【作者】 罗斌；

【导师】 羌卫中；

【作者基本信息】 华中科技大学 ， 信息安全， 2015， 硕士

【摘要】 基于非线性尺度空间的特征提取算法,相比于基于高斯尺度空间的特征提取算法具有更高的鲁棒性,但同时也具有更高算法复杂度,不利于在图形处理器(Graphics Processor Unit,GPU)上实现并行优化。现有的基于图形处理器的高斯尺度空间的特征提取算法优化方案存在两个问题:算法并行度低,不能直接移植到非线性尺度空间。针对于非线性尺度空间的特点,为解决存在的问题,提出了一种新的基于图形处理器的非线性尺度空间特征提取算法优化机制。首先,在不降低算法鲁棒性的基础上,提出了一种分组尺度空间。在分组尺度空间中,将原始尺度空间中尺寸相同的层归为一组,各组之间相互独立,使得整个算法中各组之间能够并行的处理,提高算法的并行度。其次,为了消除分组尺度空间中降采样造成的负载不均衡问题,提出了一种数据打包方法。通过数据打包方法,既可以消除负载不均衡问题,又可以在图形处理器中实现各组并行处理。最后,在打包后的数据上,实现了基于图形处理器的分组尺度空间构造、关键点检测和描述符生成的并行优化。实验结果表明,相比于原始的非线性尺度空间特征提取算法,在鲁棒性有一定提高的前提下,可以得到6到22倍的加速比。即使相比于现有的基于图形处理器的尺度不变特征算法,在鲁棒性有大幅度提升的前提下,也可以得到1.5倍左右的加速比。更多还原

【Abstract】 Feature extraction algorithms in nonlinear scale space have much better robustness than ones in Gaussian scale space. However, these algorithms are very time-consuming. There were some shortcomings in the previous optimizations in Graphics Processor Unit(GPU) for these feature extraction algorithms in Gaussian scale space. These optimizations have low parallelism and cannot be implemented for feature extraction algorithms in nonlinear scale space directly.Based on the feature extraction algorithms in nonlinear scale space, a new GPU-based optimization mechanism is proposed. This optimization is used to overcome these shortcomings in previous optimizations. Firstly, based on the traditional scale space, a grouped scale space is proposed. The layers with same size are grouped into one group. In grouped scale space, all groups are independent of others. So that all groups can be proposed in parallel in GPU. Secondly, in order to eliminate the load imbalance of different sizes in grouped scale space, a data-package method is proposed. Thanks to the data-package method, not only the load imbalance can be eliminated, but also that all groups can be proposed in parallel in GPU. Finally, based on the packaged data, the scale space construction, keypoints detection and keypoints description are implemented in GPU.As the experimental result shows that, the proposed method can get 6~22x speedups with higher robustness when compared with the CPU-based feature extraction algorithms in nonlinear scale space. Even compared with the GPU-based SIFT algorithm, the proposed method can get 1.5x speedup with much higher robustness.更多还原