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中文摘要：
在基于原始SIFT算法的目标识别中，特征描述符的计算复杂，特征点的匹配时间较长，为此提出一种快速SIFT算法。该算法采用同心圆形窗口内的灰度累加值和差分值构建16维的简化描述符，并且在目标识别时，按照金字塔结构由粗至精进行特征点匹配。实验表明，在保证目标识别准确率的前提下，快速算法的运算时间比原始SIFT算法减少了两个数量级，具有很好的实时性能。
英文摘要：
In the object recognition based on original SIFT algorithm, the calculation of the feature descriptor is complex and the time of feature point matching is long, so a fast SIFT algorithm is proposed. The fast algorithm takes cumulative gray-scale value and its difference in a concentic circular window to construct a simplified feature descriptor with 16 dimension, and it matches feature points according to a coarse-to-fine pyramid structure when recognising the object. Experiments show that, under the premise of ensuring the recognition accuracy, the computation time of the proposed fast algorithm is reduced two orders of magnitude compared with the original SIFT algorithm, so the proposed algorithm has a good real-time performance.
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Two-dimensional local graph embedding discriminant analysis (2DLGEDA) with its application to face a
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