Robust Real-Time Face Detection
International Journal of Computer Vision
Image Based Regression Using Boosting Method
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
FIMH '09 Proceedings of the 5th International Conference on Functional Imaging and Modeling of the Heart
Machine learning for high-speed corner detection
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Foundations and Trends® in Computer Graphics and Vision
Interest points localization for brain image using landmark-annotated atlas
International Journal of Imaging Systems and Technology
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The identification of anatomical landmarks in medical images is an important task in registration and morphometry. Manual labeling is time consuming and prone to observer errors. We propose a manifold learning procedure, based on Laplacian Eigenmaps, that learns an embedding from patches drawn from multiple brain MR images. The position of the patches in the manifold can be used to predict the location of the landmarks via regression. New images are embedded in the manifold and the resulting coordinates are used to predict the landmark position in the new image. The output of multiple regressors is fused in a weighted fashion to boost the accuracy and robustness. We demonstrate this framework in 3D brain MR images from the ADNI database. We show an accuracy of ∼0.5mm, an increase of at least two fold when compared to traditional approaches such as registration or sliding windows.