Abstract

Image segmentation is one of the most important research directions in computer fields and is also an important component of pattern recognition. In RoboCup competitions, the accurate positioning of the target object is particularly important to the control of the robots. An image segmentation method based on edge and color features is proposed in the present paper. Firstly, the image pixel color features are extracted in multi-color space, and the edge features of the objects are extracted by an adaptive Canny operator. The color features set the entropy, mean, and variance of the pixels in the component image. Then the color and edge features of the image are clustered by Fuzzy C-means. The clustering result of FCM is used as the input of the RBF-SVM model. Finally, the training model is applied to segment the image and output the position of the target object. The present experiment first verified the performance of the multi-color space model by simulation experiments, then conducted image segmentation experiments in images from RoboCup competition, and used Region Circularity and Boundary Center Deviation as evaluation indicators to analyze the performance of the algorithm. The experimental results showed that this method has better performance in image segmentation. Compared with existing methods, it reduced the segmentation time and improved the image segmentation performance.

© 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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References

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  1. H. Stokman and T. Gevers, “Selection and fusion of color models for image feature detection,” IEEE Trans. Pattern Anal. Mach. Intell. 29(3), 371–381 (2007).
    [Crossref]
  2. N. R. Pal and S. K. Pal, “A review on image segmentation techniques,” Pattern Recognition 26(9), 1277–1294 (1993).
    [Crossref]
  3. N. Otsu, “A Threshold Selection Method from Gray-Level Histograms,” IEEE Trans. Syst., Man, Cybern. 9(1), 62–66 (1979).
    [Crossref]
  4. J. N. Kapur, P. K. Sahoo, and A. K. C. Wong, “A new method for gray-level picture thresholding using the entropy of the histogram,” Comput. Vis. Graph. Image Process 29(3), 273–285 (1985).
    [Crossref]
  5. K. Hammouche, M. Diaf, and P. Siarry, “A multilevel automatic thresholding method based on a genetic algorithm for a fast image segmentation,” Computer Vision and Image Understanding 109(2), 163–175 (2008).
    [Crossref]
  6. J. Kennedy, “Particle swarm optimization,” in Encyclopedia of Machine Learning (Springer, 2011), pp. 760–766.
  7. R. Storn and K. Price, “Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces,” J. Global Optim. 11(4), 341–359 (1997).
    [Crossref]
  8. E. Cuevas, D. Zaldivar, and M. Pérez-Cisneros, “A novel multi-threshold segmentation approach based on differential evolution optimization,” Expert. Syst. Appl. 37(7), 5265–5271 (2010).
    [Crossref]
  9. Y. Qian Zhao, X. Hong Wang, X. Fang Wang, and F. Y. Shih, “Retinal vessels segmentation based on level set and region growing,” Pattern Recognition 47(7), 2437–2446 (2014).
    [Crossref]
  10. P. Lassalle, J. Inglada, J. Michel, M. Grizonnet, and J. Malik, “A Scalable Tile-Based Framework for Region-Merging Segmentation,” IEEE Trans. Geosci. Electron. 53(10), 5473–5485 (2015).
    [Crossref]
  11. S. Liu and Y. Peng, “A local region-based Chan–Vese model for image segmentation,” Pattern Recognition 45(7), 2769–2779 (2012).
