Sign Language Recognition on Android

-
 Sign Language Recognition on Android

OUTPUT


Project Idea and Goals

For our project, we would like to develop an algorithm for human hand detection and in-terpretation. This means that we not only want to identify human hands in images, but also correctly model their pose and classify gestures. Although this algorithm has many varied applications such as human-machine interface commands or machine understanding of human behavior, we will use this algorithm specifically to detect and interpret American Sign Language gestures.

Specific goals for the project include (1) live video detection and conversion of sign language to text in real time and (2) mobile implementation on an Android device.

Proposed Methodology

In order to do this idea justice, we plan to organize the project into an image processing component for hand segmentation and modeling of the gesture (for EE 368), and a machine learning component for hand pose interpretation (for CS 229). We would like the image processing component of this project to detect both the position and configuration of human hands. This will make the overall algorithm more robust in terms of the camera angle,lighting conditions, etc.
The first step should be a form of segmentation that can identify human skin tones/ hand geometry. We then intend to include some redundant measures of identifying hands, in order to counter scenarios in which one method might fail to pick up the presence of a hand. We have already identified potential datasets (such as this American Sign Language Lexicon Video Dataset) to use in development.
Once a region of interest has been identified as containing a hand, the last detection step would involve creating a model of the hand pose. The features extracted from this model would then be provided to the machine learning component of the project. We will initially implement this algorithm on still images, and then tackle video streams if time permits.

References

• Kuznetsova, et.al. ”Real-time sign language recognition using a depth camera”
• J. Isaacs, S. Foo. ”Hand pose estimation for American sign language recognition.”

FOR BASE PAPER PLEASE MAIL US AT ARNPRSTH@GMAIL.COM

DOWNLOAD PROJECT SOURCE CODE :CLICK HERE

Comments

Post a Comment

Popular posts from this blog

Light Field Images for Background Removal

-
Image
Light Field Images for Background Removal OUTPUT OVERVIEW Standard edge detection or foreground/background separation techniques, such as Otzu’smethod, require color or intensity differences between the background and regions that need tobe separated. For example, green screens are routinely set up as the background in a scene so thatthere is a clear difference in color between the background and foreground.Light filed images, captured in 4D and passively containing depth information for thescene can be used to approximate this effect. Depth estimation in the scene alone could provide a metric to separate the foreground and background, but more sophisticated methods are available.Considering the edge detection from a single image from a single viewpoint and analyzing the depths from the light field around the edges in that image, occluded edges and intensity or color edges can be distinguished. Along with the rest of the depth information this can allow the foreground and b
Read more

Using Image Processing to Identify and Score Darts thrown into a Dartboard

-
Image
✔️Using Image Processing to Identify and Score Darts thrown into a Dartboard ✔️Introduction The game of darts is a throwing sport in which participants toss projectiles into a circular target  attached to a vertical surface. The target is divided into many regions which correspond to different point and multiplier values. Darts is commonly played across North America and Europe and is a popular past time for many so an application that allows players to more conveniently keep score would be widely  beneficial. “Darts” is a general term for a targeting game following this basic premise and many game variations exist within this archetype; all utilizing the standard dart projectiles with a regulation dart board. Each game variant may have different objectives for which players to aim but identifying the region in  which the dart has hit in the dartboard is necessary for proper scoring. This project proposes the use of image processing to identify thrown darts and to det
Read more

Digital Make up Face Generation

-
Image
Digital Make up Face Generation Goal Current make-up applications rely on using photoshop tools to apply makeup on the target's digital  faces and generate results. While these applications allow customization, a customer who wants to  quickly decide on the type of makeup kit to buy at a store will not find it useful. The customer might just  want to find out how the make-up look on the cover or billboard will look like on her face. The goal of  this project is to use an existing reference image of another subject with a make-up applied, and  transfer the reference's make up on the target's face. The application can be further extended to photo  retouching and illumination transfer from the reference image to the target. Methodology In order to transfer the make-up from the reference onto target in a pixel by pixel basis, the areas of  interest must align. Face features such as eyes, nose, mouth and contours of the face, will be recognized  using Active Shape
Read more