Next Article in Journal
Addendum: Pardo, J.; Zamora-Martínez, F.; Botella-Rocamora, P. Online Learning Algorithm for Time Series Forecasting Suitable for Low Cost Wireless Sensor Networks Nodes. Sensors 2015, 15, 9277–9304
Previous Article in Journal
A New Perspective on Fault Geometry and Slip Distribution of the 2009 Dachaidan Mw 6.3 Earthquake from InSAR Observations
Article Menu

Export Article

Open AccessArticle
Sensors 2015, 15(7), 16804-16830; doi:10.3390/s150716804

Integral Images: Efficient Algorithms for Their Computation and Storage in Resource-Constrained Embedded Vision Systems

School of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3SQ, UK
Department of Electrical Engineering, COMSATS Institute of Information Technology, Islamabad 44000, Pakistan
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M.N. Passaro
Received: 24 December 2014 / Revised: 27 June 2015 / Accepted: 2 July 2015 / Published: 10 July 2015
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [2069 KB, uploaded 10 July 2015]   |  


The integral image, an intermediate image representation, has found extensive use in multi-scale local feature detection algorithms, such as Speeded-Up Robust Features (SURF), allowing fast computation of rectangular features at constant speed, independent of filter size. For resource-constrained real-time embedded vision systems, computation and storage of integral image presents several design challenges due to strict timing and hardware limitations. Although calculation of the integral image only consists of simple addition operations, the total number of operations is large owing to the generally large size of image data. Recursive equations allow substantial decrease in the number of operations but require calculation in a serial fashion. This paper presents two new hardware algorithms that are based on the decomposition of these recursive equations, allowing calculation of up to four integral image values in a row-parallel way without significantly increasing the number of operations. An efficient design strategy is also proposed for a parallel integral image computation unit to reduce the size of the required internal memory (nearly 35% for common HD video). Addressing the storage problem of integral image in embedded vision systems, the paper presents two algorithms which allow substantial decrease (at least 44.44%) in the memory requirements. Finally, the paper provides a case study that highlights the utility of the proposed architectures in embedded vision systems. View Full-Text
Keywords: integral image; parallel architecture; memory-efficient design; embedded vision systems integral image; parallel architecture; memory-efficient design; embedded vision systems

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Ehsan, S.; Clark, A.F.; Rehman, N.U.; McDonald-Maier, K.D. Integral Images: Efficient Algorithms for Their Computation and Storage in Resource-Constrained Embedded Vision Systems. Sensors 2015, 15, 16804-16830.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top