%Aigaion2 BibTeX export from Idiap Publications
%Thursday 21 November 2024 12:44:33 PM

@INPROCEEDINGS{Ali_ICCV_2009,
         author = {Ali, Karim and Fleuret, Francois and Hasler, David and Fua, Pascal},
       projects = {Idiap, IM2},
          title = {Joint Pose Estimator and Feature Learning for Object Detection},
      booktitle = {Proceedings of the IEEE International Conference on Computer Vision},
           year = {2009},
       abstract = {A new learning strategy for object detection is presented.  The
proposed scheme forgoes the need to train a collection of detectors
dedicated to homogeneous families of poses, and instead learns a
single classifier that has the inherent ability to deform based on the
signal of interest.

Specifically, we train a detector with a standard AdaBoost procedure
by using combinations of pose-indexed features and pose estimators
instead of the usual image features. This allows the learning process
to select and combine various estimates of the pose with features able
to implicitly compensate for variations in pose. We demonstrate that a
detector built in such a manner provides noticeable gains on two hand
video sequences and analyze the performance of our detector as these
data sets are synthetically enriched in pose while not increased in
size.}
}