Robust and Accurate Shape Model Matching Using Random Forest Regression-Voting

By Claudia Lindner, Paul A. Bromiley, Mircea C. Ionita, Tim Cootes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015

420 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/68c1090f912b69b76437644dd16922909dd40d60

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In Computer Vision and Pattern Recognition

Citations in the corpus

Multi-Scale Deep Reinforcement Learning for Real-Time 3D-Landmark Detection in CT Scans (2019, neutral): “In this context, shape model matching with random forest voting [38] has been proposed.”
A benchmark for comparison of dental radiography analysis algorithms (2016, background): “) ( Lindner, 2015 ) as part of a fully automatic landmark detection system ( Lindner, 2013 ) to detect the 19 landmarks on new unseen images.”
Compositional Human Pose Regression (2017, neutral): “There is a lot of extensions [23, 50, 36, 35, 51, 26, 9].”
A Data-Driven Design for Fault Detection of Wind Turbines Using Random Forests and XGboost (2018, methodology): “These two ensemble models have already been employed in various classification or regression applications, which have shown that RF and XGBoost are effective and efficient classifier design algorithms [27].”
Support Vector Machine Versus Random Forest for Remote Sensing Image Classification: A Meta-Analysis and Systematic Review (2020, positive): “They have been widely adopted and applied as a standard clas-siﬁer to a variety of prediction and classiﬁcation tasks, such as those in bioinformatics [131], computer vision [132], and RS land cover classiﬁcation [133].”
Body Parts Dependent Joint Regressors for Human Pose Estimation in Still Images (2014, background): “…reveals that our approach achieves a body part detection accuracy on a par with the state-of-the-art on the sports dataset and greatly outperforms [5] in terms of joint localization accuracy on the FashionPose dataset, while achieving a run time of a couple of images per second on the LSP dataset.”
Under review as a conference paper at ICLR 2020 many domain adaptation methods (2019, methodology): “Random forests have been applied to many applications such as object tracking Zhang et al. (2017), feature point detection Lindner et al. (2014), and speech recognition Black & Muthukumar (2015), to name a few.”
Learning Deep Representation for Face Alignment with Auxiliary Attributes (2014, background): “We believe that facial landmark detection is not a standalone problem, but its estimation can be influenced by a number of heterogeneous and subtly correlated factors.”
Random tree walk toward instantaneous 3D human pose estimation (2015, background): “The experiments show that even with large computation gain, the accuracy is higher or comparable to the state-of-the-art pose estimation methods.”
Learn to Combine Multiple Hypotheses for Accurate Face Alignment (2013, methodology): “In [4], impressive performance was achieved by using random forest to vote for the best position of each landmark.”
Complete Random Forest Based Class Noise Filtering Learning for Improving the Generalizability of Classifiers (2019, methodology): “The sampling methods reduce the number of features for establishing a decision tree so that the overfitting can be decreased [61], [62], [63].”
A Parallel Random Forest Algorithm for Big Data in a Spark Cloud Computing Environment (2017, methodology): “The PRF algorithm is optimized based on a hybrid approach combining data-parallel and task-parallel optimization.”