Foreground Object Extraction Using Variation of Blurs Based on Camera Focusing

Abstract

Image foreground object extraction is still challenging yet interesting topic in visual computing area. This paper proposes foreground extraction method based on one of the basic camera functions, focusing. A focus point and variation of blurs are extracted based on focusing, and these can be essential information to extract the foreground image. A focus point shows the rough position of the foreground, and variation of blurs becomes a distinctive image feature to segment foreground and background. The major contributions of this paper are developing a framework to extract a foreground object based on a camera function, and estimating the blur with the matching position between images using SIFT (Scale Invariant Feature Transform). SIFT is able to detect and describe image features which are invariant to scale, rotation and luminance changing. Moreover, scale-space extrema in SIFT can be used to estimate the variation of blurs. This is shown experimentally. Proposed foreground extraction is conducted in three steps. First, sparse variation of blurs between images is computed using SIFT. Next, the sparse variation of blurs is propagated using EAI (Edge Aware Interpolation) and a full variation of blur map is generated. Finally, foreground object extraction is obtained by Graph Cut algorithm using a focus point as a constraint of the object. Experimental evaluation result shows improvement in performance of foreground object extraction using variation of blurs.