ICTACT Journal on Image and Video Processing

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Structure Preserving Image Abstraction and Artistic Stylization from Complex Background and Low Illuminated Images


Affiliations
1 Department of Information Science and Engineering, Jawaharlal Nehru National College of Engineering, India
2 Department of Information Science and Engineering, Bapuji Institute of Engineering and Technology, India
3 Department of Studies in Computer Science, University of Mysore, India
4 Department of Mechanical Engineering, Jawaharlal Nehru National College of Engineering, India
5 Research and Development, Bapuji Institute of Engineering and Technology, India
     

   Subscribe/Renew Journal


The paper deals with NPR filtering and image processing techniques to produce the structure preserving image abstraction and artistic stylization effect from complex background and low illuminated images. Structure preserving image abstraction and stylization is most useful in the animation process, film industry, and artistic illustration and education sectors for innovative teaching. Abstraction concept reduces the image complexity and Stylization produces good visual effect to human’s sense. The work involves combining different NPR filtering techniques to create an effective NPR artistic illustration. The proposed technique consists of adoptive structure tensor flow, difference of Gaussian filter, 2D modified coherence shock filter, order dithering and Mean Curvature Flow (MCF). The work involves applying all these techniques in a series and the proposed scheme is found to give a good rendering effect on images with complex background and low luminance images. Moreover the proposed method does not require any kind of post processing techniques for abstraction and artistic stylization. The applied method produces the best abstraction effect and avoids halo effect. Implementation of proposed work is carried out in the Matlab environment. Efficiency of proposal work has been corroborated by conducting different experiments on various types of images and the results are compared with contemporary works. This approach is found to be computationally efficient in rendering effective structure preserving abstraction and stylization to the Human Visual System (HVS) and this approach opens up new research paths towards image and video stylization.

Keywords

Non-Photorealistic Rendering, Adoptive Structure Tensor Flow, Mean Curvature Flow, Order Dithering, Difference of Gaussian Filter, Shock Filtering.
Subscription Login to verify subscription
User
Notifications
Font Size

  • Regan L. Mandryk, David Mould and Hua Li, “Evaluation of Emotional Response to Non-Photorealistic Images”, Proceedings of Euro Graphics Symposium on Non-Photorealistic Animation and Rendering, pp. 7-16, 2011.

  • H.S. Nagendra Swamyand M.P. Pavan Kumar, “An Integrated Filter Based Approach for Image Abstraction and Stylization”, Multimedia Processing, Communication and Computing Applications, Vol. 213, pp. 241-252, 2013.

  • P. Kumar and N. Swamy, “Line Drawing for Conveying Shapes in HDR Images”, International Journal of Innovations in Engineering and Technology, Vol. 8, No. 3, pp. 866-872, 2013.

  • Paul Haeberli, “Paint by Numbers: Abstract Image Representations”, Proceedings of 17th Annual Conference on Computer Graphics and Interactive Techniques, pp. 1-12, 2014.

  • P. Haggerty, “Almost Automatic Computer Painting”, IEEE Computer Graphics and Applications, Vol. 11, No. 3, pp. 11-12, 1991.

  • K. Anjyo and K. Hiramitsu, “Stylized Highlights for Cartoon Rendering and Animation”, IEEE Computer Graphics and Applications, Vol. 23, No. 4, pp. 54-61, 2003.

  • J. Kyprianidis and J. Dollner, “Image Abstraction by Structure Adaptive Filtering”, Proceedings of International Conference on Theory and Practice of Computer Graphics, pp. 51-58, 2013.

  • Sven C. Olsen and Bruce Gooch, “Real Time Video Abstraction”, Proceedings of International Conference on Computer Graphics, pp. 1221–1226, 2006.

  • Jan Eric Kyprianidis, “Image and Video Abstraction by Multi-Scale Anisotropic Kuwahara Filtering”, Proceedings of International Euro Graphics Symposium on Non-Photorealistic Animation and Rendering, pp. 55-64, 2011.

  • Jan Eric Kyprianidis, John Collomosse, Tinghuai Wang and Tobias Isenberg, “State of the “Art”: A Taxonomy of Artistic Stylization Techniques for Images and Video”, IEEE Transactions on Visualization and Computer Graphics, Vol. 19, No. 5, pp. 866-885, 2013.

