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Fully automatic feature localization for medical images using a global vector concentration approach.

, , , and . CVPR, IEEE Computer Society, (2008)

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Deep Depth From Aberration Map., , , and . ICCV, page 4069-4078. IEEE, (2019)Facial feature localization using weighted vector concentration approach., , , and . Image Vis. Comput., 28 (5): 772-780 (2010)An Efficient 3D Geometrical Consistency Criterion for Detection of a Set of Facial Feature Points., , and . IEICE Trans. Inf. Syst., 91-D (7): 1871-1877 (2008)Facial feature localization usingweighted vector concentration approach., , , and . FG, page 1-6. IEEE Computer Society, (2008)Physical Cue based Depth-Sensing by Color Coding with Deaberration Network., , , , and . BMVC, page 28. BMVA Press, (2019)COUNT Forest: CO-Voting Uncertain Number of Targets Using Random Forest for Crowd Density Estimation., , , and . ICCV, page 3253-3261. IEEE Computer Society, (2015)Random ensemble metrics for object recognition., , and . ICCV, page 1959-1966. IEEE Computer Society, (2011)Skip-Pose Vectors: Pose-based motion embedding using Encoder-Decoder models., and . MVA, page 1-6. IEEE, (2019)Cat face detection with two heterogeneous features., , , and . ICIP, page 1213-1216. IEEE, (2009)An Efficient 3D Geometrical Consistency Criterion for Detection of a Set of Facial Feature Points., , and . MVA, page 25-28. (2007)