    [Crossref]
  12. P. Arbelaez, M. Maire, C. Fowlkes, and J. Malik, “Contour detection and hierarchical image segmentation,” IEEE Trans. Pattern Anal. Mach. Intell. 33(5), 898–916 (2011).
    [Crossref]
  13. L.-C. Chen, J. T. Barron, G. Papandreou, K. Murphy, and A. L. Yuille, “Semantic image segmentation with task-specific edge detection using cnns and a discriminatively trained domain transform,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016), 4545–4554.
  14. A. F. M. G. Kibria and M. M. Islam, “Color image segmentation using visible color difference and canny edge detector,” in 2012 15th International Conference on Computer and Information Technology (ICCIT), 2012), 138–143.
  15. M. Gong, Y. Liang, J. Shi, W. Ma, and J. Ma, “Fuzzy C-means clustering with local information and kernel metric for image segmentation,” IEEE Trans. on Image Process. 22(2), 573–584 (2013).
    [Crossref]
  16. L. Wang and C. Pan, “Robust level set image segmentation via a local correntropy-based K-means clustering,” Pattern Recognition 47(5), 1917–1925 (2014).
    [Crossref]
  17. O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical image computing and computer-assisted intervention, (Springer, 2015), 234–241.
  18. L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, “Semantic image segmentation with deep convolutional nets and fully connected crfs,” arXiv preprint arXiv:1412.7062 (2014).
  19. H. Noh, S. Hong, and B. Han, “Learning Deconvolution Network for Semantic Segmentation,” in 2015 IEEE International Conference on Computer Vision (ICCV), 2015), 1520–1528.
  20. R. K. Thakur and M. V. Deshpande, “Kernel Optimized-Support Vector Machine and Mapreduce framework for sentiment classification of train reviews,” Sadhana 44(1), 6 (2019).
    [Crossref]
  21. S. M. Erfani, S. Rajasegarar, S. Karunasekera, and C. Leckie, “High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning,” Pattern Recognition 58, 121–134 (2016).
    [Crossref]
  22. H. Guo and W. Wang, “An active learning-based SVM multi-class classification model,” Pattern Recognition 48(5), 1577–1597 (2015).
    [Crossref]
  23. D. Zhang, L. Jiao, X. Bai, S. Wang, and B. Hou, “A robust semi-supervised SVM via ensemble learning,” Appl. Soft Comput. 65, 632–643 (2018).
    [Crossref]
  24. D. T. Kuan, A. A. Sawchuk, , T. C. Strand, and P. Chavel, “Adaptive Noise Smoothing Filter for Images with Signal-Dependent Noise,” IEEE Trans. Pattern Anal. Mach. Intell. PAMI-7(2), 165–177 (1985).
    [Crossref]
  25. S. Dong-Hyuk, P. Rae-Hong, Y. Seungjoon, and J. Jae-Han, “Block-based noise estimation using adaptive Gaussian filtering,” IEEE Trans. Broadcast Telev. Receivers 51(1), 218–226 (2005).
    [Crossref]
  26. J. Canny, “A Computational Approach to Edge Detection,” in Readings in Computer Vision, M. A. Fischler and O. Firschein, eds. (Morgan Kaufmann, San Francisco (CA), 1987), pp. 184–203.
  27. L. Yuan and X. Xu, “Adaptive Image Edge Detection Algorithm Based on Canny Operator,” in2015 4th International Conference on Advanced Information Technology and Sensor Application (AITS), (2015), pp. 28–31.
  28. F. Garcia-Lamont, J. Cervantes, A. López, and L. Rodriguez, “Segmentation of images by color features: A survey,” Neurocomputing 292, 1–27 (2018).
    [Crossref]
  29. M. A. Hearst, S. T. Dumais, E. Osuna, J. Platt, and B. Scholkopf, “Support vector machines,” IEEE Intell. Syst. Their Appl. 13(4), 18–28 (1998).
    [Crossref]

