Utilizing cloud storage for realistic augmented reality on mobile
by on with No Comments

ABSTRACT

As for today, augmented reality technologies are shifting towards small devices with a focus on user interaction. As more techniques in rendering AR objects are developed, more computing powers are needed to keep up. Mobile AR technology has all functions built in, in addition to GPS and compass for realistic AR rendering technology. However, mobile devices lack storage and the raw power for 3D rendering of complex objects. The paper discusses the possibility of integrating cloud to fix these problems, and conclude that using cloud for performance is difficult while using cloud for storage is possible. Results show that performance drop when utilizing cloud storage for 3D objects are minimal. As for now, cloud fetched objects are rendered without textures, leading to a reduce in realism compared to local fetched objects. Thus, the next step of the pro ject is implementing textures fetch from cloud DB on top of the 3D object file fetch.

Links: Github, Paper

 

Survey Paper
by on with No Comments

Week 1 March 30th

I have gone ahead and read more carefully about the chosen topic from my bibliography. Furthermore, I’m looking for some more papers to add to the bibliography list. I also have the general outline for the survey paper ready, and waiting to add more details.

 

Week 2 April 6th

  • I have skimmed through a few more papers to try to find something to add to the bibliography.
  • Try to improve the overall structure of the paper, haven’t been able to
  • Read carefully the papers already in the bibliography. Some of which doesn’t fit in the current context and needs to be replaced/removed.

Week 3 April 12th

  • Sit and think of some other projects that might befit this
  • Find papers about Cloud computing on mobile devices
  • Find some more paper about 3D augmented rendering on mobile devices
  • Find a good tutorial on beginner augmented reality (place a 3D cube on a spot with a mark)

Week 4 April 19th

  • Add a few articles to the reference list
  • Update cloud computing
  • Finish up the 2nd draft

Week 5 April 26th

  • Continue the paper
  • Looking at AR examples

Annotated Bibliography
by on with No Comments

Profiler:

1/ Real Time Face Detection

M. Sharif, K. Ayub, D. Sattar, M. Raza

Real Time Face Detection method by changing the RGB color space to HSB color space and try to detect the skin region. Then, try to detect the eye of the face in the region. The first step is face detection, second step is face verification. Time is crucial since it is real time. The published time is 2012.

http://sujo.usindh.edu.pk/index.php/SURJ/article/view/1553

https://www.researchgate.net/publication/257022696_Real_Time_Face_Detection

https://arxiv.org/abs/1503.03832

2/ FaceNet: A Unified Embedding for Face Recognition and Clustering:

A paper describing the method for face recognition using FaceNet system. FaceNet is a system by Google that allows high accuracy and speed in face detection mechanism. The accuracy of FaceNet system is 99.63% on the widely used Labeled Faces in the Wild (LFW) dataset.

Two Implementations of FaceNet for Face Recognition:

https://github.com/cmusatyalab/openface

https://github.com/davidsandberg/facenet

3/ http://www.cmlab.csie.ntu.edu.tw/~cyy/learning/papers/SVM_FaceCVPR1997.pdf

Virtual Space:

1/ Towards Massively Multi-User Augmented Reality on Handheld Devices

Develop a framework for implementing augmented reality interface on hand-held devices. There’s a graphical tool for developing graphical interfaces called PocketKnife, a software renderer called Klimt and a wrapper library that provides access to network sockets, threads and shared memory. In the end, they develop several AR games with the framework, such as the invisible train game.

2/ http://www.mitpressjournals.org/doi/abs/10.1162/pres.1997.6.4.355

Investor:

1/ Financial time series forecasting using support vector machines

Using support vector machine and compare the results with other methods of forecasting. The upper bound C and the gamma kernel parameter play an important role in the performance of SVMs. The prediction performance may be increased if the optimum parameters of SVM are selected.

C parameter is the parameter for how small will the hyperplane of largest minimum margin be.

2/ https://papers.nips.cc/paper/1238-support-vector-regression-machines.pdf

3/ http://link.springer.com/article/10.1023/A:1018628609742

Capstone Abstracts – v1
by on with No Comments

This paper will describe a project created using support vector machines (SVM) to predict stock price. Since the method is support vector machines, the data must be labeled, which fits what needed for stock evaluation. Stock’s information comes from its financial statements, which are all labeled. In this particular project, the version of SVM is a machine called least square support vector machines, which are used for regression analysis. The language being used is Python with scikit-learn, which has SVM implemented in the library.

This paper will describe a project using augmented reality (AR). AR is a live direct or indirect view of a physical, real world environment augmented (or supplemented) by computer-generated sensory input such as sound, video, graphics or GPS data. For this particular project, I will use Swift to implement a iOS app to provide users a augmented reality graphical view with supplemented GPS information. The application will take the user’s location and give additional information about the POI around the areas on the phone when the POI shows up.

This paper will describe a project using Machine Learning for Real-time Face Detection and Recognition using the mobile’s camera and compare the result to college’s student database. The paper will allow people to connect easily by knowing the name, location and mobile number with just a look on the phone. The program will run on iOS and Android using Cordova as a base.