Harris affine

18 examples (0.01 sec)
  • 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.