Modular Stereo Vision: Model and Implementation

Modular Stereo Vision: Model and Implementation

Ng Oon-Ee (Monash University Sunway Campus, Malaysia), Velappa Ganapathy (University of Malaya, Malaysia) and S.G. Ponnambalam (Monash University Sunway Campus, Malaysia)
Copyright: © 2012 |Pages: 23
DOI: 10.4018/978-1-61350-326-3.ch013

Abstract

The two-frame stereo vision algorithm is typically conceived of and implemented as a single process. The standard practice is to categorize individual algorithms according to the ‘type’ of process used. Evaluation is done based on the quality of the depth map produced. In this chapter, we demonstrate that the stereo vision process is actually composed of a number of inter-linked processes. Stereo vision is shown to be modular in nature; algorithms implementing it typically implement distinct stages of the entire process. The modularity of stereo vision implies that the specific methods used in different algorithms can be combined to produce new algorithms. We present a model describing stereo vision in a modular manner. We also provide examples of the stereo vision process being implemented in a modular manner, with practical example code. The purpose of this chapter is to present this model and implementation for the use of researchers in the field of computational stereo vision.
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Computational Stereo Vision

This section provides a brief introduction to computational stereo vision. Readers who are familiar with stereo vision and its implementation on computers should feel free to skip to the next section on Modular Stereo Vision.

The first sub-section describes the challenges inherent in stereo vision, and gives a general summary of how these challenges are tackled by existing algorithms. The second sub-section discusses how the typical stereo vision algorithm is considered to be a black box when compared against a competing algorithm and the background behind this understanding. The final sub-section briefly discusses the quantitative measurement of stereo vision result quality.

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