Paper Title
SMART IMAGE SEGMENTATION: DEVELOPING AN EFFICIENT BACKGROUND REMOVER

Abstract
This study proposes an advanced Android application for automated background removal from photographs, a critical task in image processing and computer vision. Unlike existing tools that require manual adjustments, the application leverages state-of-the-art techniques such as deep learning, probabilistic graphical models, and semantic segmentation to achieve precise and efficient results. Building on methods like DeepMask, SharpMask, and MultiPathNet, the app automatically distinguishes between foreground and background across diverse image types. It aims to offer a seamless user experience with a user-friendly interface, enabling users to create visually appealing images effortlessly. This paper explores the underlying algorithms and their applications in developing a versatile and effective solution. Keywords – Deep Mask, Sharp Mask, Multi Path Net