Image matting is a basic feature in most of image quality improvement applications and is considered as a fundamental problem in the computer vision field. In this paper, an End-to-End Image Matting Platform is proposed to segment the foreground and background by creating an alpha matte image.
My end-to-end image matting algorithm is a learning based network which contains two stages. The first stage is to create a tri-map image from an RGB image using segmentation neural network where tri-map images are used to locate the expected foreground objects with rough outlines. The second stage is an image matting neural network, and it takes the outputs from the first stage as prior knowledge to predict precise alpha matte images. With the help of image matting formula and the outputs from the second stage, contents in an RGB image can be easily split into foreground and background. I applied both training and evaluating on Adobe matting benchmark and Car Media 2.0's car oriented image matting dataset, and the outcomes demonstrated the convenience and superior performance of our algorithm compared to existing state of the art methods.
This paper put forward a web platform structure to integrate deep learning algorithms. I applied multiple strategies to enhance the performance of the platform. By using this platform, multiple users can work with different deep learning applications at the same time which dramatically increases the efficiency of the server usage. / Thesis / Master of Applied Science (MASc)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/24738 |
| Date | January 2019 |
| Creators | Xiao, Botao |
| Contributors | Chen, Jun, Electrical and Computer Engineering |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
Page generated in 0.0017 seconds