Harris Affine Region Detector - Overview

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 An Affine Invariant Interest Point Detector. 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, the use affine adapted feature points for wide baseline matching by Baumberg and the first use of scale invariant feature points by Lindeberg; see also 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:

1. Identify initial region points using scale-invariant Harris-Laplace Detector.
2. For each initial point, normalize the region to be affine invariant using affine shape adaptation.
3. Iteratively estimate the affine region: selection of proper integration scale, differentiation scale and spatially localize interest points..
4. Update the affine region using these scales and spatial localizations.
5. Repeat step 3 if the stopping criterion is not met.