httpy/opencv.willowgarage.com/wiki/. A book length treatment on learning OpenCV is
available by Bradski and Kaehler (2008). The proposed use ca^s require a computer
vision solution for object recognition, pattern identification, and then using computer
graphics such as OpenGL (www.opengl.org/aboutO for overlying graphical nelp onto
the camera view. Additionally, the augmented reality solutions described above
require infrastructure. Included in this necessary infrastructure are the databases that
provide recommendations or sum historical checkouts must be modeled and populated
ฆ and made accessible to web technologies.
This computing infrastructure includes a database model that leverages the
expertise of reference and instructional librarians. In some cases this modeled backend
is theoretically a repackaging of already existing knowledge and help resources and,
with mobile augmented reality applications, it can become more fully integrated into
the student's information environment.
It is advised to make use of RESTful methods to building the apps. As an example
of a RESTful method, consider the modern architecture of the web: a client such as
your mobile web browser requests a page over HTTP from a server. The server sends
the page by HTTP back to the browser. Augmented reality apps, for extensibility to
other mobile platforms and mobile web services should be built using this common
web-based architecture.
Research on web-based services to delivering AR data has been completed by
researchers at Nokia (Belimpasakis et aly 2010), showing the viability of a web-based
approach for designing mobile AR software. Further, researchers in geographic
information systems working to implement augmented reality solutions for cultural
intuitions have advocated such an approach in their experimentation with the data
modeling of mobile augmented reality applications (Marcus, 2011b).
Future work
In order to meet the challenge of the digital era in which access to both print and digital
is of ongoing importance, there are two broad options for mobile augmented reality
applications. The first option is for libraries to experiment with developing mobile
augmented applications in-house, applying their information access expertise to this
emerging domain. Library professionals may choose to internally develop services like
the OCR recommendation services that researchers at the University of Illinois
Undergraduate Library are pioneering. This involves setting up development
environments for research assistants, as well as recruiting and hiring research staff
who have the computing expertise to implement the experimental mobile systems.
A second approach is to use already existing projects outside libraries, but still
provide programming application interfaces or APIs. As an example of a third party
API, consider experimenting with a tool such as the Layar app: www.layar.com.
Investigation into the data transformations within Layar is discussed by (Osheim,
2011). Developing library frameworks on existing computer vision libraries compiled
in mobile platforms, like the OpenCV for Android project (http://code.opencv.org/
projects/opencv/wiki/OpenCV4Android) is a time efficient strategy for developing
functiona! prototypes. Already developed mobile augmented reality projects can be
adapted to library uses.
There are hybrid approaches to mobile application development, where third party
APIs can be incorporated into locally developed mobile computing applications.