针对目前效率最高的斑点检测算法—Fast Hessian算法的运行速度还无法满足目标识别与跟踪等对图像实时性要求较高的技术应用的问题, 提出了加速的Fast Hessian多尺度斑点特征检测算法来进一步提升Fast Hessian的运行速度。该算法从减少算法过程中滤波运算量的角度入手, 有选择地计算每个Octave首末两个尺度层中的采样点值; 与原算法在这两层中求取所有采样点值的方法进行比较, 提出的算法明显降低了滤波运算量, 从而缩短了斑点检测过程的耗时。实验结果表明, 加速的Fast Hessian算法在斑点检测结果上与原算法一致, 而运行速度比原算法提升了近40%, 因此更加适合实时应用。

As the origional Fast Hessian which is the most efficient blob feature detection algorithm can not meet requirements of those images in real-time applications to the target recognition, target tracking and so on, an accelerated Fast Hessian multi-scale blob feature detection algorithm is proposed to upgrade the detecting speed of the Fast Hessian. The basic idea of the proposed algorithm is to decrease the number of filter operations and to calculate selectively the values of sample points in the first and last scales for each Octave. Compared to the original one which calculates all the sample point values in these scales, the number of filter operations are distinctly decreased and the consuming time of detecting processing is also reduced. The experiments indicate that the accelerated Fast Hessian algorithm and the original one have the same detection results, but the implementation speed of the accelerated Fast Hessian is upgraded nearly 40% of the original one. It concludes that the accelerated algorithm is much more fit for real-time applications.