IMAGE MATTING TO EXTRACT FOREGROUND OBJECTS WITH COMPLEX BACKGROUNDS

Abstract

In computer vision, picture matting is an essential operation that includes removing things in the
foreground from photos with complicated backgrounds. Applications like virtual reality, object identification,
and picture editing all depend on this mechanism. In this study, we investigate the use of sophisticated methods
to provide precise and effective foreground object extraction, particularly color sampling and graph cuts. Color
sampling is the process of separating background and foreground pixels by examining the color distribution of
the picture. We use MATLAB to create a color sampling technique that iteratively incorporates color similarity
information to optimize the alpha matte, which represents pixel transparency. When the foreground and
background colors are comparable, this method improves the accuracy of foreground item extraction. Graph
cuts are an additional potent method for picture matting. We provide an approach that formulates the picture
matting issue as an energy reduction job, based on graph cuts. Alpha values that maximize the energy function
are effectively computed by building a graph representation of the picture. By using graph optimization tools,
our MATLAB implementation of the graph cuts technique guarantees quick and accurate foreground
extraction.We perform trials on a variety of photographs with complicated backgrounds in order to assess the
efficacy of our suggested solutions. Our findings show that the combined color sampling and graph cuts strategy
outperforms conventional matting techniques. A user-friendly platform for picture matting is provided by the
MATLAB implementation, making it simple for practitioners and academics to recover foreground items from
photos with complicated backgrounds