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Reconstructing depth map from a sequence of images with different camera settings, known as Depth from Focus (DFF), has been addressed for decades. However, Classical local methods, e.g., windowed filter, usually produce uncertain estimation in textureless areas and depth discontinuities. Meanwhile, they appear sensitive to filter size and image noise. In this research a semi-global depth from focus...
During the last two decades, a series of subspace methods have succeeded in achieving a satisfactory performance for face recognition tasks, but have always failed when partial occlusions occur. This paper combines the subspace techniques with probabilistic models, and aims at achieving invariance to occlusions. The concept underlying the proposed method is that two faces with the same identity, even...
In this paper, we propose a novel method for extracting a set of baseline-independent features, which are based on the combination of global and local information. A HMM-based recognition system is developed with 161 models that include a space model and a blank model. All of the models are trained using the standard Baum-Welch Algorithm with the state-tying technique, and are then decoded using the...
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