Finding Dense Supervoxel Correspondence of Cone-Beam Computed Tomography Images
暂无分享,去创建一个
Hongbin Zha | Yuru Pei | Yuke Guo | Gengyu Ma | Tianmin Xu | Gui Chen | Yunai Yi | H. Zha | Yuru Pei | Yuke Guo | Gengyu Ma | Gui Chen | T. Xu | Yunai Yi
[1] D. Louis Collins,et al. Patch-based segmentation using expert priors: Application to hippocampus and ventricle segmentation , 2011, NeuroImage.
[2] Ben Glocker,et al. Encoding atlases by randomized classification forests for efficient multi-atlas label propagation , 2014, Medical Image Anal..
[3] Hongbin Zha,et al. Unsupervised Random Forest Manifold Alignment for Lipreading , 2013, 2013 IEEE International Conference on Computer Vision.
[4] Ben Glocker,et al. Supervoxel Classification Forests for Estimating Pairwise Image Correspondences , 2015, MLMI.
[5] Mehryar Mohri,et al. Random Composite Forests , 2016, AAAI.
[6] Misha Denil,et al. Narrowing the Gap: Random Forests In Theory and In Practice , 2013, ICML.
[7] Pascal Fua,et al. SLIC Superpixels Compared to State-of-the-Art Superpixel Methods , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[8] Antonio Criminisi,et al. Regression Forests for Efficient Anatomy Detection and Localization in CT Studies , 2010, MCV.
[9] Shu Liao,et al. Automated Segmentation of CBCT Image Using Spiral CT Atlases and Convex Optimization , 2013, MICCAI.
[10] J. Shotton,et al. Decision Forests for Classification, Regression, Density Estimation, Manifold Learning and Semi-Supervised Learning , 2011 .
[11] Leo Breiman,et al. Random Forests , 2001, Machine Learning.