Difference between revisions of "IS480 Team wiki: 2012T1 M.O.O.T/Project Overview/Learning Outcomes"
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| style="background: #FFFFFF; padding-bottom: 20px; padding: 5px 10px 0 18px; width: 40%;" valign="top" | C# programming | | style="background: #FFFFFF; padding-bottom: 20px; padding: 5px 10px 0 18px; width: 40%;" valign="top" | C# programming | ||
| style="background: #FFFFFF; padding-bottom: 20px; padding-left: 16px; width: 60%;" | | | style="background: #FFFFFF; padding-bottom: 20px; padding-left: 16px; width: 60%;" | | ||
+ | Programming for Kinect is done in either C# or Visual Basics. We have chosen to code in C#, using Microsoft Visual Studio. | ||
+ | |||
+ | |- | ||
+ | | style="background: #FFFFFF; padding-bottom: 20px; padding: 5px 10px 0 18px; width: 40%;" valign="top" | Machine learning | ||
+ | | style="background: #FFFFFF; padding-bottom: 20px; padding-left: 16px; width: 60%;" | | ||
+ | Introduced into the concept of machine learning by our supervisor, we are taking the plunge: exploring C# neural network, backward propagation, Sigmoid's alpha value, and more to eventually making the machine smarter over time as more gender recognition has been executed | ||
|- | |- | ||
| style="background: #FFFFFF; padding-bottom: 20px; padding: 5px 10px 0 18px; width: 40%;" valign="top" | Microsoft Tag application | | style="background: #FFFFFF; padding-bottom: 20px; padding: 5px 10px 0 18px; width: 40%;" valign="top" | Microsoft Tag application | ||
| style="background: #FFFFFF; padding-bottom: 20px; padding-left: 16px; width: 60%;" | | | style="background: #FFFFFF; padding-bottom: 20px; padding-left: 16px; width: 60%;" | | ||
+ | We make use of Microsoft Tag to direct shopper to relevant advertisement or promotion, which will also send data to Promotion / Advertisement Management System | ||
|- | |- | ||
| style="background: #FFFFFF; padding-bottom: 20px; padding: 5px 10px 0 18px; width: 40%;" valign="top" | Kinect set up & calibration | | style="background: #FFFFFF; padding-bottom: 20px; padding: 5px 10px 0 18px; width: 40%;" valign="top" | Kinect set up & calibration | ||
| style="background: #FFFFFF; padding-bottom: 20px; padding-left: 16px; width: 60%;" | | | style="background: #FFFFFF; padding-bottom: 20px; padding-left: 16px; width: 60%;" | | ||
+ | Setting Kinect up to play XboX games is different from setting it up for AlterSense: we have learnt to set the tilting of angle, adjusting the background color, etc | ||
|- | |- | ||
| style="background: #FFFFFF; padding-bottom: 20px; padding: 5px 10px 0 18px; width: 40%;" valign="top" | Gender recognition methods | | style="background: #FFFFFF; padding-bottom: 20px; padding: 5px 10px 0 18px; width: 40%;" valign="top" | Gender recognition methods | ||
| style="background: #FFFFFF; padding-bottom: 20px; padding-left: 16px; width: 60%;" | | | style="background: #FFFFFF; padding-bottom: 20px; padding-left: 16px; width: 60%;" | | ||
+ | An increasingly popular yet newly ventured area for us - we explore physical differences based on published research, try doing it with the help of Kinect, as well as developing a sensible scoring system to eventually determine shopper's gender | ||
|- | |- | ||
| style="background: #FFFFFF; padding-bottom: 20px; padding: 5px 10px 0 18px; width: 40%;" valign="top" | Human Computer Interaction | | style="background: #FFFFFF; padding-bottom: 20px; padding: 5px 10px 0 18px; width: 40%;" valign="top" | Human Computer Interaction | ||
| style="background: #FFFFFF; padding-bottom: 20px; padding-left: 16px; width: 60%;" | | | style="background: #FFFFFF; padding-bottom: 20px; padding-left: 16px; width: 60%;" | | ||
− | + | Natural user interface is a novel concept and apparently there are a lot of learning and experiments we have to undergo in order to better understand how to make AlterSense intuitive enough in terms of gestures | |
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− | |||
− | |||
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| style="background: #FFFFFF; padding-bottom: 20px; padding: 5px 10px 0 18px; width: 40%;" valign="top" | 3D programming: x, y, z & depth | | style="background: #FFFFFF; padding-bottom: 20px; padding: 5px 10px 0 18px; width: 40%;" valign="top" | 3D programming: x, y, z & depth | ||
| style="background: #FFFFFF; padding-bottom: 20px; padding-left: 16px; width: 60%;" | | | style="background: #FFFFFF; padding-bottom: 20px; padding-left: 16px; width: 60%;" | | ||
+ | We have to put ourselves in the Kinect's shoes - every pixel consists of x, y, and z values - to be able to process image, insert image, and many more | ||
|- | |- |
Revision as of 15:34, 3 September 2012
Project Description | Project Goals | Client's Requirements | Deliverables | Learning Outcomes |
Lessons Learnt | ||
S/N | Learning Outcomes | How We Are Going To Achieve Them |
1. | Hard skills | |
C# programming |
Programming for Kinect is done in either C# or Visual Basics. We have chosen to code in C#, using Microsoft Visual Studio. | |
Machine learning |
Introduced into the concept of machine learning by our supervisor, we are taking the plunge: exploring C# neural network, backward propagation, Sigmoid's alpha value, and more to eventually making the machine smarter over time as more gender recognition has been executed | |
Microsoft Tag application |
We make use of Microsoft Tag to direct shopper to relevant advertisement or promotion, which will also send data to Promotion / Advertisement Management System | |
Kinect set up & calibration |
Setting Kinect up to play XboX games is different from setting it up for AlterSense: we have learnt to set the tilting of angle, adjusting the background color, etc | |
Gender recognition methods |
An increasingly popular yet newly ventured area for us - we explore physical differences based on published research, try doing it with the help of Kinect, as well as developing a sensible scoring system to eventually determine shopper's gender | |
Human Computer Interaction |
Natural user interface is a novel concept and apparently there are a lot of learning and experiments we have to undergo in order to better understand how to make AlterSense intuitive enough in terms of gestures | |
3D programming: x, y, z & depth |
We have to put ourselves in the Kinect's shoes - every pixel consists of x, y, and z values - to be able to process image, insert image, and many more | |
2. | Soft skills | |
Idea pitching | ||
Decentralized teamwork | ||
Retail domain knowledge | ||
Stakeholder management | ||
IT-business communication | ||
Corporate presentation skills | ||
Cross-industry idea application |