High performance GPU based optimized feature matching for computer vision applications

详细信息    查看全文

• 作者：Kajal Sharma ; ^{kajal175@gmail.com" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Computer vision ; Graphics processing unit ; Self-organizing map ; Stereo vision
• 刊名：Optik - International Journal for Light and Electron Optics
• 出版年：2016
• 出版时间：February 2016
• 年：2016
• 卷：127
• 期：3
• 页码：1153-1159
• 全文大小：2273 K

文摘

In this paper, a graphics processing unit (GPU) based matching technique is proposed to perform fast feature matching between different images under various image conditions with viewpoint changes. Most recently, general-purpose graphics processing units (GPGPUs or GPUs) have become commonplace within high performance supercomputers. GPUs allow developers to effectively exploit the computational power for high performance computing. This paper focuses on improving the performance of feature matching based on self-organizing map by porting it onto the GPUs. GPU optimization has been applied for the fast computation of keypoints to make the system fast and efficient. This scheme has enhanced the overall performance and is much more efficient compared to other methods without degradation of detection results. The proposed matching algorithm is partitioned between the CPU and GPU in a way that best exploits the parallelism and perform matching between the different images. Experimental results demonstrate that fast feature matching is achieved using the graphics processing units, and its computational efficiency is checked by comparing its results and run times with those of some standard software (MATLAB) run on central processing unit (CPU).