shape adaptation

The algorithm uses this shape adaptation matrix, , to transform the image into a normalized reference frame.

For each initial point, normalize the region to be affine invariant using affine shape adaptation.

The affine region normalization algorithm automatically detects the scale and estimates the shape adaptation matrix, .

Such functions are often used in image processing and in computational models of visual system function - see the articles on scale space and affine shape adaptation.

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.

Affine shape adaptation can also be used for affine invariant texture recognition and affine invariant texture segmentation.

Equivalently, affine shape adaptation can be accomplished by iteratively warping a local image patch with affine transformations while applying a rotationally symmetric filter to the warped image patches.

To obtain true Galilean invariance, however, also the shape of the spatio-temporal window function needs to be adapted, corresponding to the transfer of affine shape adaptation from spatial to spatio-temporal image data.

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 structure tensor also plays a central role in the Lucas-Kanade optical flow algorithm, and in its extensions to estimate affine shape adaptation; where the magnitude of is an indicator of the reliability of the computed result.

Affine shape adaptation is a methodology for iteratively adapting the shape of the smoothing kernels in an affine group of smoothing kernels to the local image structure in neighbourhood region of a specific image point.

It has been noted that parasitism by haematozoa is higher in colonial birds and it has been suggested that blood parasites might have shaped adaptations such as larger organs in the immune system and life-history traits.

In practice, the affine shape adaptation process described here is often combined with interest point detection automatic scale selection as described in the articles on blob detection and corner detection, to obtain interest points that are invariant to the full affine group, including scale changes.

Besides the commonly used multi-scale Harris operator, this affine shape adaptation can also be applied to other types of interest point operators such as the Laplacian/Difference of Gaussian blob operator and the determinant of the Hessian (Lindeberg 2008).

To handle such non-linear deformations locally, partial invariance (or more correctly covariance) to local affine deformations can be achieved by considering affine Gaussian kernels with their shapes determined by the local image structure, see the article on affine shape adaptation for theory and algorithms.

Furthermore, the scale levels obtained from automatic scale selection can be used for determining regions of interest for subsequent affine shape adaptation to obtain affine invariant interest points or for determining scale levels for computing associated image descriptors, such as locally scale adapted N-jets.