research-article

Paint selection

Authors:

Heung-Yeung ShumAuthors Info & Claims

SIGGRAPH '09: ACM SIGGRAPH 2009 papers

Article No.: 69, Pages 1 - 7

https://doi.org/10.1145/1576246.1531375

Published: 27 July 2009 Publication History

Abstract

In this paper, we present Paint Selection, a progressive painting-based tool for local selection in images. Paint Selection facilitates users to progressively make a selection by roughly painting the object of interest using a brush. More importantly, Paint Selection is efficient enough that instant feedback can be provided to users as they drag the mouse. We demonstrate that high quality selections can be quickly and effectively "painted" on a variety of multi-megapixel images.

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References

[1]

Adobe Photoshop. http://www.adobe.com/support/photoshop/.

[2]

An, X., and Pellacini, F. 2008. Appprop: all-pairs appearance-space edit propagation. ACM Trans. Graph. 27, 3, 1--9.

Digital Library

[3]

Bai, X., and Sapiro, G. 2007. A geodesic framework for fast interactive image and video segmentation and matting. In Proceedings of ICCV, 1--8.

[4]

Blake, A., Rother, C., Brown, M., Perez, P., and Torr, P. 2004. Interactive image segmentation using an adaptive gmmrf model. In Proceedings of ECCV.

[5]

Boykov, Y., and Jolly, M. P. 2001. Interactive graph cuts for optimal boundary & region segmentation of objects in n-d images. In Proceedings of ICCV, 105--112.

[6]

Boykov, Y., and Kolmogorov, V. 2001. An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. In Energy Minimization Methods in CVPR.

Digital Library

[7]

Chen, J., Paris, S., and Durand, F. 2007. Real-time edgeaware image processing with the bilateral grid. ACM Trans. Graph. 26, 3, 103.

Digital Library

[8]

Delong, A., and Boykov, Y. 2008. A scalable graph-cut algorithm for n-d grids. In Proceedings of CVPR, 1--8.

[9]

Grady, L. 2006. Random walks for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 28, 11, 1768--1783.

Digital Library

[10]

Kass, M., Witkin, A., and Terzopoulos, D. 1987. Snakes: Active contour models. IJCV 1, 4, 321--331.

[11]

Kopf, J., Cohen, M. F., Lischinski, D., and Uyttendaele, M. 2007. Joint bilateral upsampling. ACM Trans. Graph. 26, 3, 96.

Digital Library

[12]

Levin, A., Lischinski, D., and Weiss, Y. 2008. A closedform solution to natural image matting. IEEE Trans. Pattern Anal. Mach. Intell. 30, 2, 228--242.

Digital Library

[13]

Li, Y., Sun, J., Tang, C. K., and Shum, H. Y. 2004. Lazy snapping. ACM Trans. Graph. 24, 3, 303--308.

Digital Library

[14]

Li, Y., Adelson, E. H., and Agarwala, A. 2008. Scribble-boost: Adding classification to edge-aware interpolation of local image and video adjustments. In EGSR.

Digital Library

[15]

Lischinski, D., Farbman, Z., Uyttendaele, M., and Szeliski, R. 2006. Interactive local adjustment of tonal values. ACM Trans. Graph. 25, 3, 646--653.

Digital Library

[16]

Lombaert, H., Sun, Y., Grady, L., and Xu, C. 2005. A multilevel banded graph cuts method for fast image segmentation. In ICCV 2005, 259--265.

Digital Library

[17]

Mortensen, E. N., and Barrett, W. A. 1995. Intelligent scissors for image composition. In Proceedings of ACM SIGGRAPH.

Digital Library

[18]

Olsen, Jr., D. R., and Harris, M. K. 2008. Edge-respecting brushes. In UIST, 171--180.

Digital Library

[19]

Reese, L. J. 1999. Intelligent paint: Region-based interactive image segmentation. In Masters Thesis, Department of CS, Brigham Young University, Provo, UT.

[20]

Rother, C., Blake, A., and Kolmogorov, V. 2004. Grabcut - interactive foreground extraction using iterated graph cuts. ACM Trans. Graph. 24, 3, 309--314.

Digital Library

[21]

Vineet, V., and Narayanan, P. 2008. Cuda cuts: Fast graph cuts on the gpu. In Proceedings of CVPR Workshops.

[22]

Wang, J., and Cohen, M. F. 2005. An iterative optimization approach for unified image segmentation and matting. In Proceedings of ICCV, 936--943.

Digital Library

[23]

Wang, J., Agrawala, M., and Cohen, M. F. 2007. Soft scissors: an interactive tool for realtime high quality matting. ACM Trans. Graph. 27, 3, 9.

