In this paper we discuss the importance of the choice of features in digital
image object recognition. The features can be classified as invariants or noninvariants.
Invariant features are robust against one or more modifications such
as rotations, translations, scaling and different light (illumination) conditions.
Non-invariant features are usually very sensitive to any of these modifiers. On
the other hand, non-invariant features can be used even in the event of
translation, scaling and rotation, but the feature choice is in some cases more
important than the training method. If the feature space is adequate then the
training process can be straightforward and good classifiers can be obtained.
In the last few years good algorithms have been developed relying on noninvariant
features. In this article, we show how non-invariant features can cope
with changes even though this requires additional computation at the detection
phase. We also show preliminary results for a hand detector based on a set of
cooperative Haar-like feature detectors. The results show the good potential of
the method as well as the challenges to achieve real-time detection.
Barczak, A.L.C., Dadgostar, F. (2005), Real-time hand tracking using a set of cooperative classifiers based on Haar-like features, Research Letters in the Information and Mathematical Sciences, 7, 29-42