Editing Template matching (section)

==Common challenges==

In instances where the template may not provide a direct match, it may be useful to implement [[eigenspace]]s to create templates that detail the matching object under a number of different conditions, such as varying perspectives, illuminations, [[Contrast (vision)|color contrasts]], or object [[pose (computer vision)|poses]].<ref>Luis A. Mateos, Dan Shao and Walter G. Kropatsch. [https://link.springer.com/content/pdf/10.1007/978-3-642-10268-4_104.pdf Expanding Irregular Graph Pyramid for an Approaching Object]. CIARP 2009: 885-891.</ref>  For example, if an algorithm is looking for a face, its template eigenspaces may consist of images (i.e., templates) of faces in different positions to the camera, in different lighting conditions, or with different expressions (i.e., poses).

It is also possible for a matching image to be obscured or occluded by an object. In these cases, it is unreasonable to provide a multitude of templates to cover each possible occlusion. For example, the search object may be a playing card, and in some of the search images, the card is obscured by the fingers of someone holding the card, or by another card on top of it, or by some other object in front of the camera. In cases where the object is malleable or poseable, motion becomes an additional problem, and problems involving both motion and occlusion become ambiguous.<ref>F. Jurie and M. Dhome. [https://www.researchgate.net/profile/Michel_Dhome/publication/221260111_Real_Time_Robust_Template_Matching/links/00b49527217af23045000000/Real-Time-Robust-Template-Matching.pdf Real time robust template matching]. In British Machine Vision Conference, pages 123–131, 2002.</ref> In these cases, one possible solution is to divide the template image into multiple sub-images and perform matching on each subdivision.