2019 (1)

R. K. Thakur and M. V. Deshpande, “Kernel Optimized-Support Vector Machine and Mapreduce framework for sentiment classification of train reviews,” Sadhana 44(1), 6 (2019).
[Crossref]

2018 (2)

D. Zhang, L. Jiao, X. Bai, S. Wang, and B. Hou, “A robust semi-supervised SVM via ensemble learning,” Appl. Soft Comput. 65, 632–643 (2018).
[Crossref]

F. Garcia-Lamont, J. Cervantes, A. López, and L. Rodriguez, “Segmentation of images by color features: A survey,” Neurocomputing 292, 1–27 (2018).
[Crossref]

2016 (1)

S. M. Erfani, S. Rajasegarar, S. Karunasekera, and C. Leckie, “High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning,” Pattern Recognition 58, 121–134 (2016).
[Crossref]

2015 (2)

H. Guo and W. Wang, “An active learning-based SVM multi-class classification model,” Pattern Recognition 48(5), 1577–1597 (2015).
[Crossref]

P. Lassalle, J. Inglada, J. Michel, M. Grizonnet, and J. Malik, “A Scalable Tile-Based Framework for Region-Merging Segmentation,” IEEE Trans. Geosci. Electron. 53(10), 5473–5485 (2015).
[Crossref]

2014 (2)

Y. Qian Zhao, X. Hong Wang, X. Fang Wang, and F. Y. Shih, “Retinal vessels segmentation based on level set and region growing,” Pattern Recognition 47(7), 2437–2446 (2014).
[Crossref]

L. Wang and C. Pan, “Robust level set image segmentation via a local correntropy-based K-means clustering,” Pattern Recognition 47(5), 1917–1925 (2014).
[Crossref]

2013 (1)

M. Gong, Y. Liang, J. Shi, W. Ma, and J. Ma, “Fuzzy C-means clustering with local information and kernel metric for image segmentation,” IEEE Trans. on Image Process. 22(2), 573–584 (2013).
[Crossref]

2012 (1)

S. Liu and Y. Peng, “A local region-based Chan–Vese model for image segmentation,” Pattern Recognition 45(7), 2769–2779 (2012).
[Crossref]

2011 (1)

P. Arbelaez, M. Maire, C. Fowlkes, and J. Malik, “Contour detection and hierarchical image segmentation,” IEEE Trans. Pattern Anal. Mach. Intell. 33(5), 898–916 (2011).
[Crossref]

2010 (1)

E. Cuevas, D. Zaldivar, and M. Pérez-Cisneros, “A novel multi-threshold segmentation approach based on differential evolution optimization,” Expert. Syst. Appl. 37(7), 5265–5271 (2010).
[Crossref]

2008 (1)

K. Hammouche, M. Diaf, and P. Siarry, “A multilevel automatic thresholding method based on a genetic algorithm for a fast image segmentation,” Computer Vision and Image Understanding 109(2), 163–175 (2008).
[Crossref]

2007 (1)

H. Stokman and T. Gevers, “Selection and fusion of color models for image feature detection,” IEEE Trans. Pattern Anal. Mach. Intell. 29(3), 371–381 (2007).
[Crossref]

2005 (1)

S. Dong-Hyuk, P. Rae-Hong, Y. Seungjoon, and J. Jae-Han, “Block-based noise estimation using adaptive Gaussian filtering,” IEEE Trans. Broadcast Telev. Receivers 51(1), 218–226 (2005).
[Crossref]

1998 (1)

M. A. Hearst, S. T. Dumais, E. Osuna, J. Platt, and B. Scholkopf, “Support vector machines,” IEEE Intell. Syst. Their Appl. 13(4), 18–28 (1998).
[Crossref]

1997 (1)

R. Storn and K. Price, “Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces,” J. Global Optim. 11(4), 341–359 (1997).
[Crossref]

1993 (1)

N. R. Pal and S. K. Pal, “A review on image segmentation techniques,” Pattern Recognition 26(9), 1277–1294 (1993).
[Crossref]

1985 (2)

J. N. Kapur, P. K. Sahoo, and A. K. C. Wong, “A new method for gray-level picture thresholding using the entropy of the histogram,” Comput. Vis. Graph. Image Process 29(3), 273–285 (1985).
[Crossref]

D. T. Kuan, A. A. Sawchuk, , T. C. Strand, and P. Chavel, “Adaptive Noise Smoothing Filter for Images with Signal-Dependent Noise,” IEEE Trans. Pattern Anal. Mach. Intell. PAMI-7(2), 165–177 (1985).
[Crossref]

1979 (1)

N. Otsu, “A Threshold Selection Method from Gray-Level Histograms,” IEEE Trans. Syst., Man, Cybern. 9(1), 62–66 (1979).
[Crossref]

Arbelaez, P.

P. Arbelaez, M. Maire, C. Fowlkes, and J. Malik, “Contour detection and hierarchical image segmentation,” IEEE Trans. Pattern Anal. Mach. Intell. 33(5), 898–916 (2011).
[Crossref]

Bai, X.

D. Zhang, L. Jiao, X. Bai, S. Wang, and B. Hou, “A robust semi-supervised SVM via ensemble learning,” Appl. Soft Comput. 65, 632–643 (2018).
[Crossref]

Barron, J. T.

