Galois Field-based Approach for Rotation and Scale Invariant Texture Classification

Abstract

In this paper, a novel Galois Field-based approach is proposed for rotation and scale invariant texture classification. The commutative and associative properties of Galois Field addition operator are useful for accomplishing the rotation and scale invariance of texture representation. Firstly, the Galois field operator is constructed, which is applied to the input textural image. The normalized cumulative histogram is constructed for Galois Field operated image. The bin values of the histogram are considered as rotation and scale invariant texture features. The classification is performed using the K-Nearest Neighbour classifier. The experimental results of the proposed method are compared with that of Rotation Invariant Local Binary Pattern (RILBP) and Log-Polar transform methods. These results obtained using the proposed method are encouraging and show the possibility of classifying texture successfully irrespective of its rotation and scale.

Improved Method of López-Dahab-Montgomery Scalar Point Multiplication in Binary Elliptic Curve Cryptography

“…Review the multiplication results of different orders curves (from 32 to 4096) by the scalars of different byte length (from 16 to 512), the following results are obtained: the average increase value is 4-5% (20 points per scalar were used). As mentioned above, considering all the processors working at the same time, multiprocessor systems that the encryption technologies based on Galois Fields theory will obtain even bigger improvements [16].…”