Analysing fundamental properties of marker-based vision system designs

Abstract

This paper investigates fundamental properties of Marker-based Vision (MBV) systems. We present a theoretical analysis of the performance of basic tag designs which is extended through simulation to investigate the effects of different processing algorithms. Real-world data are processed and related to the simulated results. Image processing is performed using Cantag, an open source software toolkit for building Marker-based Vision (MBV) systems that can identify and accurately locate printed markers in three dimensions. Cantag supports multiple fiducial shapes, payload types, data sizes and image processing algorithms in one framework. This paper explores the design space of tags within the Cantag system, and describes the design parameters and performance characteristics which an application writer can use to select the best tag system for any given scenario.

An Efficiency Analysis of Augmented Reality Marker Recognition Algorithm

“…Different types of markers can be used for reality augmentation, where according to [1] and [13] the most common markers are: bars (e.g., QR, DataMatrix, PDF-417), circles (e.g., CircleInner, CircleSplit, CircleOuter, SpotCode), squares (ID markers, binary), and images (e.g., StudierStube). In [1] it is shown that square marker with a thick border is the most accurate for marker-based systems.…”

“…To add more, markers are important AR tool because of their high degree of accuracy to capture [1], [4], [13]. The system should also be able to calculate the position of the camera using the detected marker.…”

“…It is of primary importance to define the conditions under which the AR system will operate correctly and to analyze the marker recognition algorithm effectiveness condition either [1], [13]. Several factors are available which can be analyzed while correctly identifying markers, i.e.…”

“…Several factors are available which can be analyzed while correctly identifying markers, i.e. the distance to the marker [13], [16], rotational angles and lighting conditions [2].…”

Robust Global Tracking Using a Seamless Structured Pattern of Dots

“…Circular markers such as TRIP [15] and InterSense's markers [19] also contain an identification tag, but the pose must be estimated using the shape of the projected circle, or with the help of multiple markers. According to Rice et al [20] square markers can contain more data, but the pose can be calculated more accurately with the help of circular markers. Another more recent form of fiducial markers are marker fields [23].…”