Numerous methods have been developed to handle lighting variations in the preprocessing step of face recognition. However, most of them only use the high-frequency information (edges, lines, corner, etc.) for recognition, as pixels lied in these areas have higher local variance values, and thus insensitive to illumination variations. In this case, information of low-frequency may be discarded and some of the features which are helpful for recognition may be ignored. In this paper, we present a new and efficient method for illumination normalization using an energy minimization framework. The proposed method aims to remove the illumination field of the observed face images while simultaneously preserving the intrinsic facial features. The normalized face image and illumination field could be achieved by a reciprocal iteration scheme. Experiments on CMU-PIE and the Extended Yale B databases show that the proposed method can preserve a very good visual quality even on the images illuminated with deep shadow and high brightness regions, and obtain promising illumination normalization results for better face recognition performance.

Recently, numerous methods have been proposed to tackle the problem of fine-grained image classification. However, rare of them focus on the pre-processing step of image alignment. In this paper, we propose a new pre-processing method with the aim of reducing the variance of objects among the same class. As a result, the variance of objects between different classes will be more significant. The proposed approach consists of four procedures. The “parts” of the objects are firstly located. After that, the rotation angle and the bounding box could be obtained based on the spatial relationship of the “parts”. Finally, all the images are resized to similar sizes. The objects in the images possess the properties of translation, scale and rotation invariance after processed by the proposed method. Experiments on the CUB-200-2011 and CUB-200-2010 datasets have demonstrated that the proposed method could boost the recognition performance by serving as a pre-processing step of several popular classification algorithms.

20-Gbps non return-to-zero (NRZ) – binary phase shift keying (BPSK) using the silicon Mach-Zehnder modulator is demonstrated and characterized. Measurement of a constellation diagram confirms successful modulation of 20-Gbps BPSK with the silicon modulator. Transmission performance is characterized in the measurement of bit-error-rate in accumulated dispersion range from -347 ps/nm to +334 ps/nm using SMF and a dispersion compensating fiber module. Optical signal-to-noise ratio required for bit-error-rate of 10-3 is 10.1 dB at back-to-back condition. It is 1.2-dB difference from simulated value. Obtained dispersion tolerance less than 2-dB power penalty for bit-error-rate of 10-3 is -220 ps/nm to +230 ps/nm. The symmetric dispersion tolerance indicates chirp-free modulation. Frequency chirp inherent in the modulation mechanism of the silicon MZM is also discussed with the simulation. The effect caused by the frequency chirp is limited to 3% shift in the chromatic dispersion range of 2 dB power penalty for BER 10-3. The effect inherent in the silicon modulation mechanism is confirmed to be very limited and not to cause any significant degradation in the transmission performance.