As computer vision experts, we have to handle almost daily with the information “hidden” on images in order to generate information “visible” and useful for our algorithms. In this entry I want to talk about the Image Matching process. The image matching is the technique used in Computer Vision to find enough patches or strong features in a couple – or more- images in order to be able to state that one of these images is contained on the other one, or that both images are the same image. For this purpose several approaches have been proposed in the literature, but we are going to focus on local features approaches.

     Local Feature representation of images are widely used for matching and recognition in the field of computer vision and, lately also used in  Augmented Reality applications by adding any augmented information on the real world. Robust feature descriptors such as SIFT, SURF, FAST, Harris-Affine or GLOH (to name some examples) have become a core component in those kind of applications. The main idea about it is first detect features and then compute a set of descriptors for these features. One important thing to keep in mind is that all these methods will be ported to mobile devices later, and they could drive to very heavy processes, not reaching real-time rates. Thus, several techniques are lately developed so that the features detection and descriptors extraction methods selected can be implemented in mobiles devices with a real-time performance. But this is another step I do not want to focus on in this entry.

Local Features

           But what is a local feature? A local feature is an image pattern which differs from its immediate neighborhood. This difference can be associated with a change of an image property, which the commonly considered are texture, intensity and color.

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