No abstract

We propose radial harmonic Fourier moments, which are shifting, scaling, rotation, and intensity invariant. Compared with Chebyshev-Fourier moments, the new moments have superior performance near the origin and better ability to describe small images in terms of image-reconstruction errors and noise sensitivity. A multidistortion-invariant pattern-recognition experiment was performed with radial harmonic Fourier moments.

In this paper, firstly, a new intuitionistic fuzzy (IF) entropy has been put forward, which considered both the uncertainty and the hesitancy degree of IF sets. Through comparing with other entropy measures, the advantage of the new entropy measure is obvious. Secondly, based on the new entropy measure, a new decision making method of a multi-attribute decision making problem was subsequently put forward, in which attribute values are expressed with IF values. In the cases of attribute weights, completely unknown and attribute weights are partially known. Two methods were constructed to determine them. One method is an extension of the ordinary entropy weight method, and the other method is a construction the optimal model according to the minimum entropy principle. Finally, two practical examples are given to illustrate the effectiveness and practicability of the proposed method.