Exploring Integral Image Word Length Reduction Techniques for SURF Detector

TL;DR

This paper investigates techniques to reduce the binary word length of integral images in SURF detectors, aiming to optimize hardware resource usage for real-time computer vision applications.

Contribution

It evaluates existing word length reduction methods and introduces a new approach specifically tailored for SURF integral images to minimize hardware resource requirements.

Findings

Significant reduction in integral image word length achieved

Improved hardware efficiency demonstrated for SURF detector

Extended method outperforms existing techniques in resource savings

Abstract

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.