  A new method is proposed to get image features' geometric information. Using
Gaussian as an input signal, a theoretical optimal solution to calculate
feature's affine shape is proposed. Based on analytic result of a feature
model, the method is different from conventional iterative approaches. From the
model, feature's parameters such as position, orientation, background
luminance, contrast, area and aspect ratio can be extracted. Tested with
synthesized and benchmark data, the method achieves or outperforms existing
approaches in term of accuracy, speed and stability. The method can detect
small, long or thin objects precisely, and works well under general conditions,
such as for low contrast, blurred or noisy images.
