Density-Weighted Fuzzy c-Means Clustering

Rock Particle Image Segmentation Based on Improved Normalized Cut

“…Changing relevant parameters can improve the image segmentation effect, but how to decrease the computing time is still a problem we need to solve in the near future. For the special particle images, the information fusion technology may be used [10].…”

“…They are: Crashed particles (aggregates) from a falling stream; Blasted rock particles (fragments) from muckpiles; and natural rock particles (aggregates). The algorithm proposed in the paper is used in these particle images, and it is compared with some widely used methods, such as Auto-threshold segmentation [3,[6][7], Contour extraction based on priori knowledge [26][27][28][29][30], Minimum spanning tree segmentation [21], FCM segmentation [11][12], Clustering segmentation [10] and Watershed segmentation [8][9]. The testing results show that the improved Normalized Cut is suitable both for densely packed rock particle images and sparsely distributed particle images, and the segmentation results are satisfactory.…”

“…In recent years, with the development of computer science, the image processing technology is gradually applied into the different research fields of the rock particle image processing [2][3][4][5]. Currently, Threshold method [6][7], Watershed [8][9], Edge detection [7], Clustering analysis [10], Fuzzy C-Means [11], Fuzzy clustering analysis methods [10][11] and Graph based algorithms such as Minimum spanning tree [12][13][14][15][16][17][18][19][20][21] are often studied and used for rock particle image segmentation, but these methods are mainly based on gray level information, not on considering the combination of other information.…”

Task coalition formation and self‐adjustment in the wireless sensor networks

“…Further, a bad solution may increase energy consumption in the process of data transmitting in the large-scale WSNs. FCM algorithm [13,14] is a mechanism of clustering that makes data belong to more than one cluster. The mechanism is widely used in pattern recognition.…”

Data Clustering Analysis on Grassmann Manifold Metric

“…K means clustering and FCM(Fuzzy C means clustering) clustering are typical representatives of this kind of algorithm. In recent years, the research results mainly include: the density weighted fuzzy clustering algorithm [3], the double exponential fuzzy C mean algorithm [4] based on the hybrid distance learning and so on. The advantages of this kind of algorithm can be attributed to the fast convergence speed and easy to extend [5].…”