GrabcutD: improved grabcut using depth information
Proceedings of the 2010 ACM workshop on Surreal media and virtual cloning
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An advanced image and video segmentation system is proposed. The system builds on existing work, but extends it to achieve efficiency and robustness, which are the two major shortcomings of segmentation methods developed so far. Six different schemes containing several approaches tailored for diverse applications constitute the core of the system. The first two focus on very-low complexity image segmentation addressing real-time applications under specific assumptions. The third scheme is a highly efficient implementation of the powerful nonlinear diffusion model. The other three schemes address the more complex task of physical object segmentation using information about the scene structure or motion. These techniques are based on an extended diffusion model and morphology. The main objective of this work has been to develop a robust and efficient segmentation system for natural video and still images. This goal has been achieved by advancing the state-of-art in terms of pushing forward the frontiers of current methods to meet the challenges of the segmentation task in different situations under reasonable computational cost. Consequently, more efficient methods and novel strategies to issues for which current approaches fail are developed. The performance of the presented segmentation schemes has been assessed by processing several video sequences. Qualitative and quantitative result of this assessment are also reported