Near-field coded aperture imaging is known to have superior image resolution and count sensitivity over conventional parallel-hole collimated nuclear imaging. There have been several studies in image reconstruction for two-dimensional planar objects using the coded aperture imaging technology. However, coded aperture imaging for three-dimensional (3D) objects has not been extensively investigated. In this paper, a 3D reconstruction method for near-field coded aperture imaging is presented. We first introduce the "out-of-focus" correction factor into the generic expectation maximization (EM) algorithm for 3D near-field coded aperture images with the assumption that the photon emissions of coded aperture projections follow the Poisson statistics. The ordered subset expectation maximization (OSEM) method is then adapted for full 3D coded aperture image reconstruction. A 3D capillary tube phantom filled with 99mTc radioactive solution was used to evaluate the performance of our methods. A dual-head SPECT camera, one head quipped with a coded aperture module and the other with a parallel-hole collimator, was utilized for image acquisitions. Images were reconstructed using the modified EM and OSEM methods associated with the depth-dependent out-of-focus correction. The preliminary phantom results showed that our methods may have potential of reconstructing 3D near-field coded aperture images and also providing superior image resolution as compared to conventional parallel-hole collimated images.
In automatic face recognition, strong discriminatory feature extraction is very important. In this paper a new approach to extract powerful local discriminatory features is introduced. Instead of using traditional wavelet features, the authors examine multiscale local statistical characteristics to achieve strong discriminatory features based on important wavelet subbands. Meanwhile, to efficiently utilize potentials for the extracted multi- MLDFs, an integrated recognition system is developed, where multi-classifiers first conduct the corresponding coarse classification, then a decision fusion scheme by associating different priorities with each of the classifiers makes the final recognition. Our experiments showed this technique achieves superior performance to popular methods such as PCA/Eigenface, HMM, wavelet features, and neural networks, etc.
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