Template matching

Template matching is a technique in digital image processing for finding small parts of an image which match a template image. It can be used in manufacturing as a part of quality control, a way to navigate a mobile robot, or as a way to detect edges in images. Template matching is a technique in digital image processing for finding small parts of an image which match a template image. It can be used in manufacturing as a part of quality control, a way to navigate a mobile robot, or as a way to detect edges in images. The main challenges in the template matching task are: occlusion, detection of non-rigid transformations, illumination and background changes, background clutter and scale changes. Feature-based approach relies on the extraction of image features such, i.e. shapes, textures, colors, to match in the target image or frame. This approach is currently achieved by using Neural Networks and Deep Learning classifiers such as VGG, AlexNet, ResNet. Deep Convolutional Neural Networks process the image by passing it through different hidden layers and at each layer produce a vector with classification information about the image. These vectors are extracted from the network and are used as the features of the image. Feature extraction by using Deep Neural Networks is extremely effective and thus is the standard in state of the art template matching algorithms. This method is considered more robust and is state of the art as it can match templates with non-rigid and out of plane transformation, it can match with high background clutter and illumination changes. For templates without strong features, or for when the bulk of the template image constitutes the matching image, a template-based approach may be effective. As aforementioned, since template-based template matching may potentially require sampling of a large number of points, it is possible to reduce the number of sampling points by reducing the resolution of the search and template images by the same factor and performing the operation on the resultant downsized images (multiresolution, or pyramid), providing a search window of data points within the search image so that the template does not have to search every viable data point, or a combination of both. In instances where the template may not provide a direct match, it may be useful to implement the use of eigenspaces – templates that detail the matching object under a number of different conditions, such as varying perspectives, illuminations, color contrasts, or acceptable matching object “poses”. For example, if the user was looking for a face, the eigenspaces may consist of images (templates) of faces in different positions to the camera, in different lighting conditions, or with different expressions. It is also possible for the 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 image 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 any object in front of the camera for that matter. In cases where the object is malleable or poseable, motion also becomes a problem, and problems involving both motion and occlusion become ambiguous. In these cases, one possible solution is to divide the template image into multiple sub-images and perform matching on each subdivision. Template matching is a central tool in Computational anatomy (CA).The deformable template model models the space of human anatomies is an orbit under the group action of diffeomorphisms.Template matching arise as a problem in matching the unknown diffeomorphism that acts on the template to match the target image. Template matching algorithms in CA have come to be called large deformation diffeomorphic metric mapping (LDDMM); there are now LDDMM template matching algorithms for matching landmark points, curves, surfaces, volumes.