  • Y. Liu, M. Yu and Q.Fu, “Cognitive Mechanism Related to Line Drawings and its Applications in Intelligent Process of Visual Media: A Survey”, Frontiers of Computer Science, Vol. 10, pp. 216-232, 2016.

  • M.P.P. Kumar, B. Poornima and H.S. Nagendraswamy, “A Comprehensive Survey on Non-Photorealistic Rendering and Benchmark Developments for Image Abstraction and Stylization”, Iran Journal of Computer Science, Vol. 2, No. 1, pp. 1-35, 2019.

  • Thomas Brox, Rein Van Den Boomgaard, Francois Lauze, Joost Van De Weijer, Joachim Weickert and Pavel Mrazek, “Adaptive Structure Tensors and their Applications”, Visualization and Processing of Tensor Fields, Springer, pp. 17-47, 2006.

  • M. Marin and M. Velez-Reyes, “Enhancement of Flow-Like Structures in Hyperspectral Imagery using Tensor Nonlinear Anisotropic Diffusion”, Proceedings of Conference on International Society for Optical Engineering, pp. 1-12, 2011.

  • T. Ian and J. Younga Lucas, “Recursive Implementation of the Gaussian Filter”, Signal Processing, Vol. 44, No. 2, pp. 139-151, 1995.

  • G. Gomez, “Local Smoothness in Terms of Variance: Adoptive Gaussian Filter”, Proceedings of Conference on British Machine Vision, Vol 2, pp. 815-824, 2000.

  • Francesco Banterle, Alessandro Artusi, Elena Sikudova, Thomas Bashford-Rogers, Patrick Ledda, Marina Bloj and Alan Chalmers, “Dynamic Range Compression by Differential Zone Mapping based on Psychophysical Experiments”, Proceedings of ACM Symposium on Applied Perception, pp. 39-46, 2012.

  • F. Banterle, A. Artusi, K. Debattista and A. Chalmers, “Advanced High Dynamic Range Imaging: Theory and Practice”, CRC Press, 2011.

  • F. Drago, K. Myszkowski, C. Thomas and D. Fellner, “Adaptive Logarithmic Mapping for Displaying High Contrast Scenes”, Proceedings of European Association Annual Conference on Computer Graphics, pp. 419-426, 2003.

  • J. Weickert, “Coherence-Enhancing Diffusion Filtering”, International Journal of Computer Vision, Vol. 31, No. 2-3, pp. 111-127, 1999.

  • S. Osher and L. Rudin, “Feature-Oriented Image Enhancement using Shock Filters”, SIAM Journal on Numerical Analysis, Vol. 27, No. 4, pp. 919-940, 1990.

  • M. Grayson, “The Heat Equation Shrinks Embed Plane Curves to Round Points”, Journal of Differential Geometry, Vol. 26, No. 2, pp. 285-314, 1986.

  • H. Kang and S. Lee, Seungyong, “Shape-Simplifying Image Abstraction”, Computer Graphics Forum, Vol. 27, pp. 1773-1780, 2008.

  • S.M. Omohundro, “Floyd-Steinberg Dithering”, Proceedings of International Conference on Computer Science, pp. 1-12, 1947.

  • C. Tomasi and R. Manduchi, “Bilateral Filtering for Gray and Color Images”, Proceedings of 6th IEEE International Conference on Computer Vision, pp. 839-845, 1998.

  • G. Papari, N. Petkov and P. Campisi, “Artistic Edge and Corner Enhancing Smoothing”, IEEE Transactions on Image Processing, Vol. 16, No 10, pp. 2449-2462, 2007.

  • D. Hasler and E. Suesstrunk, “Measuring Colorfulness in Natural Images”, Proceedings of International Conference on Optical Engineering, pp. 87-95, 2003.

  • Kresimir Matkovic, Attila Neumann, Thomas Psik and Werner Purgathofer, “Global Contrast Factor-A New Approach to Image Contrast”, Proceedings of 1st Euro Graphics Conference on Computational Aesthetics in Graphics, Visualization and Imaging, pp. 159-167. 2005.

