The optical chopper array based on Holographic Polymer Dispersed Liquid Crystal (H-PDLC) working at high
frequencies, for example 1KHz, 2KHz, and its application in an improved Frequency Division Multiplexed Fluorescence
Confocal Microscope (FDMFCM) system are reported in this article. The system is a combination of the confocal
microscopy and the frequency division multiplexing technique. Taking advantages of the optical chopper array based on
H-PDLC that avoids mechanical movements, the FDMFCM system is able to obtain better Signal-Noise Ratio (SNR),
smaller volume, more independent channels and more efficient scanning. What's more, the FDMCFM maintained the
high special resolution ability and realized faster temporal resolution than pervious system. Using the proposed device,
the FDMFCM system conducts successful parallel detection of rat neural cells. Fluorescence intensity signals from two
different points on the specimen, which represent concentration of certain kind of proteins in the sample cells, are
achieved. The experimental results show that the proposed H-PDLC optical chopper array has feasibility in FDMFCM
system, which owes to its unique characteristics such as fast response, simple fabrication and lower consumption etc.
With the development of H-PDLC based devices, there will be prospective in upgrading FDMFCM system's
performance in the biomedical area.
Unlike conventional video-based face recognition systems, in which the tracking and recognition are considered as two independent components, this paper presents a new integrated framework for simultaneously tracking and recognizing human faces. In this framework, tracking and recognition modules share the same appearance manifold. During training, because locally linear embedding (LLE) can detect the meaningful hidden structure of the nonlinear face manifold, LLE combined with K-means is employed to assign face images of every individual into clusters to construct view specific submanifolds. To improve the robustness of tracking and recognition, robust locality-preserving projection is developed to obtain linear subspaces that approximate the nonlinear submanifolds. Dynamics is also learned during this period. During testing, to reduce the great computational load, the integrated posterior probability is partitioned into two independent probabilities, which are obtained by a particle filter and by maximum posterior estimation by Bayesian inference, respectively. Extensive experimental results show that our proposed framework is effective for tracking and recognition under significant variations in pose, facial expression, and illumination and under scale variations and partial occlusion.
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