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 recogniz...
✔️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 da...
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 ...
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