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In the fields of computer vision and image analysis, the Harris affine region detector belongs to the category of feature detection. Feature detection is a preprocessing step of several algorithms that rely on identifying characteristic points or interest points so to make correspondences between images, recognize textures, categorize objects or build panoramas. == Overview == The Harris affine detector can identify similar regions between images that are related through affine transformations and have different illuminations. These ''affine-invariant'' detectors should be capable of identifying similar regions in images taken from different viewpoints that are related by a simple geometric transformation: scaling, rotation and shearing. These detected regions have been called both ''invariant'' and ''covariant''. On one hand, the regions are detected ''invariant'' of the image transformation but the regions ''covariantly'' change with image transformation.〔 Do not dwell too much on these two naming conventions; the important thing to understand is that the design of these interest points will make them compatible across images taken from several viewpoints. Other detectors that are affine-invariant include Hessian affine region detector, Maximally stable extremal regions, Kadir–Brady saliency detector, edge-based regions (EBR) and intensity-extrema-based regions (IBR). Mikolajczyk and Schmid (2002) first described the Harris affine detector as it is used today in (Affine Invariant Interest Point Detector'' ).〔(Mikolajcyk, K. and Schmid, C. 2002. An affine invariant interest point detector. In ''Proceedings of the 8th International Conference on Computer Vision'', Vancouver, Canada. )〕 Earlier works in this direction include use of affine shape adaptation by Lindeberg and Garding for computing affine invariant image descriptors and in this way reducing the influence of perspective image deformations,〔(T. Lindeberg and J. Garding (1997). "Shape-adapted smoothing in estimation of 3- depth cues from affine distortions of local 2- structure". Image and Vision Computing 15: pp 415—434. )〕 the use affine adapted feature points for wide baseline matching by Baumberg〔(A. Baumberg (2000). "Reliable feature matching across widely separated views". Proceedings of IEEE Conference on Computer Vision and Pattern Recognition: pages I:1774—1781. )〕 and the first use of scale invariant feature points by Lindeberg;〔(Lindeberg, Tony, Scale-Space Theory in Computer Vision, Kluwer Academic Publishers, 1994 ), ISBN 0-7923-9418-6〕〔(T. Lindeberg (1998). "Feature detection with automatic scale selection". International Journal of Computer Vision 30 (2): pp 77—116. )〕 for an overview of the theoretical background. The Harris affine detector relies on the combination of corner points detected thorough Harris corner detection, multi-scale analysis through Gaussian scale space and affine normalization using an iterative affine shape adaptation algorithm. The recursive and iterative algorithm follows an iterative approach to detecting these regions: # Identify initial region points using scale-invariant Harris-Laplace Detector. # For each initial point, normalize the region to be affine invariant using affine shape adaptation. # Iteratively estimate the affine region: selection of proper integration scale, differentiation scale and spatially localize interest points.. # Update the affine region using these scales and spatial localizations. # Repeat step 3 if the stopping criterion is not met. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Harris affine region detector」の詳細全文を読む スポンサード リンク
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