based on relating each pixel in the image to different output centroids via a fuzzy membership function, avoiding any initial hard decision.
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Fuzzy image processing
Horst Haußecker,Hamid R. Tizhoosh +1 more
- 01 Jan 2000
TL;DR: This chapter provides an overview of the basic principles and potentials of state of the art fuzzy image processing that can be applied to a variety of computer vision tasks.
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Fuzzy sets and their applications to cognitive and decision processes
Zadeh Lotfi A.,Fu King-Sun,Tanaka Kokichi,Shimura Masamichi +3 more
- 11 Mar 2008
TL;DR: Fuzzy sets are a class in which there may be a continuum of grades of membership as, say, in the class of long objects as mentioned in this paper, which underlie much of our ability to summarize, communicate, and make decisions under uncertainty or partial information.
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Smoothing, forecasting and prediction of discrete time series
Robert Goodell Brown
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Image enhancement and thresholding by optimization of fuzzy compactness
Sankar K. Pal,Azriel Rosenfeld +1 more
TL;DR: Algorithms based on minimization of compactness and of fuzziness are developed whereby it is possible to obtain both fuzzy and nonfuzzy (thresholded) versions of an ill-defined image.
295