affine detector

16 examples (0.01 sec)
  • However, for some structured scenes, like buildings, the Hessian affine detector performs very well.
  • 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 Hessian affine detector responds well to textured scenes in which there are a lot of corner-like parts.
  • The Harris affine detector relies on interest points detected at multiple scales using the Harris corner measure on the second-moment matrix.
  • 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.
  • Like other feature detectors, the Hessian affine detector is typically used as a preprocessing step to algorithms that rely on identifiable, characteristic interest points.
  • For a single image, the Hessian affine detector typically identifies more reliable regions than the Harris-Affine detector.
  • Furthermore, using these initially detected points, the Hessian affine detector uses an iterative shape adaptation algorithm to compute the local affine transformation for each interest point.
  • Overall, the Hessian affine detector performs second best to MSER.
  • 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.
  • 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.
  • 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.