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VLSI Design
Volume 2012 (2012), Article ID 602737, 17 pages
Research Article

Hardware Design Considerations for Edge-Accelerated Stereo Correspondence Algorithms

Department of Electrical and Computer Engineering, University of Cyprus, P.O. Box 20537, 1678 Nicosia, Cyprus

Received 25 February 2012; Accepted 23 March 2012

Academic Editor: Muhammad Shafique

Copyright © 2012 Christos Ttofis and Theocharis Theocharides. 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.


Stereo correspondence is a popular algorithm for the extraction of depth information from a pair of rectified 2D images. Hence, it has been used in many computer vision applications that require knowledge about depth. However, stereo correspondence is a computationally intensive algorithm and requires high-end hardware resources in order to achieve real-time processing speed in embedded computer vision systems. This paper presents an overview of the use of edge information as a means to accelerate hardware implementations of stereo correspondence algorithms. The presented approach restricts the stereo correspondence algorithm only to the edges of the input images rather than to all image points, thus resulting in a considerable reduction of the search space. The paper highlights the benefits of the edge-directed approach by applying it to two stereo correspondence algorithms: an SAD-based fixed-support algorithm and a more complex adaptive support weight algorithm. Furthermore, we present design considerations about the implementation of these algorithms on reconfigurable hardware and also discuss issues related to the memory structures needed, the amount of parallelism that can be exploited, the organization of the processing blocks, and so forth. The two architectures (fixed-support based versus adaptive-support weight based) are compared in terms of processing speed, disparity map accuracy, and hardware overheads, when both are implemented on a Virtex-5 FPGA platform.