L.-C. Chen, J. T. Barron, G. Papandreou, K. Murphy, and A. L. Yuille, “Semantic image segmentation with task-specific edge detection using cnns and a discriminatively trained domain transform,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016), 4545–4554.

Brox, T.

O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical image computing and computer-assisted intervention, (Springer, 2015), 234–241.

Canny, J.

J. Canny, “A Computational Approach to Edge Detection,” in Readings in Computer Vision, M. A. Fischler and O. Firschein, eds. (Morgan Kaufmann, San Francisco (CA), 1987), pp. 184–203.

Cervantes, J.

F. Garcia-Lamont, J. Cervantes, A. López, and L. Rodriguez, “Segmentation of images by color features: A survey,” Neurocomputing 292, 1–27 (2018).
[Crossref]

Chavel, P.

D. T. Kuan, A. A. Sawchuk, , T. C. Strand, and P. Chavel, “Adaptive Noise Smoothing Filter for Images with Signal-Dependent Noise,” IEEE Trans. Pattern Anal. Mach. Intell. PAMI-7(2), 165–177 (1985).
[Crossref]

Chen, L.-C.

L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, “Semantic image segmentation with deep convolutional nets and fully connected crfs,” arXiv preprint arXiv:1412.7062 (2014).

L.-C. Chen, J. T. Barron, G. Papandreou, K. Murphy, and A. L. Yuille, “Semantic image segmentation with task-specific edge detection using cnns and a discriminatively trained domain transform,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016), 4545–4554.

Cuevas, E.

E. Cuevas, D. Zaldivar, and M. Pérez-Cisneros, “A novel multi-threshold segmentation approach based on differential evolution optimization,” Expert. Syst. Appl. 37(7), 5265–5271 (2010).
[Crossref]

Deshpande, M. V.

R. K. Thakur and M. V. Deshpande, “Kernel Optimized-Support Vector Machine and Mapreduce framework for sentiment classification of train reviews,” Sadhana 44(1), 6 (2019).
[Crossref]

Diaf, M.

K. Hammouche, M. Diaf, and P. Siarry, “A multilevel automatic thresholding method based on a genetic algorithm for a fast image segmentation,” Computer Vision and Image Understanding 109(2), 163–175 (2008).
[Crossref]

Dong-Hyuk, S.

S. Dong-Hyuk, P. Rae-Hong, Y. Seungjoon, and J. Jae-Han, “Block-based noise estimation using adaptive Gaussian filtering,” IEEE Trans. Broadcast Telev. Receivers 51(1), 218–226 (2005).
[Crossref]

Dumais, S. T.

M. A. Hearst, S. T. Dumais, E. Osuna, J. Platt, and B. Scholkopf, “Support vector machines,” IEEE Intell. Syst. Their Appl. 13(4), 18–28 (1998).
[Crossref]

Erfani, S. M.

S. M. Erfani, S. Rajasegarar, S. Karunasekera, and C. Leckie, “High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning,” Pattern Recognition 58, 121–134 (2016).
[Crossref]

Fang Wang, X.

Y. Qian Zhao, X. Hong Wang, X. Fang Wang, and F. Y. Shih, “Retinal vessels segmentation based on level set and region growing,” Pattern Recognition 47(7), 2437–2446 (2014).
[Crossref]

Fischer, P.

O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical image computing and computer-assisted intervention, (Springer, 2015), 234–241.

Fowlkes, C.

P. Arbelaez, M. Maire, C. Fowlkes, and J. Malik, “Contour detection and hierarchical image segmentation,” IEEE Trans. Pattern Anal. Mach. Intell. 33(5), 898–916 (2011).
[Crossref]

Garcia-Lamont, F.

F. Garcia-Lamont, J. Cervantes, A. López, and L. Rodriguez, “Segmentation of images by color features: A survey,” Neurocomputing 292, 1–27 (2018).
[Crossref]

Gevers, T.

H. Stokman and T. Gevers, “Selection and fusion of color models for image feature detection,” IEEE Trans. Pattern Anal. Mach. Intell. 29(3), 371–381 (2007).
[Crossref]

Gong, M.

