A comparative study of parallel software SURF implementations

Abstract

SUMMARYNowadays, it is common to find problems that require recognizing objects in an image, tracking them along time, or recognizing a complete real‐world scene. One of the most known and used algorithms to solve these problems is the Speeded Up Robust Features (SURF) algorithm. SURF is a fast and robust local, scale and rotation invariant, features detector. This means that it can be used for detecting and describing a set of points of interest (keypoints) from an image. Because of the importance of this algorithm and the rise of the parallelism‐based technologies, in the last years, diverse parallel implementations of SURF have been proposed. These parallel implementations are based on very different techniques: Compute Unified Device Architecture, OpenMp, OpenCL, and so on. In conclusion, we think valuable a comparative study of all of them highlighting the advantages and disadvantages of each parallel implementation. To our best knowledge, this article is the first attempt to do this comparative study. In order to make this study, we have used the standard metrics and image collection in this field, as well as other important metrics in parallelism as speedup and efficiency. Copyright © 2013 John Wiley & Sons, Ltd.

Fast descriptor extraction method for a SURF‐based interest point

“…We can see that d(C, A) and d(B, D) are repeatedly computed in (5) and (6). This demonstrates that the computations of H h and H v based on (2) and (3) contain unnecessary operations. In this Letter, we propose a fast descriptor extraction method in which the combined filter, as shown in Since the PM accelerates the HWR step, it can be easily combined with existing fast methods.…”

“…Hence, a lot of research has been performed on applying the SURF algorithm to real-time applications. These studies have mainly focused on designing efficient hardware architecture [2,3] and developing parallel computing-based software solution [4,5].…”

Computer assisted diagnosis of bipolar disorder using invariant features

“…Speeded up robust features (SURF) 14,15 is a combination of local feature detector and descriptor. It focuses on scale and in-plane rotation-invariant detectors and descriptors.…”