Automatic Age Estimation Based on Facial Aging Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discriminative Learning for Deformable Shape Segmentation: A Comparative Study
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Anytime learning for the NoSLLiP tracker
Image and Vision Computing
IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
Face verification across age progression using discriminative methods
IEEE Transactions on Information Forensics and Security
Age regression from faces using random forests
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Cross-modality assessment and planning for pulmonary trunk treatment using CT and MRI imaging
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part I
Regression forests for efficient anatomy detection and localization in CT studies
MCV'10 Proceedings of the 2010 international MICCAI conference on Medical computer vision: recognition techniques and applications in medical imaging
Laplacian eigenmaps manifold learning for landmark localization in brain MR images
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part II
Accurate regression-based 4D mitral valve surface reconstruction from 2D+t MRI slices
MLMI'11 Proceedings of the Second international conference on Machine learning in medical imaging
Example based non-rigid shape detection
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Interest points localization for brain image using landmark-annotated atlas
International Journal of Imaging Systems and Technology
Eye pupil localization with an ensemble of randomized trees
Pattern Recognition
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We present a general algorithm of image based regression that is applicable to many vision problems. The proposed regressor that targets a multiple-output setting is learned using boosting method. We formulate a multiple-output regression problem in such a way that overfitting is decreased and an analytic solution is admitted. Because we represent the image via a set of highly redundant Haar-like features that can be evaluated very quickly and select relevant features through boosting to absorb the knowledge of the training data, during testing we require no storage of the training data and evaluate the regression function almost in no time. We also propose an efficient training algorithm that breaks the computational bottleneck in the greedy feature selection process. We validate the efficiency of the proposed regressor using three challenging tasks of age estimation, tumor detection, and endocardial wall localization and achieve the best performance with a dramatic speed, e.g., more than 1000 times faster than conventional data-driven techniques such as support vector regressor in the experiment of endo-cardial wall localization.