M. Gong, Y. Liang, J. Shi, W. Ma, and J. Ma, “Fuzzy C-means clustering with local information and kernel metric for image segmentation,” IEEE Trans. on Image Process. 22(2), 573–584 (2013).
[Crossref]

Grizonnet, M.

P. Lassalle, J. Inglada, J. Michel, M. Grizonnet, and J. Malik, “A Scalable Tile-Based Framework for Region-Merging Segmentation,” IEEE Trans. Geosci. Electron. 53(10), 5473–5485 (2015).
[Crossref]

Guo, H.

H. Guo and W. Wang, “An active learning-based SVM multi-class classification model,” Pattern Recognition 48(5), 1577–1597 (2015).
[Crossref]

Hammouche, K.

K. Hammouche, M. Diaf, and P. Siarry, “A multilevel automatic thresholding method based on a genetic algorithm for a fast image segmentation,” Computer Vision and Image Understanding 109(2), 163–175 (2008).
[Crossref]

Han, B.

H. Noh, S. Hong, and B. Han, “Learning Deconvolution Network for Semantic Segmentation,” in 2015 IEEE International Conference on Computer Vision (ICCV), 2015), 1520–1528.

Hearst, M. A.

M. A. Hearst, S. T. Dumais, E. Osuna, J. Platt, and B. Scholkopf, “Support vector machines,” IEEE Intell. Syst. Their Appl. 13(4), 18–28 (1998).
[Crossref]

Hong, S.

H. Noh, S. Hong, and B. Han, “Learning Deconvolution Network for Semantic Segmentation,” in 2015 IEEE International Conference on Computer Vision (ICCV), 2015), 1520–1528.

Hong Wang, X.

Y. Qian Zhao, X. Hong Wang, X. Fang Wang, and F. Y. Shih, “Retinal vessels segmentation based on level set and region growing,” Pattern Recognition 47(7), 2437–2446 (2014).
[Crossref]

Hou, B.

D. Zhang, L. Jiao, X. Bai, S. Wang, and B. Hou, “A robust semi-supervised SVM via ensemble learning,” Appl. Soft Comput. 65, 632–643 (2018).
[Crossref]

Inglada, J.

P. Lassalle, J. Inglada, J. Michel, M. Grizonnet, and J. Malik, “A Scalable Tile-Based Framework for Region-Merging Segmentation,” IEEE Trans. Geosci. Electron. 53(10), 5473–5485 (2015).
[Crossref]

Islam, M. M.

A. F. M. G. Kibria and M. M. Islam, “Color image segmentation using visible color difference and canny edge detector,” in 2012 15th International Conference on Computer and Information Technology (ICCIT), 2012), 138–143.

Jae-Han, J.

S. Dong-Hyuk, P. Rae-Hong, Y. Seungjoon, and J. Jae-Han, “Block-based noise estimation using adaptive Gaussian filtering,” IEEE Trans. Broadcast Telev. Receivers 51(1), 218–226 (2005).
[Crossref]

Jiao, L.

D. Zhang, L. Jiao, X. Bai, S. Wang, and B. Hou, “A robust semi-supervised SVM via ensemble learning,” Appl. Soft Comput. 65, 632–643 (2018).
[Crossref]

Kapur, J. N.

J. N. Kapur, P. K. Sahoo, and A. K. C. Wong, “A new method for gray-level picture thresholding using the entropy of the histogram,” Comput. Vis. Graph. Image Process 29(3), 273–285 (1985).
[Crossref]

Karunasekera, S.

S. M. Erfani, S. Rajasegarar, S. Karunasekera, and C. Leckie, “High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning,” Pattern Recognition 58, 121–134 (2016).
[Crossref]

Kennedy, J.

J. Kennedy, “Particle swarm optimization,” in Encyclopedia of Machine Learning (Springer, 2011), pp. 760–766.

Kibria, A. F. M. G.

A. F. M. G. Kibria and M. M. Islam, “Color image segmentation using visible color difference and canny edge detector,” in 2012 15th International Conference on Computer and Information Technology (ICCIT), 2012), 138–143.

Kokkinos, I.

L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, “Semantic image segmentation with deep convolutional nets and fully connected crfs,” arXiv preprint arXiv:1412.7062 (2014).

