In this paper, we propose an effective method that detects fire automatically. The proposed algorithm is composed of four stages. In the first stage, an approximate median method is used to detect moving regions. In t...
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ISBN:
(纸本)9783642210891
In this paper, we propose an effective method that detects fire automatically. The proposed algorithm is composed of four stages. In the first stage, an approximate median method is used to detect moving regions. In the second stage, a fuzzyc-means (FcM) algorithm based on the color of fire is used to select candidate fire regions from these moving regions. In the third stage, a discrete wavelet transform (DWT) is used to derive the approximated and detailed wavelet coefficients of sub-image. In the fourth stage, using these wavelet coefficients, a back-propagation neural network (BPNN) is utilized to distinguish between fire and non-fire. Experimental results indicate that the proposed method outperforms other fire detection algorithms, providing high reliability and low false alarm rate.
One of the main challenges in the field of c-meansclustering models is creating an algorithm that is both accurate and robust. In the absence of outlier data, the conventional probabilisticfuzzyc-means (FcM) algori...
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ISBN:
(纸本)9783642225888
One of the main challenges in the field of c-meansclustering models is creating an algorithm that is both accurate and robust. In the absence of outlier data, the conventional probabilisticfuzzyc-means (FcM) algorithm, or the latest possibilistic-fuzzy mixture model (PFcM), provide highly accurate partitions. However, during the 30-year history of FcM, the researcher community of the field failed to produce an algorithm that is accurate and insensitive to outliers at the same time. This paper introduces a novel mixture clustering model built upon probabilistic and possibilisticfuzzy partitions, where the two components are connected to each other in a qualitatively different way than they were in earlier mixtures. The fuzzy-possibilistic product partition c-means ((FPcM)-c-3) clustering algorithm seems to fulfil the initial requirements, namely it successfully suppresses the effect of outliers situated at any finite distance and provides partitions of high quality.
This paper proposes an effective, four-stage smoke-detection algorithm using video images. In the first stage, an approximate median method is used to segment moving regions in a video frame. In the second stage, a fu...
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This paper proposes an effective, four-stage smoke-detection algorithm using video images. In the first stage, an approximate median method is used to segment moving regions in a video frame. In the second stage, a fuzzyc-means (FcM) method is used to cluster candidate smoke regions from these moving regions. In the third phase, a parameter extraction method is used to extract a set of parameters from spatial and temporal characteristics of the candidate smoke regions: these parameters include the motion vector, surface roughness and area randomness of smoke. In the fourth stage, the parameters extracted from the third stage are used as input feature vectors to train a support vector machine (SVM) classifier, which is then used by the smoke alarm to make a decision. Experimental results show that the proposed four-stage smoke-detection algorithm outperforms conventional smoke-detection algorithms in terms of accuracy of smoke detection, providing a low false-alarm rate and high reliability in open and large spaces. (c) 2011 Elsevier Ltd. All rights reserved.
Apply interpretive structural modeling to construct knowledge structure of linear algebra. New fuzzyclustering algorithms improved fuzzy c-means algorithm based on Mahalanobis distance has better performance than fuz...
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ISBN:
(纸本)9783037850695
Apply interpretive structural modeling to construct knowledge structure of linear algebra. New fuzzyclustering algorithms improved fuzzy c-means algorithm based on Mahalanobis distance has better performance than fuzzy c-means algorithm. Each cluster of data can easily describe features of knowledge structures individually. The results show that there are six clusters and each cluster has its own cognitive characteristics. The methodology can improve knowledge management in classroom more feasible.
In this paper, a fuzzyc-meansclustering algorithm is proposed to determine the optimum deployment of sensor nodes. It is for a given application space to improve energy efficiency and reduce cost. We performed simul...
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ISBN:
(纸本)9781936338290
In this paper, a fuzzyc-meansclustering algorithm is proposed to determine the optimum deployment of sensor nodes. It is for a given application space to improve energy efficiency and reduce cost. We performed simulation for building area to find minimum number and optimum location of sensor nodes.
The automatic diagnosis of breast cancer is an important, real-world medical problem. A major class of problems in Medical Science involves the diagnosis of disease, based upon various tests performed upon the patient...
