Local Phase Quantization (LPQ) Descriptors
1、New Texture Descriptors
Inspired by the huge success of the Local Binary Pattern (LBP) method, we have also proposed new local texture descriptors, including the blur-insensitive Local Phase Quantization (LPQ) method and the descriptor based on Weber's law (WLD) that have provided state-of-the-art performance e.g. in face recognition and texture classification problems. Both LPQ and WLD are related but complementary to the LBP method.
2、Local Phase Quantization (LPQ)
Fourier phase spectrum can be shown to be invariant to image blurring with centrally symmetric point spread functions (PSF) at such frequencies where the Fourier transform of the PSF is positive. In many cases real image blur can be approximated by Gaussian blur, linear motion blur, or disk-shaped blur that all have centrally symmetric PSFs. Local Phase Quantization (LPQ) is a novel texture descriptor that utilizes the blur invariance property of the phase spectrum. It is based on binary coding of the quantized Fourier phase computed locally around each pixel. Because of the limited resolution of the local phase information, the descriptor obtained is not completely invariant to the blur, but it has been experimentally verified that it is still highly insensitive to moderate blurring. Also, for sharp images LPQ has proved to be an extremely powerful descriptor. In comparative studies it has outperformed other texture descriptors such as LBP. Applications of LPQ include face recognition and medical image analysis.
