IMAGE-PROCESSING WITH AUGMENTED REALITY (AR) HOSSEIN BABAEI Faculty of Information & Communication Technology, Limkokwing University of Creative Technology, Cyberjaya, Malaysia
[email protected]
PAGIEL L. MOHURUTSHE Faculty of Information & Communication Technology, Limkokwing University of Creative Technology, Cyberjaya, Malaysia
[email protected]
Abstract. In this project, the aim is to discuss and articulate the intent to create an image-based Android Application. The basis of this study is on real-time image detection and processing. It’s a new convenient measure that allows users to gain information on imagery right on the spot. Past studies have revealed attempts to create image based applications but have only gone up to creating image finders that only work with images that are already stored within some form of database. Android platform is rapidly spreading around the world and provides by far the most interactive and technical platform for smart-phones. This is why it was important to base the study and research on it. Augmented Reality in this project allows the user to manipulate the data and can add enhanced features (video, GPS tags) to the image taken.
user by mobilising tasks through countless applications making it even more convenient for them. This research covers reviewed articles from technological conventions [1]. Japan revealed that their contribution in image detection was to mark certain points on an image and retrieve data about it through emphasis on the histogram of the model and the pixel sizes. Other studies [2] included video skims that were integrated with audio skims to come up with shots or images in the form of frames .Detection by algorithm [3] are also illustrated. See below figure1 below.
Keywords: Mobile Application, Augmented Reality, Mobile (Android) AR, Image processing. 1.0 Introduction This project entails a full analysis and a step by step layout of the proposed application. This application will only work with camera-enabled Android smart phones. The user focuses the Smartphone then takes a picture of a sign, billboard or poster and the image taken is detected and processed and through this application, there is an output or display of results about the image. The main aim is that the results displayed will show relevant content on that particular sign. After reviewing articles, it came to general understanding that many experts in the past who attempted a similar project were mainly concerned with edgedetection and colour coding and intersection. The proposed application will help offline users acquire information about displayed signs and posters without having to browse the web or necessarily redirecting to other pages. 2.0 Background Study Google`s Android is a new platform for Smartphone’s that aims to simplify matters for a
Figure 1: Intensity changes using real-time images from a subway.
There have been several attempts as far as image processing is concerned. One of them includes a research by Chuan Qin who was visiting systems and networking research group at Duke University. The basis of his study was mainly on ‘TagSense’ [4] which he believed was easier envisioned than image processing. His argument was that tags on a picture could provide faster feedback and that the method was less technical. He mentioned also that Google’s Goggles provides poor tags.
Google has come up with a means to tackle image finding. It has introduced Google Goggles, visual
recognition software for Android that adds a notetaking feature helping users to have a search historyof images they had previously taken. The application is still fairly limited in its scope of its recognition capabilities. Currently it can only recognise common images or objects that are already stored online. It relies on tags when Goggles cannot find a similar image.Figure 2 illustrates this.
Figure 2: Clint Boulton, writer for eWEEK. Google Goggles 1.4 visual recognition software for Android
3.0 Problem Statement Image searching has been introduced, but the accuracy of the results is poor and yields many undesirable entries. Thus it is unable to provide specialized precise image resources. Moreover, it is difficult for mobile phones to process images, for which users end up spending a significant amount of time without receiving what they need. Mobile phone image processing technology is still very much ineffective and still cannot provide real-time responses.
3.1Problem solving with image detection The objectives of this project;
Tointegrate a high-efficiency, real-time architectural image-based application.
Tounify an efficientprocessing, storing and mining image-technology.
To enhance usage of Augmented Reality (videos, GPS) in Android phones allowing for improved result output where imagery is concerned.
