Fuzzy C-means (FCM) clustering algorithm: a decade review from 2000 to 2014
The Fuzzy c-means is one of the most popular ongoing area of research among all types of
researchers including Computer science, Mathematics and other areas of engineering, as …
researchers including Computer science, Mathematics and other areas of engineering, as …
A comprehensive review of fruit and vegetable classification techniques
Recent advancements in computer vision have enabled wide-ranging applications in every
field of life. One such application area is fresh produce classification, but the classification of …
field of life. One such application area is fresh produce classification, but the classification of …
Segmentation of images by color features: A survey
Image segmentation is an important stage for object recognition. Many methods have been
proposed in the last few years for grayscale and color images. In this paper, we present a …
proposed in the last few years for grayscale and color images. In this paper, we present a …
Semi-supervising Interval Type-2 Fuzzy C-Means clustering with spatial information for multi-spectral satellite image classification and change detection
Data clustering has been widely applied to numerous real-world problems such as natural
resource management, urban planning, and satellite image analysis. Especially, fuzzy …
resource management, urban planning, and satellite image analysis. Especially, fuzzy …
Total Bregman divergence-driven possibilistic fuzzy clustering with kernel metric and local information for grayscale image segmentation
C Wu, X Zhang - Pattern Recognition, 2022 - Elsevier
Kernel possibilistic fuzzy C-means with local information (KWPFLICM) has important
research significance of image segmentation, but it is very sensitive to high noise or outliers …
research significance of image segmentation, but it is very sensitive to high noise or outliers …
Color-based image segmentation by means of a robust intuitionistic fuzzy c-means algorithm
To yield well-suited image segmentation results, conventional clustering algorithms depend
on customized hand-crafted features as well as an appropriate initialization process. This …
on customized hand-crafted features as well as an appropriate initialization process. This …
Block-Matching Fuzzy C-Means clustering algorithm for segmentation of color images degraded with Gaussian noise
F Gamino-Sánchez, IV Hernández-Gutiérrez… - … Applications of Artificial …, 2018 - Elsevier
In this paper, we present the Block-Matching Fuzzy C-Means (BMFCM) clustering algorithm
to segment RGB color images degraded with Additive White Gaussian Noise (AWGN). The …
to segment RGB color images degraded with Additive White Gaussian Noise (AWGN). The …
Semi-supervised fuzzy C-means clustering for change detection from multispectral satellite image
Data clustering has been applied in almost areas such as health, natural resource
management, urban planning∶ especially, fuzzy clustering which the advantage with …
management, urban planning∶ especially, fuzzy clustering which the advantage with …
Color image segmentation using adaptive hierarchical-histogram thresholding
M Li, L Wang, S Deng, C Zhou - PloS one, 2020 - journals.plos.org
Histogram-based thresholding is one of the widely applied techniques for conducting color
image segmentation. The key to such techniques is the selection of a set of thresholds that …
image segmentation. The key to such techniques is the selection of a set of thresholds that …
Color image segmentation using feedforward neural networks with FCM
S Arumugadevi, V Seenivasagam - International Journal of Automation …, 2016 - Springer
This paper proposes a hybrid technique for color image segmentation. First an input image
is converted to the image of CIE L* a* b* color space. The color features “a” and “b” of CIE L …
is converted to the image of CIE L* a* b* color space. The color features “a” and “b” of CIE L …