affine invariant

17 examples (0.01 sec)
  • Provided that this iterative process converges, the resulting fixed point will be affine invariant.
  • In the area of computer vision, this idea has been used for defining affine invariant interest point operators as well as affine invariant texture analysis methods.
  • The goal of the affine invariant detector is to identify regions in images that are related through affine transformations.
  • Affine shape adaptation can also be used for affine invariant texture recognition and affine invariant texture segmentation.
  • The Hessian affine also uses a multiple scale iterative algorithm to spatially localize and select scale & affine invariant points.
  • Notice that this is an affine invariant construction since parallelism and midpoints are invariant under affine transformations.
  • To detect affine invariant region, the detector need to detect ellipse as in figure 4.
  • This modification increases the search space of the previous algorithm from a scale to a set of parameters and therefore the complexity of the affine invariant saliency detector increases.
  • 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.
  • The PCBR detector is a structure-based affine-invariant detector.
  • The Rihaczek and Choi-William distributions are examples of affine invariant Cohen's class distributions.
  • To summarize: Affine invariant saliency detector is invariant to affine transformation and able to detect more generate salient regions.
  • He pioneered the use of partial differential equations in computer vision and biomedical imaging co-inventing with Guillermo Sapiro an affine-invariant heat equation for image enhancement.
  • In practice the affine invariant saliency detector starts with the set of points and scales generated from the similarity invariant saliency detector then iteratively approximates the suboptimal parameters.
  • From the detection invariance point of view, feature detectors can be divided into fixed scale detectors such as normal Harris corner detector, scale invariant detectors such as SIFT and affine invariant detectors such as Hessian-affine.
  • 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).
  • 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.