移动智能终端的SIFT特征检测并行算法

特征的检测和匹配在计算机视觉应用中是一个重要的组成部分,如图像匹配、物体识别和视频跟踪等。SIFT算法以其尺度不变性和旋转不变性在图像配准领域得到了广泛应用。传统的SIFT算法效率低,因此提出一种在移动智能终端上实现的高效方法。在Android平台利用OpenCL框架实现了移动智能终端的SIFT算法,通过计算任务的重新分配,优化SIFT算法在移动GPU上的并行实现。实验结果表明,移动平台的SIFT算法充分利用了GPU并行计算能力,大大提高了SIFT算法的执行效率,实现了高效的特征检测。

SIFT Feature Extraction Parallel Algorithm on Mobile Device

Feature extraction and matching is an important part in computervision applications,such as image matching,object recognition and video tracking.SIFT algorithm is widely used in the field of image registration because of its scale invariance and rotation invariance.To deal with the low efficiency of the traditional SIFT algorithm,we proposed an efficient method which is implemented on mobile platform.In this paper,we used the OpenCL to achieve the SIFT algorithm on mobile device,through redistributing the calculation of tasks and optimizing the SIFT algorithm in the mobile OpenCL parallel implementation.Experimental results show that our SIFT algorithm takes full advantage of the GPU parallel computing power,greatly improving the efficiency of the SIFT algorithm,achieving the efficient feature extraction.