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Such points usually lie on high-contrast regions of the image, such as object edges.ДЄnother important characteristic of these features is that the relative positions between them in the original scene shouldn't change from one image to another. To perform reliable recognition, it is important that the features extracted from the training image be detectable even under changes in image scale, noise and illumination. This description, extracted from a training image, can then be used to identify the object when attempting to locate the object in a test image containing many other objects.

( October 2010) ( Learn how and when to remove this template message)ДЇor any object in an image, interesting points on the object can be extracted to provide a 'feature description' of the object. Please help improve it to make it understandable to non-experts, without removing the technical details. This section may be too technical for most readers to understand.

Object matches that pass all these tests can be identified as correct with high confidence. Finally the probability that a particular set of features indicates the presence of an object is computed, given the accuracy of fit and number of probable false matches.

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Each cluster of 3 or more features that agree on an object and its pose is then subject to further detailed model verification and subsequently outliers are discarded. The determination of consistent clusters is performed rapidly by using an efficient hash table implementation of the generalised Hough transform.

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From the full set of matches, subsets of keypoints that agree on the object and its location, scale, and orientation in the new image are identified to filter out good matches. An object is recognized in a new image by individually comparing each feature from the new image to this database and finding candidate matching features based on Euclidean distance of their feature vectors. SIFT keypoints of objects are first extracted from a set of reference images and stored in a database. ДЄpplications include object recognition, robotic mapping and navigation, image stitching, 3D modeling, gesture recognition, video tracking, individual identification of wildlife and match moving. The scale-invariant feature transform ( SIFT) is a computer vision algorithm to detect, describe, and match local features in images, invented by David Lowe in 1999.

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