Kuan, D. T.

D. T. Kuan, A. A. Sawchuk, , T. C. Strand, and P. Chavel, “Adaptive Noise Smoothing Filter for Images with Signal-Dependent Noise,” IEEE Trans. Pattern Anal. Mach. Intell. PAMI-7(2), 165–177 (1985).
[Crossref]

Lassalle, P.

P. Lassalle, J. Inglada, J. Michel, M. Grizonnet, and J. Malik, “A Scalable Tile-Based Framework for Region-Merging Segmentation,” IEEE Trans. Geosci. Electron. 53(10), 5473–5485 (2015).
[Crossref]

Leckie, C.

S. M. Erfani, S. Rajasegarar, S. Karunasekera, and C. Leckie, “High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning,” Pattern Recognition 58, 121–134 (2016).
[Crossref]

Liang, Y.

M. Gong, Y. Liang, J. Shi, W. Ma, and J. Ma, “Fuzzy C-means clustering with local information and kernel metric for image segmentation,” IEEE Trans. on Image Process. 22(2), 573–584 (2013).
[Crossref]

Liu, S.

S. Liu and Y. Peng, “A local region-based Chan–Vese model for image segmentation,” Pattern Recognition 45(7), 2769–2779 (2012).
[Crossref]

López, A.

F. Garcia-Lamont, J. Cervantes, A. López, and L. Rodriguez, “Segmentation of images by color features: A survey,” Neurocomputing 292, 1–27 (2018).
[Crossref]

Ma, J.

M. Gong, Y. Liang, J. Shi, W. Ma, and J. Ma, “Fuzzy C-means clustering with local information and kernel metric for image segmentation,” IEEE Trans. on Image Process. 22(2), 573–584 (2013).
[Crossref]

Ma, W.

M. Gong, Y. Liang, J. Shi, W. Ma, and J. Ma, “Fuzzy C-means clustering with local information and kernel metric for image segmentation,” IEEE Trans. on Image Process. 22(2), 573–584 (2013).
[Crossref]

Maire, M.

P. Arbelaez, M. Maire, C. Fowlkes, and J. Malik, “Contour detection and hierarchical image segmentation,” IEEE Trans. Pattern Anal. Mach. Intell. 33(5), 898–916 (2011).
[Crossref]

Malik, J.

P. Lassalle, J. Inglada, J. Michel, M. Grizonnet, and J. Malik, “A Scalable Tile-Based Framework for Region-Merging Segmentation,” IEEE Trans. Geosci. Electron. 53(10), 5473–5485 (2015).
[Crossref]

P. Arbelaez, M. Maire, C. Fowlkes, and J. Malik, “Contour detection and hierarchical image segmentation,” IEEE Trans. Pattern Anal. Mach. Intell. 33(5), 898–916 (2011).
[Crossref]

Michel, J.

P. Lassalle, J. Inglada, J. Michel, M. Grizonnet, and J. Malik, “A Scalable Tile-Based Framework for Region-Merging Segmentation,” IEEE Trans. Geosci. Electron. 53(10), 5473–5485 (2015).
[Crossref]

Murphy, K.

L.-C. Chen, J. T. Barron, G. Papandreou, K. Murphy, and A. L. Yuille, “Semantic image segmentation with task-specific edge detection using cnns and a discriminatively trained domain transform,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016), 4545–4554.

L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, “Semantic image segmentation with deep convolutional nets and fully connected crfs,” arXiv preprint arXiv:1412.7062 (2014).

Noh, H.

H. Noh, S. Hong, and B. Han, “Learning Deconvolution Network for Semantic Segmentation,” in 2015 IEEE International Conference on Computer Vision (ICCV), 2015), 1520–1528.

Osuna, E.

M. A. Hearst, S. T. Dumais, E. Osuna, J. Platt, and B. Scholkopf, “Support vector machines,” IEEE Intell. Syst. Their Appl. 13(4), 18–28 (1998).
[Crossref]

Otsu, N.

N. Otsu, “A Threshold Selection Method from Gray-Level Histograms,” IEEE Trans. Syst., Man, Cybern. 9(1), 62–66 (1979).
[Crossref]

Pal, N. R.

