  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.
