Object Recognition Using Frequency Domain Blur Invariant Features

In this paper, we propose novel blur invariant features for the recognition of objects in images. The features are computed either using the phase-only spectrum or bispectrum of the images and are invariant to centrally symmetric blur, such as linear motion or defocus blur as well as linear illumination changes. The features based on the bispectrum are also invariant to translation, and according to our knowledge they are the only combined blur-translation invariants in the frequency domain. We have compared our features to the blur invariants based on image moments in simulated and real experiments. The results show that our features can recognize blurred images better and, in a practical situation, they are faster to compute using FFT.