Fuzzy clustering based transition region extraction for image segmentation

Parida, Priyadarsan; Bhoi, Nilamani

doi:10.1016/j.jestch.2018.05.012

Cited by 15 publications

(6 citation statements)

References 31 publications

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“…Advantages Disadvantages superpixel [5,6] reduce redundant information; less complexity cannot locate the edges accurately watershed [7,8] simple and intuition usually result in over segmention active contour models [9,10] rigorous mathematical base; sensitive noise; high computation complexity clustering [11,12] intensive value is enough; simple the number of cluster cannot be determined automatically; spatial information is ignored;…”

Section: Methodsmentioning

confidence: 99%

“…To deal with image segmentation, many approaches and strategies had been developed. For example, turbopixel/superpixel segmentation methods [5,6], watershed segmentation methods [7,8], active contour models [9,10], clustering based methods [11,12], deep learning-based methods [13,14], thresholding methods [15,16],and so on. The advantages and disadvantages of these methods are summarized in Table 1.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Nonlocal Means Two Dimensional Histogram-Based Image Segmentation via Minimizing Relative Entropy

Jiang

Yang²,

Guo

et al. 2018

Entropy

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Spatial correlation information between pixels is considered to be very important in thresholding methods. However, it is often ignored and thus unsatisfied segmentation results maybe obtained. To overcome this shortcoming, we propose a new image segmentation approach by taking not only pixels’ spatial information but also pixels’s gray level into account. First, a non-local mean filter is imposed on the image. Then the filtered image and the original image together are adopted to build a two dimensional histogram, it is called non-local mean two dimensional histogram. Finally, a minimum relative entropy criteria is used to select the ideal thresholding vector. Since the non-local mean filter process is performed in a neighborhood of current pixel, it carries out the spatial information of current pixel. Segmentation results on several images illustrate the effectiveness of the proposed thresholding method, whose segmentation accuracy are greatly improved compared to most existing thresholding methods.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Nonlocal Means Two Dimensional Histogram-Based Image Segmentation via Minimizing Relative Entropy

Jiang

Yang²,

Guo

et al. 2018

Entropy

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show abstract

“…In [21], the authors exploited infrared thermography to diagnose materials decay taking into account different historical periods.This approach is used as a tool in the diagnostic level, for the detection of invisible superficial cracks or/and disparities, as well as the revelation of moisture presence within structures. In [22], the authors introduced a fuzzy clustering approach for extracting the local variance feature from an image. This method applied to define the transitional features implementing hybrid segmentation.…”

Section: Related Workmentioning

confidence: 99%

Improved Resilience and Sustainable Reconstruction of Cultural Heritage Areas to cope with Climate Change and Other Hazards based on Innovative Algorithms and Modelling Tools

Labropoulos¹,

Zafeiropoulos²,

Protopapadakis³

et al. 2022

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Our proposed framework aims to efficiently train a network of fellows on the field of the resilience of Cultural Heritage (CH) areas and historic cities against Climate Change (CC) and other types of hazards. Towards this direction, the proposed framework aims to introduce a research framework for downscaling the created climate and atmospheric composition as well as associated risk maps down to the 1x1 km (historic area) scale, and specific damage functions for CH ma-terials. Applying atmospheric modelling for specific CC scenarios at such refined spatial and time scales allows for an accurate quantitative and qualitative impact as-sessment of the estimated micro-climatic and atmospheric stressors. Our proposed framework will perform combined structural/geotechnical analysis of the CH sites and damage assessment under normal and changed conditions, based on the cli-matic zone, the micro-climate conditions, the petrographic and textural features of building materials, historic data for the structures, the effect of previous restoration processes and the environmental/physical characteristics of the surrounding envi-ronment. The data coming from installed monitoring system will be coupled with simulated data and will be further analysed through our data management system, while supporting communities’ participation and public awareness. The data from the monitoring system will feed the Decision- Support-System (DSS) so as to pro-vide proper adaptation and mitigation strategies. The produced vulnerability map will be used by the local authorities to assess the threats of CC (and other natural hazards), visualize the built heritage and cultural landscape under future climate scenarios, model the effects of different adaptation strategies, and ultimately prior-itize any rehabilitation actions to best allocate funds in both pre- and post-event en-vironments. To train the fellows, our approach will make use of extensive workshop and training sessions, as well organise summer schools.

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“…This approach provides elastic features of the architectural structure materials in order to detect the crack and inclusion in the building taking into consideration the affected layer inside the material. In [10], the authors introduced a fuzzy clustering approach for extracting the local variance feature from an image. This method applied to define the transitional features implementing hybrid segmentation.…”

Section: Related Workmentioning

confidence: 99%

Evaluating the Usefulness of Unsupervised Monitoring in Cultural Heritage Monuments

Zafeiropoulos¹,

Tzortzis²,

Rallis³

et al. 2021

Frontiers in Artificial Intelligence and Applications

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In this paper, we scrutinize the effectiveness of various clustering techniques, investigating their applicability in Cultural Heritage monitoring applications. In the context of this paper, we detect the level of decomposition and corrosion on the walls of Saint Nicholas fort in Rhodes utilizing hyperspectral images. A total of 6 different clustering approaches have been evaluated over a set of 14 different orthorectified hyperspectral images. Experimental setup in this study involves K-means, Spectral, Meanshift, DBSCAN, Birch and Optics algorithms. For each of these techniques we evaluate its performance by the use of performance metrics such as Calinski-Harabasz, Davies-Bouldin indexes and Silhouette value. In this approach, we evaluate the outcomes of the clustering methods by comparing them with a set of annotated images which denotes the ground truth regarding the decomposition and/or corrosion area of the original images. The results depict that a few clustering techniques applied on the given dataset succeeded decent accuracy, precision, recall and f1 scores. Eventually, it was observed that the deterioration was detected quite accurately.

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Fuzzy clustering based transition region extraction for image segmentation

Cited by 15 publications

References 31 publications

Nonlocal Means Two Dimensional Histogram-Based Image Segmentation via Minimizing Relative Entropy

Nonlocal Means Two Dimensional Histogram-Based Image Segmentation via Minimizing Relative Entropy

Improved Resilience and Sustainable Reconstruction of Cultural Heritage Areas to cope with Climate Change and Other Hazards based on Innovative Algorithms and Modelling Tools

Evaluating the Usefulness of Unsupervised Monitoring in Cultural Heritage Monuments

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