Adaptive fuzzy-K-means clustering algorithm for image segmentation

Sulaiman, Siti Noraini; Isa, Nor Ashidi Mat

doi:10.1109/tce.2010.5681154

Cited by 168 publications

(60 citation statements)

References 30 publications

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“…The unsupervised fuzzy k-means classifier was the most common clustering method used in a number of studies that mapped ice cover in Canadian rivers (e.g., [7,[10][11][12]16]. The aim of the fuzzy k-means approach is data reduction, to aid in information transfer in the field of pattern [32][33][34]. Data reduction is conducted by translating a multiple attribute description of an object into k membership values, with respect to k classes which represent the fuzzy behaviour.…”

Section: Mapping Algorithm Of River Ice Typesmentioning

confidence: 99%

“…Data reduction is conducted by translating a multiple attribute description of an object into k membership values, with respect to k classes which represent the fuzzy behaviour. For further details on the fuzzy k-means algorithm, readers are refer to Sulaiman et al [33], Dehariya et al [32], and Jain [34]. In general, the fuzzy k-means classifier uses an iterative procedure that starts with an initial random allocation of the objects to be classified into k clusters.…”

Section: Mapping Algorithm Of River Ice Typesmentioning

confidence: 99%

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Monitoring the Variation in Ice-Cover Characteristics of the Slave River, Canada Using RADARSAT-2 Data—A Case Study

Chu

Das

2015

Remote Sensing

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Abstract:The winter regime of river-ice covers in high northern latitude regions is often a determining factor in the management of water resources, conservation of aquatic ecosystems and preservation of traditional and cultural lifestyles of local peoples. As ground-based monitoring of river-ice regimes in high northern latitudes is expensive and restricted to a few locations due to limited accessibility to most places along rivers from shorelines, remote sensing techniques are a suitable approach for monitoring. This study developed a RADARSAT-2 based method to monitor the spatio-temporal variation of ice covers, as well as ice types during the freeze-up period, along the main channel of the Slave River Delta in the Northwest Territories of Canada. The spatio-temporal variation of ice covers along the river was analyzed using the backscatter-based coefficient of variation (CV) in the 2013-2014 and 2014-2015 winters. As a consequence of weather and flow conditions, the ice cover in the 2013-2014 winter had the higher variation than the 2014-2015 winter, particularly in the potential areas of flooded/cracked ice covers. The river sections near active channels (e.g., Middle Channel and Nagle Channel), Big Eddy, and Great Slave Lake also yielded higher intra-annual variation of ice cover characteristics during the winters. With the inclusion of backscatter and texture analysis from RADARSAT-2 data, four water and ice cover classes consisting of open water, thermal ice, juxtaposed ice, and consolidated ice, were discriminated in the images acquired between November and March in both the studied winters. In addition to river geomorphology and climatic conditions such as river width, sinuosity or air temperature, the fluctuation of water flows during the winter has a significant impact on the variation of OPEN ACCESS Remote Sens. 2015, 7 13665 ice cover as well as the formation of different ice types in the Slave River. The RADARSAT-2 based monitoring algorithm can also be applied to other river systems in high latitude ecosystems to annually monitor their river-ice variation and formation during the freeze-up and ice cover progression period.

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Section: Mapping Algorithm Of River Ice Typesmentioning

confidence: 99%

Section: Mapping Algorithm Of River Ice Typesmentioning

confidence: 99%

Monitoring the Variation in Ice-Cover Characteristics of the Slave River, Canada Using RADARSAT-2 Data—A Case Study

Chu

Das

2015

Remote Sensing

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“…Researchers focus on solving this battery grouping by data clustering algorithms [15]. As an important form of data analysis technology, data clustering [16] has been applied to many areas, such as data mining [17], content retrieval [18,19] and image segmentation [20]. By clustering algorithms, data can be divided into different clusters based on some criteria, and data in the same cluster are similar to each other and dissimilar otherwise as a result.…”

Section: Related Workmentioning

confidence: 99%

A Robust Battery Grouping Method Based on a Characteristic Distribution Model

Yang

Gao

et al. 2017

Energies

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Abstract:The inconsistent characteristics of individual power batteries in a battery pack can seriously affect the performance and service life of the whole pack. Battery grouping is an effective approach for dealing with the inconsistency problem by grouping batteries with similar characteristics in the same battery pack. In actual production, the battery grouping process still relies on the traditional manual method, which results in high labor and time costs. In this paper, a robust and effective battery grouping method based on the characteristic distribution model is developed. Specifically, a novel characteristic distribution model is proposed to determine the grouping priority of different batteries. Then, an improved k-nearest-neighbor algorithm is used to decide which batteries should be group into the same battery pack. Experimental results demonstrate the effectiveness of the proposed method.

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“…If a pixel is assigned to a region, it is not considered a seed in this procedure. The region-growing method ends when all pixels in the image are assigned to a region [35][36][37].…”

Section: Segmentation Of First Component Image From Pcamentioning

confidence: 99%

Semi-Automatic Detection of Swimming Pools from Aerial High-Resolution Images and LIDAR Data

Rodríguez-Cuenca

Alonso

2014

Remote Sensing

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Bodies of water, particularly swimming pools, are land covers of high interest. Their maintenance involves energy costs that authorities must take into consideration. In addition, swimming pools are important water sources for firefighting. However, they also provide a habitat for mosquitoes to breed, potentially posing a serious health threat of mosquito-borne disease. This paper presents a novel semi-automatic method of detecting swimming pools in urban environments from aerial images and LIDAR data. A new index for detecting swimming pools is presented (Normalized Difference Swimming Pools Index) that is combined with three other decision indices using the Dempster-Shafer theory to determine the locations of swimming pools. The proposed method was tested in an urban area of the city of Alcalá de Henares in Madrid, Spain. The method detected all existing swimming pools in the studied area with an overall accuracy of 99.86%, similar to the results obtained by support vector machines (SVM) supervised classification.

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Adaptive fuzzy-K-means clustering algorithm for image segmentation

Cited by 168 publications

References 30 publications

Monitoring the Variation in Ice-Cover Characteristics of the Slave River, Canada Using RADARSAT-2 Data—A Case Study

Monitoring the Variation in Ice-Cover Characteristics of the Slave River, Canada Using RADARSAT-2 Data—A Case Study

A Robust Battery Grouping Method Based on a Characteristic Distribution Model

Semi-Automatic Detection of Swimming Pools from Aerial High-Resolution Images and LIDAR Data

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