N. R. Pal and S. K. Pal, “A review on image segmentation techniques,” Pattern Recognition 26(9), 1277–1294 (1993).
[Crossref]

Pal, S. K.

N. R. Pal and S. K. Pal, “A review on image segmentation techniques,” Pattern Recognition 26(9), 1277–1294 (1993).
[Crossref]

Pan, C.

L. Wang and C. Pan, “Robust level set image segmentation via a local correntropy-based K-means clustering,” Pattern Recognition 47(5), 1917–1925 (2014).
[Crossref]

Papandreou, G.

L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, “Semantic image segmentation with deep convolutional nets and fully connected crfs,” arXiv preprint arXiv:1412.7062 (2014).

L.-C. Chen, J. T. Barron, G. Papandreou, K. Murphy, and A. L. Yuille, “Semantic image segmentation with task-specific edge detection using cnns and a discriminatively trained domain transform,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016), 4545–4554.

Peng, Y.

S. Liu and Y. Peng, “A local region-based Chan–Vese model for image segmentation,” Pattern Recognition 45(7), 2769–2779 (2012).
[Crossref]

Pérez-Cisneros, M.

E. Cuevas, D. Zaldivar, and M. Pérez-Cisneros, “A novel multi-threshold segmentation approach based on differential evolution optimization,” Expert. Syst. Appl. 37(7), 5265–5271 (2010).
[Crossref]

Platt, J.

M. A. Hearst, S. T. Dumais, E. Osuna, J. Platt, and B. Scholkopf, “Support vector machines,” IEEE Intell. Syst. Their Appl. 13(4), 18–28 (1998).
[Crossref]

Price, K.

R. Storn and K. Price, “Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces,” J. Global Optim. 11(4), 341–359 (1997).
[Crossref]

Qian Zhao, Y.

Y. Qian Zhao, X. Hong Wang, X. Fang Wang, and F. Y. Shih, “Retinal vessels segmentation based on level set and region growing,” Pattern Recognition 47(7), 2437–2446 (2014).
[Crossref]

Rae-Hong, P.

S. Dong-Hyuk, P. Rae-Hong, Y. Seungjoon, and J. Jae-Han, “Block-based noise estimation using adaptive Gaussian filtering,” IEEE Trans. Broadcast Telev. Receivers 51(1), 218–226 (2005).
[Crossref]

Rajasegarar, S.

S. M. Erfani, S. Rajasegarar, S. Karunasekera, and C. Leckie, “High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning,” Pattern Recognition 58, 121–134 (2016).
[Crossref]

Rodriguez, L.

F. Garcia-Lamont, J. Cervantes, A. López, and L. Rodriguez, “Segmentation of images by color features: A survey,” Neurocomputing 292, 1–27 (2018).
[Crossref]

Ronneberger, O.

O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical image computing and computer-assisted intervention, (Springer, 2015), 234–241.

Sahoo, P. K.

J. N. Kapur, P. K. Sahoo, and A. K. C. Wong, “A new method for gray-level picture thresholding using the entropy of the histogram,” Comput. Vis. Graph. Image Process 29(3), 273–285 (1985).
[Crossref]

Sawchuk, A. A.

D. T. Kuan, A. A. Sawchuk, , T. C. Strand, and P. Chavel, “Adaptive Noise Smoothing Filter for Images with Signal-Dependent Noise,” IEEE Trans. Pattern Anal. Mach. Intell. PAMI-7(2), 165–177 (1985).
[Crossref]

Scholkopf, B.

M. A. Hearst, S. T. Dumais, E. Osuna, J. Platt, and B. Scholkopf, “Support vector machines,” IEEE Intell. Syst. Their Appl. 13(4), 18–28 (1998).
[Crossref]

Seungjoon, Y.

S. Dong-Hyuk, P. Rae-Hong, Y. Seungjoon, and J. Jae-Han, “Block-based noise estimation using adaptive Gaussian filtering,” IEEE Trans. Broadcast Telev. Receivers 51(1), 218–226 (2005).
[Crossref]

Shi, J.

