Unsupervised Image Segmentation using Multi-label Graph Cuts
碩士 === 國立清華大學 === 資訊工程學系 === 104 === Image segmentation is an important issue in image editing and computer vision. Due to the complexity of information in images, efficient extraction of a foreground object is a challenging problem. Recently, several approaches based on optimization by graph cuts h...
Main Authors: | Wang, Chung Han, 王宗涵 |
---|---|
Other Authors: | Chang, Long Wen |
Format: | Others |
Language: | en_US |
Published: |
2016
|
Online Access: | http://ndltd.ncl.edu.tw/handle/92616521540163396109 |
Similar Items
-
Unsupervised Multi-Label Image and Texture Segmentation based on Optimal Feature Representation
by: Mehmet Cem Catalbas
Published: (2019-01-01) -
KmsGC: An Unsupervised Color Image Segmentation Algorithm Based on K-Means Clustering and Graph Cut
by: Binmei Liang, et al.
Published: (2014-01-01) -
Unsupervised Cell Segmentation and Labelling in Neural Tissue Images
by: Sara Iglesias-Rey, et al.
Published: (2021-04-01) -
Unsupervised Figure-ground Segmentation using Edge Detection and Game-theoretical Graph-cut Approach
by: Hsiao, Yu-Min, et al.
Published: (2014) -
Image Segmentation Using Unsupervised Classification
by: Zong-Shuo Xie, et al.
Published: (2008)