Neuro-fuzzy clustering of radiographic tibia image data using type 2 fuzzy sets

John, Robert; Innocent, Peter R.; Barnes, M.R.

doi:10.1016/s0020-0255(00)00009-8

Cited by 78 publications

(30 citation statements)

References 22 publications

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“…In [10,50], the training set and testing set are obtained using the cross-validation technique. All papers show comparative results against T1 FS as well as other classifier techniques, where, in accordance with the classification rates, the approach based on T2 FS [50,53] is better than T1 FS approaches and other known methods. Based on these results, we can observe that T2 fuzzy techniques perform better when applied to classification applications, especially when digital images are incomplete or involve some noise, in which case it is difficult for the system to determine if a pixel is part or not of a class.…”

Section: Discussionmentioning

confidence: 58%

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Review of Recent Type-2 Fuzzy Image Processing Applications

et al. 2017

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This paper presents a literature review of applications using type-2 fuzzy systems in the area of image processing. Over the last years, there has been a significant increase in research on higher-order forms of fuzzy logic; in particular, the use of interval type-2 fuzzy sets and general type-2 fuzzy sets. The idea of making use of higher orders, or types, of fuzzy logic is to capture and represent uncertainty that is more complex. This paper is focused on image processing systems, which includes image segmentation, image filtering, image classification and edge detection. Various applications are presented where general type-2 fuzzy sets, interval type-2 fuzzy sets, and interval-value fuzzy sets are used; some are compared with the traditional type-1 fuzzy sets and others methodologies that exist in the literature for these areas in image processing. In all accounts, it is shown that type-2 fuzzy sets outperform both traditional image processing techniques as well as techniques using type-1 fuzzy sets, and provide the ability to handle uncertainty when the image is corrupted by noise.

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Section: Discussionmentioning

confidence: 58%

“…Papers reviewed in this section [10,50,53] use real images, where [50,53] are focused on medical applications. They use different metrics to evaluate the classification accuracy in which we can single out the Friedman test, contingency coefficient, and Kappa.…”

Section: Discussionmentioning

confidence: 99%

Review of Recent Type-2 Fuzzy Image Processing Applications

et al. 2017

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“…Most of the method basing on fuzzy logic adopt Type-1 fuzzy sets representing uncertainties with the range between [0,1] and type-1 fuzzy sets is having a precise membership function where its elements are real number. To handle [28] this difficulties a type-2 fuzzy sets is introduced which is most able to handle the uncertainty related to noisy and non-stationary than type-1 fuzzy set along with allowing uncertainty [29][30][31][32] to its associated membership degree. For the prediction of stock price Chih-Feng et al presented a type-2 neuro-fuzzy model where [28] a self constructed clustering method designed the type-2 fuzzy rules and then refined it by a hybrid algorithm.…”

Section: Psomentioning

confidence: 99%

A Survey on Impact of Bio-inspired Computation on Stock Market Prediction

Das¹,

Mishra²,

Rout³

2017

JESTR

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Financial forecasting is one of the imperative fields of research, where investors invest money and get restless for the future changes of the stock values in the market. In the recent course of time forecasting stock price is one of the challenging tasks. To predict the stock price most Artificial Neural Network (ANN) based model are used in the historical data along with statistical measures, technical indicators etc. With the development of ANNs, investors are hoping the best prediction because networks have great capability of machine learning problems such as classification and prediction. ANNs particularly Back Propagation (BP) has overlooked the non-stationary and noise characteristics of stock market data, as the training of BP is intricate due to the noise data and the network fall into a natural solution such as always predict an usual output. Most optimization techniques have been used for training the weights of forecasting models. Since no single optimization technique is invariably superior to others. Recently various nature inspired optimization techniques have been introduced and successfully employed to many fields of Financial Engineering. This survey aims to study the mostly used Bio-inspired optimization techniques on Stock Market Prediction and makes a comparative analysis between them.

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“…Although these algorithms can smooth noise to a certain extent, effectively solve the problem of pixel uncertainty caused by the spatial correlation of pixels, and thus improve the segmentation accuracy of the algorithms, they cannot deal with the influence of uncertainty of decision-making on the segmentation results in high-resolution image segmentation. In recent years, type-2 fuzzy theory (Karnik and Mendel., 2001;Jonr et al, 2000;Liang and Mendel .,2001;Mendel., 2000) , applied to deal with the uncertainty problems, is a new method. Based on this theory, the image model with the primary membership function and secondary membership function are built, which can effectively deal with the above two kinds of uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Interval Type-2 Fuzzy Based Neural Network for High Resolution Remote Sensing Image Segmentation

Wang¹,

Xu²,

Li³

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT：Recently, high resolution remote sensing image segmentation is a hot issue in image processing procedures. However, it is a difficult task. The difficulties derive from the uncertainties of pixel segmentation and decision-making model. To this end, we take spatial relationship into consideration when constructing the interval type-2 fuzzy neural networks for high resolution remote sensing image segmentation. First, the proposed algorithm constructs a Gaussian model as a type-1 fuzzy model to describe the uncertainty contained in the image. Second, the interval type-2 fuzzy model is obtained by blurring the mean and variance in type-1 model. The proposed interval type-2 model can strengthen the expression of uncertainty and simultaneously decrease the uncertainty in the decision model. Then the fuzzy membership function itself and its upper and lower fuzzy membership functions of the training samples are used as the input of the neuron network which acts as the decision model in the proposed algorithm. Finally, the relationship of neighbour pixels is taken into consideration and the fuzzy membership functions of the detected pixel and its neighbourhood are used to decide the class of each pixel to get the final segmentation result. The proposed algorithm, FCM and HMRF-FCM algorithm and an interval type-2 fuzzy neuron networks without spatial relationships are performed on synthetic and real high resolution remote sensing images. The qualitative and quantitative analyses demonstrate the efficient of the proposed algorithm, especially for homogeneous regions which contains a great difference in its gray level (for example forest).

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Neuro-fuzzy clustering of radiographic tibia image data using type 2 fuzzy sets

Cited by 78 publications

References 22 publications

Review of Recent Type-2 Fuzzy Image Processing Applications

Review of Recent Type-2 Fuzzy Image Processing Applications

A Survey on Impact of Bio-inspired Computation on Stock Market Prediction

Interval Type-2 Fuzzy Based Neural Network for High Resolution Remote Sensing Image Segmentation

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