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ISBN:
(纸本)9783642192623
The automatic diagnosis of breast cancer is an important, real-world medical problem. A major class of problems in Medical Science involves the diagnosis of disease, based upon various tests performed upon the patient. When several tests are involved, the ultimate diagnosis may be difficult to obtain, even for a medical expert. This has given rise, over the past few decades, to computerized diagnostic tools, intended to aid the Physician in making sense out of the confusion of data. This Paper carried out to generate and evaluate both fuzzy and neural network models to predict malignancy of breast tumor, using Wisconsin Diagnosis Breast cancer Database (WDBc). Our objectives in this Paper are: (i) to compare the diagnostic performance of fuzzy and neural network models in distinction between malignance and benign patterns, (ii) to reduce the number of benign cases sent for biopsy using the best model as a supportive tool, and (iii) to validate the capability of each model to recognize new cases.
This paper presents a novel semi-automated image processing procedure dedicated to the identification and characterization of the dental root canal, based on high-resolution micro-cT records. After the necessary image...
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ISBN:
(纸本)9783642250842
This paper presents a novel semi-automated image processing procedure dedicated to the identification and characterization of the dental root canal, based on high-resolution micro-cT records. After the necessary image enhancement, parallel slices are individually segmented via histogram based quick fuzzyc-meansclustering. The 3D model of root canal is built up from the segmented cross sections using the reconstruction of the inner surface, and the medial line is extracted by a 3D curve skeletonization algorithm. The central line of the root canal can finally be approximated as a 3D spline curve. The proposed procedure may support the planning of several kinds of endodontic interventions.
Two-dimensional (2D) polyacrylamide gel electrophoresis of proteins is a robust and reproducible technique. It is the most widely used separation tool in proteomics. current efforts in the field are directed at develo...
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Two-dimensional (2D) polyacrylamide gel electrophoresis of proteins is a robust and reproducible technique. It is the most widely used separation tool in proteomics. current efforts in the field are directed at development of tools for expanding the range of proteins accessible with 2D gels. Proteomics was built around the 2D gel. The idea that multiple proteins can be analyzed in parallel grew from 2D gel maps. Proteomics researchers needed to identify interested protein spots by examining the gel. This is time-consuming, labor-extensive, and error-prone process. It is desired that the computer can analyze the proteins automatically by first detecting then quantifying the protein spots in the 2D gel images. In our previous work, we presented a new technique for segmentation of 2D gel images using the fuzzyc-means (FcM) algorithm using the notion of fuzzy relations. In this paper, we will describe the new relational FcM (RFcM) algorithm and use it for automatic protein spots quantification. We will also use two methods to evaluate its performance: the unsupervised evaluation method and comparison with the expert spots quantification.
Abstract This paper presents a novel image processing procedure dedicated to the automated detection of the medial axis of the root canal from dental micro cT records. The 3D model of root canal is built up from sever...
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Abstract This paper presents a novel image processing procedure dedicated to the automated detection of the medial axis of the root canal from dental micro cT records. The 3D model of root canal is built up from several hundreds of parallel cross sections, using image enhancement and an enhanced fuzzyc-means based partitioning, center point detection in the segmented slice, three dimensional inner surface reconstruction and curve skeleton extraction in three dimensions. The central line of the root canal can finally be approximated as a 3D spline curve. The proposed procedure can help prepare several kinds of endodontic interventions.
This paper presents a fuzzy support vector classifier by integrating modified fuzzyc-meansclustering based on Mahalanobis distance into fuzzy support vector data description. The proposed algorithmcan be used to de...
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This paper presents a fuzzy support vector classifier by integrating modified fuzzyc-meansclustering based on Mahalanobis distance into fuzzy support vector data description. The proposed algorithmcan be used to deal with the outlier sensitivity problem in traditional multi-class classification problems. The modified fuzzyc-meansclustering algorithm based on Mahalanobis distance takes into the samples' correlation account, and is improved to generate different weight values for main training data points and outliers according to their relative importance in the training data. Experimental results show that the proposed method can reduce the effect of outliers and give high classification accuracy.
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