An efficient object extraction scheme from the low depth-of-field images

Abstract

This thesis describes a novel and efficient algorithm, which extracts focused objects from still images or video sequences with low depth-of-field (DOF), or even normal images with motion blur or homogenous color in background. Unlike the prior work[1][2][3], which utilities time-consuming morphological filters to obtain OOI candidate regions, the fast algorithm is proposed in this thesis to deal with not only low DOF images, but also low DOF image sequences. The algorithm unfolds into four modules. In the first module, a HOS map, in which the spatial distribution of the high-frequency components is represented, is obtained from an input low DOF image. The second module finds OOI candidate by using characteristics of the HOS. Since it is possible to contain some holes in the region, the third module detects and fills them. In order to obtain an OOI, the last module gets rid of background pixels in the OOI candidate. The experimental results show that the proposed method is highly useful in various applications, such as image indexing for content-based retrieval from huge amounts of image database, image analysis for digital cameras, and video analysis for virtual reality, immersive video system, photo-realistic video scene generation and video indexing system.