4.0 Literature Review Many people design and implement applications at different levels for different reasons and are determined to optimise the results for the intended user. As computer processing becomes faster and cheaper, the opportunities for image-based applications become even more interesting. This technology is broad as it encompasses issues of security via face-recognition, human screening or even emotion detection [5]. Other attempts however have been made in order to realize successful detection, this includes the efforts made by the Windows platform in which they use image editor in gray level where they input low-quality pictures and transform them to high quality pictures. This may include image resizing, flip images or even rotation [6]. In other studies, researchers used the method of image detection in relation with solar images[7].This was a more eco-friendly approach and mainly used a camera, the sun rays and relevant angles to process and calculate the collected image to work out direction and elevation. There is evidence that image-detection may be used as a solution to health problems [8].This involves detection in ultrasound images by focusing on regions of interest (ROI`s).This can facilitate early disease-detection and diagnosis. We have other newer methods that are completely different from the traditional methods [9]. Images are detected in blocks rather than paying attention to the image as a whole. The study uses a `fuzzy function appropriation’ where the model can be expressed in variables, but that’s just their technique (vector). Table 1 below shows the detection types as discussed in section 4.0 summarized below. Table 1: detection types
Detection Type Face Recogniton Imageeditor(windows) Solar Images Ultrasound
Detection Description Not precise(requires database) Provides minimal features(grayscale) Depends on the climate changes Limited form of
Images Block images(variables)
detection. Only Taskspecific. technical form
Android and the iPhone have the ability to overlay information over an image providing additional features (figure 3)
5.0 Mobile Platform (Android, Palm OS, Windows Mobile, Linux and Symbian) Mobile devices promise to deliver now and for the years to come. Through such devices we are granted efficiency and convenience. Mobile platforms extend brands through easier browsing and helps grow revenue for some. The increasing importance of mobile devices has triggered competition between software giants such as Google, Apple, Palm and Microsoft. Since the launch of the apple Ios and Google`s Android, the market has exploded or grown exponentially. This part of the research however seeks to cover all ground including platforms by Nokia (Symbian) and rapidly-spreading Blackberry platform (OS from RIM). See the breakdown in figure 4 below.
Figure 3 Illustration of Augmented Reality on the iPhone
7.0 Methodology 7.1 Development Methodology The development method for this research is the Incremental Reversible Software Development Life Cycle (IR-SDLC).This method was selected as it deals closely with the intentions of the research by enhancing real-time. Solution allows you to capture organizational events that may escalate into incidents, evaluate incident criticality, and assign response based on impact and regulatory requirements. By so doing, users can also consolidate response procedures, manage investigations end-to-end, and report on trends and related incidents. Moreover, IR-SDLC allows for the maintenance feature which allows developers to correct flaws and to enhance the efficiency of the application.IR-SDLC defined in figure 5 below.
Figure 4 Pie-chart Distribution of different OS
6.0 MobileAR Mobile Based Augmented Reality is a new but quickly growing field of mobile applications. It allows integration of the information based virtual reality and the real world physical reality. This paper examines how this connection is made and what practical and enjoyable applications can be made from this technology. Currently the Google 7.2 Testing Methodology
Figure 5.Application Development Cycle
The testing methodology is adopted later in the project. The method type to be used in this project
is the application prototyping. The project cannot be implemented or given approval unless it qualifies as error-free or zero flaws. The testing phase of the project will focus mainly on the application design and interaction. Feedback will be collected accordingly. The prototype will be revised as below (The application prototype as in figure 6):
Building Prototype Evaluating Prototype Refining Prototype
[4] ChuanQinyx, June 28–July 1, 2011, TagSense: A Smartphone-based Approach to Automatic Image Tagging, Retrieved September 12, 2011 [5] Zahir Larabi, 2009, Efficient data access management for FPGA-Based image processing SoCs, Reviewd on October 4th 2011 [6] Omer M. Soysal, 2010, An Image Processing Tool for Efficient Feature Extraction in ComputerAided Detection Systems, last visited on October 4th 2011 [7] Luciano Godoy Fagundes, 2010, Development of Computer Graphics and Digital Image Processing Applications on the iPhone, Reviewd on October 4th 2011 [8] Hua Zhang, 2011, Design and Implementation of Digital Image Processing System, Last visited on October 4th, 2011
Figure 6: Application Prototype
8.0 Conclusion The project is primarily intended for users of an android platform enabled smart phones. The Android OS proved to be most suitable as over the years it has produced many widely used mobile applications that are functional and user friendly (largely received in the market).The significance of this project is to tackle existing technological challenges, to enable users’ enhanced mobile options. The project allows users to receive feedback on the spot (Real-time) and reduces delay. It offers a mobile user the convenience to acquire information with ease from a single image taken. Acknowledgement The special thank goes to our helpful advisor Dr. Arash Habibi Lashkari for his supervising and advising in the progression of our dissertation and project. References [1] V V Vinod, 1996, Focussed Color Intersection with Efficient Searching for Object Detection and Image Retrieval, Retrieved on October 4, 2011 [2] Michael A. Smith, 1997, Video Skimming and Characterization through the Combination of Image and Language Understanding Techniques, Last visited on September 18, 2011 [3] Stefan Huwer, 2000, Adaptive Change Detection for Real-Time Surveillance Applications, Lastvisited on September 21, 2011
[9] Lu Guiming, 2011, a New Additive Fuzzy System for Image Processing, retrieved on 5th October 2011
