  A novel template matching algorithm that can incorporate the concept of
deformable parts, is presented in this paper. Unlike the deformable part model
(DPM) employed in object recognition, the proposed template-matching approach
called Deformable Template Matching (DTM) does not require a training step.
Instead, deformation is achieved by a set of predefined basic rules (e.g. the
left sub-patch cannot pass across the right patch). Experimental evaluation of
this new method using the PASCAL VOC 07 dataset demonstrated substantial
performance improvement over conventional template matching algorithms.
Additionally, to confirm the applicability of DTM, the concept is applied to
the generation of a rotation-invariant SIFT descriptor. Experimental evaluation
employing deformable matching of SIFT features shows an increased number of
matching features compared to a conventional SIFT matching.
