Features
The detection and description of local image features can help in object recognition. The SIFT features are local and based on the appearance of the object at particular interest points, and are invariant to image scale and rotation. They are also robust to changes in illumination, noise, and minor changes in viewpoint. In addition to these properties, they are highly distinctive, relatively easy to extract and allow for correct object identification with low probability of mismatch. They are relatively easy to match against a (large) database of local features but however the high dimensionality can be an issue, and generally probabilistic algorithms such as k-d trees with best bin first search are used. Object description by set of SIFT features is also robust to partial occlusion; as few as 3 SIFT features from an object are enough to compute its location and pose. Recognition can be performed in close-to-real time, at least for small databases and on modern computer hardware.
Read more about this topic: Scale-invariant Feature Transform
Famous quotes containing the word features:
“The features of our face are hardly more than gestures which force of habit made permanent. Nature, like the destruction of Pompeii, like the metamorphosis of a nymph into a tree, has arrested us in an accustomed movement.”
—Marcel Proust (1871–1922)
“Each reader discovers for himself that, with respect to the simpler features of nature, succeeding poets have done little else than copy his similes.”
—Henry David Thoreau (1817–1862)