Exploring Integral Image Word Length Reduction Techniques for SURF Detector
Exploring Integral Image Word Length Reduction Techniques for SURF Detector
复制标题
探索 SURF 检测器的整体图像字长缩减技术
DOI：
10.1109/iccee.2009.138
复制
发表时间：
2009
期刊：
影响因子：
--
通讯作者：
K. Mcdonald
中科院分区：
文献类型：
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作者：
Shoaib Ehsan;K. Mcdonald
加速鲁棒特征 (SURF) 是一种最先进的计算机视觉算法，它依靠积分图像表示来执行缩放和旋转不变的图像特征的快速检测和描述。然而，积分图像表示的主要缺点是二进制字长较大，这会导致内存大小大幅增加。在设计专用硬件以实现SURF算法的实时性能时，必须考虑积分图像对内存大小、总线宽度和计算资源的不利影响。以最小化硬件资源为目标，本文提出了一种基于缩减字长积分图像的SURF检测器的新颖实现概念。它针对 SURF 检测器的特定情况评估了两种现有的字长缩减技术，并扩展了其中一种技术以实现字长的进一步缩减。本文还介绍了一种实现 SURF 检测器整体图像字长缩减的新方法。
Speeded Up Robust Features (SURF) is a state of the art computer vision algorithm that relies on integral image representation for performing fast detection and description of image features that are scale and rotation invariant. Integral image representation, however, has major draw back of large binary word length that leads to substantial increase in memory size. When designing a dedicated hardware to achieve real-time performance for the SURF algorithm, it is imperative to consider the adverse effects of integral image on memory size, bus width and computational resources. With the objective of minimizing hardware resources, this paper presents a novel implementation concept of a reduced word length integral image based SURF detector. It evaluates two existing word length reduction techniques for the particular case of SURF detector and extends one of these to achieve more reduction in word length. This paper also introduces a novel method to achieve integral image word length reduction for SURF detector.