Affine invariant based on determinant points in object recognition

The common affine invariants were very sensitive to the edge of the image, so a new method entitled ″an affine variant based on the determinant points in image recognition″ was proposed. First the centroid of the separated object image was computed, and then many line segments were derived through the centroid, at the end the nearest extreme grayscale points were found on every line. In order to compute the affine invariants, the extreme points were used to build up a collection of determinant points. The affine invariants of the collection could be served as an input vector of the trained neural networks to distinguish whether the source image was the destination image. The method was applied to the plane recognition and was proved to keep highly stable even if the object contour was ill-segmented or noisy. It is much easier and effective compared with the traditional methods and has a very wide scope of application.