Lazy snapping

In this paper, we present Lazy Snapping, an interactive image cutout tool. Lazy Snapping separates coarse and fine scale processing, making object specification and detailed adjustment easy. Moreover, Lazy Snapping provides instant visual feedback, snapping the cutout contour to the true object boundary efficiently despite the presence of ambiguous or low contrast edges. Instant feedback is made possible by a novel image segmentation algorithm which combines graph cut with pre-computed over-segmentation. A set of intuitive user interface (UI) tools is designed and implemented to provide flexible control and editing for the users. Usability studies indicate that Lazy Snapping provides a better user experience and produces better segmentation results than the state-of-the-art interactive image cutout tool, Magnetic Lasso in Adobe Photoshop.

[1]  Donald Geman,et al.  Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Luc Vincent,et al.  Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  William A. Barrett,et al.  Intelligent scissors for image composition , 1995, SIGGRAPH.

[4]  Michael Gleicher,et al.  This document was created with FrameMaker 4.0.4 Image Snapping , 2022 .

[5]  William A. Barrett,et al.  Toboggan-based intelligent scissors with a four-parameter edge model , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[6]  David Salesin,et al.  A Bayesian approach to digital matting , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[7]  Marie-Pierre Jolly,et al.  Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[8]  Narendra Ahuja,et al.  Selecting objects with freehand sketches , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[9]  Patrick Pérez,et al.  JetStream: probabilistic contour extraction with particles , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[10]  William A. Barrett,et al.  Image Editing with Intelligent Paint , 2002, Eurographics.

[11]  William A. Barrett,et al.  Object-based image editing , 2002, ACM Trans. Graph..

[12]  Irfan A. Essa,et al.  Graphcut textures: image and video synthesis using graph cuts , 2003, ACM Trans. Graph..

[13]  Jerry Alan Fails,et al.  A design tool for camera-based interaction , 2003, CHI '03.

[14]  Vladimir Kolmogorov,et al.  An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision , 2004, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Harry Shum,et al.  Pop-up light field: An interactive image-based modeling and rendering system , 2004, TOGS.

[16]  Vladimir Kolmogorov,et al.  "GrabCut": interactive foreground extraction using iterated graph cuts , 2004, ACM Trans. Graph..

[17]  David Salesin,et al.  Interactive digital photomontage , 2004, SIGGRAPH 2004.