超像素分割技术发展情况梳理(Superpixel Segmentation)
来源:互联网 发布:指针数组初始化 null 编辑:程序博客网 时间:2024/05/29 14:11
一. 基于图论的方法(Graph-based algorithms):
Jianbo Shi and Jitendra Malik. Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 22(8):888–905,
T. Cour, F. Benezit, and J. Shi. Spectral segmentation with multiscale graph decomposition. In IEEEComputer Vision and Pattern Recognition (CVPR) 2005, 2005.
Project Home Page:
http://www.cis.upenn.edu/~jshi/software/
http://www.timotheecour.com/software/ncut/ncut.html
2. Graph-based segmentation, 2004.
Pedro Felzenszwalb and Daniel Huttenlocher. Efficient graph-basedimage segmentation. International Journal of Computer Vision (IJCV),59(2):167–181, September 2004.
Project Home Page:
3. Graph cuts method, 2008.
Alastair Moore, Simon Prince, Jonathan Warrell, Umar Mohammed, andGraham Jones. Superpixel Lattices. IEEE Computer Vision and PatternRecognition (CVPR), 2008.
Project Home Page:
4. GCa10 and GCb10, 2010.
O. Veksler, Y. Boykov, and P. Mehrani. Superpixels and supervoxels in an energy optimization framework. In European Conference on Computer Vision (ECCV), 2010.
Project Home Page:
5. Entropy Rate Superpixel Segmentation, 2011.
Ming-Yu Liu, Tuzel, O., Ramalingam, S. , Chellappa, R., Entropy Rate Superpixel Segmentation, CVPR,2011.
Project Home Page:http://www.umiacs.umd.edu/~mingyliu
6. Superpixels via Pseudo-Boolean Optimization, 2011.
Yuhang Zhang, Richard Hartley, John Mashford and Stewart Burn, Superpixels via Pseudo-Boolean Optimization, International Conference on Computer Vision (ICCV), 2011.
http://yuhang.rsise.anu.edu.au/yuhang/misc.html
二. 基于梯度下降的方法(Gradient-ascent-based algorithms):
1. Watershed,1991.
Luc Vincent and Pierre Soille. Watersheds in digital spaces: An efficient algorithm based on immersion simulations. IEEE Transactions on Pattern Analalysis and Machine Intelligence, 13(6):583–598, 1991.
2. Mean Shift, 2002.
D. Comaniciu and P. Meer. Mean shift: a robust approach toward featurespace analysis. IEEE Transactions on Pattern Analysis and MachineIntelligence, 24(5):603–619, May 2002.
3. Quick Shift, 2008
A. Vedaldi and S. Soatto. Quick shift and kernel methods for mode seeking. In European Conference on Computer Vision (ECCV), 2008.
Project Home Page:
4. Turbopixel, 2009.
A. Levinshtein, A. Stere, K. Kutulakos, D. Fleet, S. Dickinson, and K. Siddiqi. Turbopixels: Fast superpixels using geometric flows. IEEETransactions on Pattern Analysis and Machine Intelligence (PAMI),2009.
Project Home Page:
5. SLIC, 2010.
R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Susstrunk , SLIC Superpixels, 2010.
Project Home Page:
6.SEEDS, 2012.
M. Van den Bergh, X. Boix, G. Roig, B. de Capitani, L. Van Gool.SEEDS: Superpixels Extracted via Energy-Driven Sampling, ECCV 2012.
Project Home Page:http://www.vision.ee.ethz.ch/~boxavier/seeds/
转载自:http://blog.sina.com.cn/s/blog_eccd03ec0101k0i4.html
- 超像素分割技术发展情况梳理(Superpixel Segmentation)
- 超像素分割技术发展情况梳理(Superpixel Segmentation)
- 超像素分割技术发展情况梳理(Superpixel Segmentation)--计算机视觉专题3
- 超像素分割技术发展情况梳理(Superpixel Segmentation)--计算机视觉专题3
- 超像素分割(Superpixel Segmentation)发展
- 超像素分割(Superpixel Segmentation)发展
- 超像素分割(Superpixel Segmentation)发展
- 超像素分割技术发展情况梳理(superpixels segment)
- (superpixel)超像素分割
- 利用吸收态马尔科夫链进行基于超像素分割的目标跟踪【Superpixel-based Tracking-by-Segmentation using Markov Chains】
- SLIC超像素(superpixel)算法
- SLIC超像素(superpixel)算法
- SLIC超像素(superpixel)算法
- Superpixel Tracking(超像素追踪)
- SLIC超像素(superpixel)算法
- 超像素(Superpixel)理解
- 超像素 Superpixel
- SLIC算法 超像素 superpixel
- (4.6.20)基于七牛和fresco的一整套安卓图片解决方案
- Bugtags 实时跟踪插件 - BugtagsInsta
- Android 开发之Git的使用,你绝对值得拥有
- 叠筐
- (五)4 写个简单的LED驱动
- 超像素分割技术发展情况梳理(Superpixel Segmentation)
- 关于iframe页面嵌入后在ios设备上position=fixed属性失效的解决办法
- 第四周项目1-建立单链表
- 顺时针输出矩阵
- bzoj1593(线段树)
- caffe-android-lib 在ubuntu下的编译
- 高精度模板
- 21 viewPager--- hzScrollView ----llContainer
- Android TextView中文字通过SpannableString来设置超链接、颜色、