Name
Abstract | ||
|---|---|---|
Image segmentation plays an important role in image processing. Image segmentation algorithms have been proposed as early as the last century, and constantly find and optimize various algorithms. The quality of the image segmentation algorithm determines the result of image analysis and image understanding. The principle, advantages and disadvantages of traditional image segmentation algorithms are briefly introduced in this paper. The variety of image segmentation algorithms is determined by the complexity of the image itself. In recent years, scholars continue to improve a variety of image segmentation algorithms, the paper introduces the improvement of fuzzy C-means algorithm and mean-shift algorithm. The fuzzy C-means algorithm does not consider the spatial information of the image. Put forward an fuzzy C-means algorithm based on membership correction is proposed, taking into account the high correlation of pixels in image segmentation. The mean shift algorithm converges slowly, and mean shift algorithm based on conjugate gradient method is proposed to improve the convergence speed of the algorithm. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ICMLC.2018.8527000 | 2018 International Conference on Machine Learning and Cybernetics (ICMLC) |
Keywords | Field | DocType |
Image segmentation,Clustering,Fuzzy C-means algorithm,Mean shift algorithm | Conjugate gradient method,Pattern recognition,Computer science,Fuzzy logic,Level set,Image processing,Image segmentation,Pixel,Artificial intelligence,Mean-shift,Cluster analysis | Conference |
Volume | ISSN | ISBN |
2 | 2160-133X | 978-1-5386-5215-2 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||