Digital Library

Cited By

Yang ZChen BZheng YChen XZhou K(2022)Human Bas-Relief Generation From a Single PhotographIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2021.309287728:12(4558-4569)Online publication date: 1-Dec-2022
https://doi.org/10.1109/TVCG.2021.3092877
Kaynar FMichl ASteinbach E(2022)Interactive RGB Image Segmentation via Depth-modified Click Encoding and Estimated Depth2022 IEEE International Symposium on Multimedia (ISM)10.1109/ISM55400.2022.00008(10-17)Online publication date: Dec-2022
https://doi.org/10.1109/ISM55400.2022.00008
Halperin THakim HVantzos OHochman GBenaim NSassy LKupchik MBibi OFried O(2021)Endless loopsACM Transactions on Graphics10.1145/3450626.345993540:4(1-12)Online publication date: 19-Jul-2021
https://dl.acm.org/doi/10.1145/3450626.3459935
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Index Terms

Paint selection
1. Computing methodologies
  1. Computer graphics
2. Theory of computation
  1. Randomness, geometry and discrete structures
    1. Computational geometry

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Published In

cover image ACM Conferences

SIGGRAPH '09: ACM SIGGRAPH 2009 papers

July 2009

795 pages

ISBN:9781605587264

DOI:10.1145/1576246

Conference Chair:
Thomas Funkhouser,
Editor:
Hugues Hoppe
ACM Transactions on Graphics

Copyright © 2009 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGGRAPH: ACM Special Interest Group on Computer Graphics and Interactive Techniques

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 July 2009

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SIGGRAPH09

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SIGGRAPH

SIGGRAPH09: Special Interest Group on Computer Graphics and Interactive Techniques Conference

August 3 - 7, 2009

Louisiana, New Orleans

Acceptance Rates

SIGGRAPH '09 Paper Acceptance Rate 78 of 439 submissions, 18%;

Overall Acceptance Rate 1,822 of 8,601 submissions, 21%

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Citations

Cited By

Yang ZChen BZheng YChen XZhou K(2022)Human Bas-Relief Generation From a Single PhotographIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2021.309287728:12(4558-4569)Online publication date: 1-Dec-2022
https://doi.org/10.1109/TVCG.2021.3092877
Kaynar FMichl ASteinbach E(2022)Interactive RGB Image Segmentation via Depth-modified Click Encoding and Estimated Depth2022 IEEE International Symposium on Multimedia (ISM)10.1109/ISM55400.2022.00008(10-17)Online publication date: Dec-2022
https://doi.org/10.1109/ISM55400.2022.00008
Halperin THakim HVantzos OHochman GBenaim NSassy LKupchik MBibi OFried O(2021)Endless loopsACM Transactions on Graphics10.1145/3450626.345993540:4(1-12)Online publication date: 19-Jul-2021
https://dl.acm.org/doi/10.1145/3450626.3459935
Zhou BXiong DWei WGui Y(2021)Fast and robust interactive image segmentation in bilateral space with reliable color modeling and higher order potentialJournal of Electronic Imaging10.1117/1.JEI.30.3.03301830:03Online publication date: 1-May-2021
https://doi.org/10.1117/1.JEI.30.3.033018
Miao JSun KLiao XLeng LChu J(2020)Human Segmentation Based on Compressed Deep Convolutional Neural NetworkIEEE Access10.1109/ACCESS.2020.30237468(167585-167595)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3023746
Lyu QChen MChen X(2020)Learning color space adaptation from synthetic to real images of cirrus cloudsThe Visual Computer10.1007/s00371-020-01990-7Online publication date: 14-Oct-2020
https://doi.org/10.1007/s00371-020-01990-7
Dolz JAyed IDesrosiers C(2017)DOPE: Distributed Optimization for Pairwise Energies2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR.2017.436(4095-4104)Online publication date: Jul-2017
https://doi.org/10.1109/CVPR.2017.436
Lin DDai JJia JHe KSun J(2016)ScribbleSup: Scribble-Supervised Convolutional Networks for Semantic Segmentation2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR.2016.344(3159-3167)Online publication date: Jun-2016
https://doi.org/10.1109/CVPR.2016.344
Aytar YZisserman A(2015)Multi-Task Multi-Sample LearningComputer Vision - ECCV 2014 Workshops10.1007/978-3-319-16199-0_6(78-91)Online publication date: 20-Mar-2015
https://doi.org/10.1007/978-3-319-16199-0_6
Sener OUgur KAlatan A(2014)Efficient MRF Energy Propagation for Video Segmentation via Bilateral FiltersIEEE Transactions on Multimedia10.1109/TMM.2014.231406916:5(1292-1302)Online publication date: Aug-2014
https://doi.org/10.1109/TMM.2014.2314069
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