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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.
- The Harris affine detector can identify similar regions between images that are related through affine transformations and have different illuminations.
- Like the Harris affine detector, Hessian affine interest regions tend to be more numerous and smaller than other detectors.
- The Harris affine detector relies on interest points detected at multiple scales using the Harris corner measure on the second-moment matrix.
- Typically 30% of the Harris affine points are distinct and dissimilar enough to not be discarded.
- The Harris affine detector relies heavily on both the Harris measure and a Gaussian scale space representation.
- The computational complexity of the Harris-Affine detector is broken into two parts: initial point detection and affine region normalization.
- Because the Harris affine algorithm looks at each initial point given by the Harris-Laplace detector independently, there is no discrimination between identical points.
- Thus far, objects which have good edge features or blob features have been successfully recognized; for example detection algorithms, see Harris affine region detector and SIFT, respectively.
- For a single image, the Hessian affine detector typically identifies more reliable regions than the Harris-Affine detector.
- As such, intensity-based interest operators (e.g., SIFT, Harris-Affine)-and the object recognition systems based on them-often fail to identify discriminative features.
- The Hessian affine detector algorithm is almost identical to the Harris affine region detector.
- The implementation of this algorithm is almost identical to that of the Harris affine detector; however, the above mentioned Hessian measure replaces all instances of the Harris corner measure.
- Like the Harris affine algorithm, these interest points based on the Hessian matrix are also spatially localized using an iterative search based on the Laplacian of Gaussians.
- Using this mathematical framework, the Harris affine detector algorithm iteratively discovers the second-moment matrix that transforms the anisotropic region into a normalized region in which the isotropic measure is sufficiently close to one.
- 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.
- In Mikolajczyk et al., six region detectors are studied (Harris-affine, Hessian-affine, MSER, edge-based regions, intensity extrema, and salient regions).
- The Hessian affine detector is part of the subclass of feature detectors known as affine-invariant detectors: Harris affine region detector, Hessian affine regions, maximally stable extremal regions, Kadir-Brady saliency detector, edge-based regions (EBR) and intensity-extrema-based (IBR) regions.