1. Higher School of Economics , Moscow, 2016
<Topic>
Prepared by:
<Master's student name>
System and Software Engineering
Supervised by:
<Supervisor name>
Faculty of Computer Science/School of Software Engineering
June, 2019
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2. Contents 2
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3. Problem Statement
Basically we use colored tips for detection which are captured by webcam.
Here, the colored fingertip acts as an object which the web cam senses.
The camera is positioned such that it recognizes the moment of finger tips and performs the operations of mouse.
The utilization of virtual mouse appears in space saving situations or in movement situation.
In periods of prolonged use, users' arms began to feel fatigue and/or discomfort. This effect contributed to the decline of touch-screen input. 5
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4. Project Scope 7
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5. Goal and Objectives
For image-based gesture recognition there are limitations on the equipment used and image noise. Images or video may not be under consistent lighting, or in the same location. Items in the background or distinct features of the users may make recognition more difficult.
The variety of implementations for image-based gesture recognition may also cause issue for viability of the technology to general usage. For example, an algorithm calibrated for one camera may not work for a different camera.
Furthermore, the distance from the camera, and the camera's resolution and quality, also cause variations in recognition accuracy. 8
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6. Ways of Solving Tasks
The Azure Mobile Apps Services provides lot of useful functions when building a backend for mobile applications.
These services were used to build the architecture from storage facilities (Azure SQL) to mobile service (which is aim to build the backend, incorporate push notification, integration of social networks and allows single sign-on authentication), 9
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7. Proposed Methods
To recognize a hand gesture, we need to resize the input image in order to map camera coordinates to screen coordinates.
Skin detection is detecting the skin colour pixels in an image. The input image from the webcam would be in the RGB colour space, thus it would have to be converted to the HSV colour space using the conversion Formulae. The Histogram-based skin detection method proposed by uses 32 bins H and S histograms to achieve skin detection.
To perform edge detection to obtain the hand contour in the image, there are several edge detection methods such as, Laplacian edge detection, canny edge detection and border finding. The OpenCV function – [cvFindContours()] uses a order finding edge detection method to find the contours in the image.
Consumed Endurance (CE) is a metric that captures the degree of arm fatigue during mid-air interactions. 10
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8. Tools and Technologies11
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9. Expected Results 12
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10. Advantages 13
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11. References 14
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12. Questions please!