Automated Deep Alpha Matting for Vehicle Images

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Current methods for image matting are primarily based on manual boundary division and judgement, which**requires skilled editors and costs enormous amounts of time. More importantly, since the acquired images are**obtained by the human driven process of manual division, the process often results in errors within the**boundaries and detailed sections, which produces unnatural re-composited images. Alpha matting is a proposed**solution, which uses automatic technology to replace the manually based boundary division. By incorporating**the progress of deep learning technology in recent years with alpha matting, this yields higher accuracy as well**as wider application use cases. The proposed project will help to build the foundation of a long-term**collaboration between McMaster University and Car Media 2.0, which is a modern business-to-business**vehicle media provider that captures and edits photos of vehicles for the purposes of advertising throughout**US, Canada and Europe. It is anticipated that the use of this improved high-precision image matting system**will yield image matting processes that are more efficient, more accurate, and less costly. The competitive**advantage gained by this technology will help Car Media 2.0 to further grow and expand its business, thus**creating new employment opportunities in Canada. This project will also provide the participating students**with rich training experiences in deep learning, which is becoming an enabling technology in the big data era.