  • Penousal Machado and Amílcar Cardoso, “Computing Aethetics”, Proceedings of 14th Brazilian Symposium on Artificial Intelligence: Advances in Artificial Intelligence, pp. 219-228, 1998.

  • John Immerkaer, “Fast Noise Variance Estimation”, Computer Vision and Image Understanding, Vol. 64, No. 2, pp. 300-302. 1996.

  • K. Bahrami and A.C. Kot, “A Fast Approach for No-Reference Image Sharpness Assessment Based on Maximum Local Variation”, IEEE Signal Processing Letters, Vol. 21, No. 6, pp. 751-755, 2014.

  • S.M. Smith and J.M. Brady, “Susan-A New Approach to Low Level Image Processing”, International Journal of Computer Vision, Vol. 23, No. 1, pp. 45-78, 1997.

  • David Mould and Paul L. Rosin, “Developing and Applying A Benchmark for Evaluating Image Stylization”, Computer and Graphics, Vol. 67, No. 3, pp. 58-76, 2017.

  • H. Yeganeh and Z. Wang, “Objective Quality Assessment of Tone Mapped Images”, IEEE Transactions on Image Processing, Vol. 22, No. 2, pp. 657-667, 2013.

  • Yusra A.Y. Al-Najjar and Der Chen Soong, “Comparison of Image Quality Assessment: PSNR, HVS, SSIM, UIQI”, International Journal of Scientific and Engineering Research, Vol. 3, No. 8, pp. 1-12, 2012.

  • F.D.A.P.V. De Arruda, J.E.R. De Querioz and H.M. Gomes, “Non-Photorealistic Neural Sketching”, Journal of the Brazilian Computer Society, Vol. 18, pp. 237-250, 2012.

  • N. Venkatanath, D. Praneeth, M. Chandrasekhar, S.S. Channappayya and S.S. Medasani. “Blind Image Quality Evaluation Using Perception Based Features”, Proceedings of 21st National Conference on Communications, pp. 1-4, 2015.

  • A. Mittal, R. Soundararajan and A.C. Bovik, “Making a Completely Blind Image Quality Analyzer”, IEEE Signal Processing Letters, Vol. 22, No. 3, pp. 209-212, 2013.


Abstract Views: 259

PDF Views: 0




  • Structure Preserving Image Abstraction and Artistic Stylization from Complex Background and Low Illuminated Images

Abstract Views: 259  |  PDF Views: 0

Authors

M. P. Pavan Kumar
Department of Information Science and Engineering, Jawaharlal Nehru National College of Engineering, India
B. Poornima
Department of Information Science and Engineering, Bapuji Institute of Engineering and Technology, India
H. S. Nagendraswamy
Department of Studies in Computer Science, University of Mysore, India
C. Manjunath
Department of Mechanical Engineering, Jawaharlal Nehru National College of Engineering, India
B. E. Rangaswamy
Research and Development, Bapuji Institute of Engineering and Technology, India

Abstract


The paper deals with NPR filtering and image processing techniques to produce the structure preserving image abstraction and artistic stylization effect from complex background and low illuminated images. Structure preserving image abstraction and stylization is most useful in the animation process, film industry, and artistic illustration and education sectors for innovative teaching. Abstraction concept reduces the image complexity and Stylization produces good visual effect to human’s sense. The work involves combining different NPR filtering techniques to create an effective NPR artistic illustration. The proposed technique consists of adoptive structure tensor flow, difference of Gaussian filter, 2D modified coherence shock filter, order dithering and Mean Curvature Flow (MCF). The work involves applying all these techniques in a series and the proposed scheme is found to give a good rendering effect on images with complex background and low luminance images. Moreover the proposed method does not require any kind of post processing techniques for abstraction and artistic stylization. The applied method produces the best abstraction effect and avoids halo effect. Implementation of proposed work is carried out in the Matlab environment. Efficiency of proposal work has been corroborated by conducting different experiments on various types of images and the results are compared with contemporary works. This approach is found to be computationally efficient in rendering effective structure preserving abstraction and stylization to the Human Visual System (HVS) and this approach opens up new research paths towards image and video stylization.

Keywords


Non-Photorealistic Rendering, Adoptive Structure Tensor Flow, Mean Curvature Flow, Order Dithering, Difference of Gaussian Filter, Shock Filtering.

References