(untitled) (Journal of Computational Mathematics, 2018)
The aim of the electron microscopy image classification is to categorize the projection images into different classes according to their similarities. Distinguishing images usually requires that these images are aligned first. However, alignment of images is a difficult task for a highly noisy data set. In this paper, we propose a translation and rotation invariant based on the Fourier transform for avoiding alignment. A novel classification method is therefore established. To accelerate the classification speed, secondary-classes are introduced in the classification process. The test results also show that our method is very efficient and effective. Classification results using our invariant are also compared with the results using other existing invariants, showing that our invariant leads to much better results.