Results 1 - 10 of 535

Fully automatic cephalometric evaluation using Random Forest regression-voting

by Claudia Lindner, Tim F. Cootes
"... Abstract. Cephalometric analysis is commonly used as a standard tool for orthodontic diagnosis and treatment planning. The identification of cephalometric landmarks on images of the skull allows the quantification and classification of anatomical abnormalities. In clinical practice, the land-marks a ..."
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-marks are placed manually which is time-consuming and subjective. This work investigates the application of Random Forest regression-voting to fully automatically detect cephalometric landmarks, and to use the iden-tified positions for automatic cephalometric evaluation. Validation exper-iments on two sets of 150

Robust and Accurate Shape Model Fitting using Random Forest Regression Voting

by T. F. Cootes, M. C. Ionita, C. Lindner, P. Sauer
"... Abstract. A widely used approach for locating points on deformable objects is to generate feature response images for each point, then to fit a shape model to the response images. We demonstrate that Random Forest regression can be used to generate high quality response images quickly. Rather than u ..."
Abstract - Cited by 34 (4 self) - Add to MetaCart
Abstract. A widely used approach for locating points on deformable objects is to generate feature response images for each point, then to fit a shape model to the response images. We demonstrate that Random Forest regression can be used to generate high quality response images quickly. Rather than

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 1 Robust and Accurate Shape Model Matching using Random Forest Regression-Voting

by Claudia Lindner, Paul A. Bromiley, Mircea C. Ionita, Tim F. Cootes
"... Abstract—A widely used approach for locating points on deformable objects in images is to generate feature response images for each point, and then to fit a shape model to these response images. We demonstrate that Random Forest regression-voting can be used to generate high quality response images ..."
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Abstract—A widely used approach for locating points on deformable objects in images is to generate feature response images for each point, and then to fit a shape model to these response images. We demonstrate that Random Forest regression-voting can be used to generate high quality response images

IEEE TRANSACTIONS ON MEDICAL IMAGING 1 Fully Automatic Segmentation of the Proximal Femur Using Random Forest Regression Voting

by C. Lindner, S. Thiagarajah, J. M. Wilkinson, The Arcogen Consortium, G. A. Wallis, T. F. Cootes
"... Abstract—Extraction of bone contours from radiographs plays an important role in disease diagnosis, pre-operative planning, and treatment analysis. We present a fully automatic method to accurately segment the proximal femur in anteroposterior pelvic radiographs. A number of candidate positions are ..."
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are produced by a global search with a detector. Each is then refined using a statistical shape model together with local detectors for each model point. Both global and local models use Random Forest regression to vote for the optimal positions, leading to robust and accurate results. The performance

Random forests

by Leo Breiman, E. Schapire - Machine Learning , 2001
"... Abstract. Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error for forests converges a.s. to a limit as the number of trees in the fo ..."
Abstract - Cited by 3613 (2 self) - Add to MetaCart
Abstract. Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error for forests converges a.s. to a limit as the number of trees

Randomized Experiments from Non-random Selection in the U.S. House Elections

by David S. Lee - Journal of Econometrics , 2008
"... This paper establishes the relatively weak conditions under which causal inferences from a regression-discontinuity (RD) analysis can be as credible as those from a randomized experiment, and hence under which the validity of the RD design can be tested by examining whether or not there is a discont ..."
Abstract - Cited by 377 (17 self) - Add to MetaCart
This paper establishes the relatively weak conditions under which causal inferences from a regression-discontinuity (RD) analysis can be as credible as those from a randomized experiment, and hence under which the validity of the RD design can be tested by examining whether or not there is a

An Empirical Comparison of Supervised Learning Algorithms

by Rich Caruana, Alexandru Niculescu-mizil - In Proc. 23 rd Intl. Conf. Machine learning (ICML’06 , 2006
"... A number of supervised learning methods have been introduced in the last decade. Unfortunately, the last comprehensive empirical evaluation of supervised learning was the Statlog Project in the early 90’s. We present a large-scale empirical comparison between ten supervised learning methods: SVMs, n ..."
Abstract - Cited by 212 (6 self) - Add to MetaCart
, neural nets, logistic regression, naive bayes, memory-based learning, random forests, decision trees, bagged trees, boosted trees, and boosted stumps. We also examine the effect that calibrating the models via Platt Scaling and Isotonic Regression has on their performance. An important aspect of our

Class-specific hough forests for object detection

by Juergen Gall, Victor Lempitsky - In Proceedings IEEE Conference Computer Vision and Pattern Recognition , 2009
"... We present a method for the detection of instances of an object class, such as cars or pedestrians, in natural images. Similarly to some previous works, this is accomplished via generalized Hough transform, where the detections of individual object parts cast probabilistic votes for possible locatio ..."
Abstract - Cited by 151 (18 self) - Add to MetaCart
to object part detection. Towards this end, we train a class-specific Hough forest, which is a random forest that directly maps the image patch appearance to the probabilistic vote about the possible location of the object centroid. We demonstrate that Hough forests improve the results of the Hough

Static pose estimation from depth images using random regression forests and hough voting

by Brian Holt, Richard Bowden - In 7th International Conference on Computer Vision Theory and Applications (VISAPP , 2012
"... pose estimation, human body, regression forests, range image, hough transform, kinect Robust and fast algorithms for estimating the pose of a human given an image would have a far reaching impact on many fields in and outside of computer vision. We address the problem using depth data that can be ca ..."
Abstract - Cited by 3 (2 self) - Add to MetaCart
be captured inexpensively using consumer depth cameras such as the Kinect sensor. To achieve robustness and speed on a small training dataset, we formulate the pose estimation task within a regression and Hough voting framework. Our approach uses random regression forests to predict joint locations from each

Quantile Regression Forests

by Nicolai Meinshausen - JOURNAL OF MACHINE LEARNING RESEARCH , 2006
"... Random Forests were introduced as a Machine Learning tool in Breiman (2001) and have since proven to be very popular and powerful for high-dimensional regression and classification. For regression, Random Forests give an accurate approximation of the conditional mean of a response variable. It is sh ..."
Abstract - Cited by 47 (0 self) - Add to MetaCart
Random Forests were introduced as a Machine Learning tool in Breiman (2001) and have since proven to be very popular and powerful for high-dimensional regression and classification. For regression, Random Forests give an accurate approximation of the conditional mean of a response variable
Results 1 - 10 of 535