M. Gong, Y. Liang, J. Shi, W. Ma, and J. Ma, “Fuzzy C-means clustering with local information and kernel metric for image segmentation,” IEEE Trans. on Image Process. 22(2), 573–584 (2013).
[Crossref]

Shih, F. Y.

Y. Qian Zhao, X. Hong Wang, X. Fang Wang, and F. Y. Shih, “Retinal vessels segmentation based on level set and region growing,” Pattern Recognition 47(7), 2437–2446 (2014).
[Crossref]

Siarry, P.

K. Hammouche, M. Diaf, and P. Siarry, “A multilevel automatic thresholding method based on a genetic algorithm for a fast image segmentation,” Computer Vision and Image Understanding 109(2), 163–175 (2008).
[Crossref]

Stokman, H.

H. Stokman and T. Gevers, “Selection and fusion of color models for image feature detection,” IEEE Trans. Pattern Anal. Mach. Intell. 29(3), 371–381 (2007).
[Crossref]

Storn, R.

R. Storn and K. Price, “Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces,” J. Global Optim. 11(4), 341–359 (1997).
[Crossref]

Strand, T. C.

D. T. Kuan, A. A. Sawchuk, , T. C. Strand, and P. Chavel, “Adaptive Noise Smoothing Filter for Images with Signal-Dependent Noise,” IEEE Trans. Pattern Anal. Mach. Intell. PAMI-7(2), 165–177 (1985).
[Crossref]

Thakur, R. K.

R. K. Thakur and M. V. Deshpande, “Kernel Optimized-Support Vector Machine and Mapreduce framework for sentiment classification of train reviews,” Sadhana 44(1), 6 (2019).
[Crossref]

Wang, L.

L. Wang and C. Pan, “Robust level set image segmentation via a local correntropy-based K-means clustering,” Pattern Recognition 47(5), 1917–1925 (2014).
[Crossref]

Wang, S.

D. Zhang, L. Jiao, X. Bai, S. Wang, and B. Hou, “A robust semi-supervised SVM via ensemble learning,” Appl. Soft Comput. 65, 632–643 (2018).
[Crossref]

Wang, W.

H. Guo and W. Wang, “An active learning-based SVM multi-class classification model,” Pattern Recognition 48(5), 1577–1597 (2015).
[Crossref]

Wong, A. K. C.

J. N. Kapur, P. K. Sahoo, and A. K. C. Wong, “A new method for gray-level picture thresholding using the entropy of the histogram,” Comput. Vis. Graph. Image Process 29(3), 273–285 (1985).
[Crossref]

Xu, X.

L. Yuan and X. Xu, “Adaptive Image Edge Detection Algorithm Based on Canny Operator,” in2015 4th International Conference on Advanced Information Technology and Sensor Application (AITS), (2015), pp. 28–31.

Yuan, L.

L. Yuan and X. Xu, “Adaptive Image Edge Detection Algorithm Based on Canny Operator,” in2015 4th International Conference on Advanced Information Technology and Sensor Application (AITS), (2015), pp. 28–31.

Yuille, A. L.

L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, “Semantic image segmentation with deep convolutional nets and fully connected crfs,” arXiv preprint arXiv:1412.7062 (2014).

L.-C. Chen, J. T. Barron, G. Papandreou, K. Murphy, and A. L. Yuille, “Semantic image segmentation with task-specific edge detection using cnns and a discriminatively trained domain transform,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016), 4545–4554.

Zaldivar, D.

E. Cuevas, D. Zaldivar, and M. Pérez-Cisneros, “A novel multi-threshold segmentation approach based on differential evolution optimization,” Expert. Syst. Appl. 37(7), 5265–5271 (2010).
[Crossref]

Zhang, D.

D. Zhang, L. Jiao, X. Bai, S. Wang, and B. Hou, “A robust semi-supervised SVM via ensemble learning,” Appl. Soft Comput. 65, 632–643 (2018).
[Crossref]

Appl. Soft Comput. (1)

D. Zhang, L. Jiao, X. Bai, S. Wang, and B. Hou, “A robust semi-supervised SVM via ensemble learning,” Appl. Soft Comput. 65, 632–643 (2018).
[Crossref]

Comput. Vis. Graph. Image Process (1)

J. N. Kapur, P. K. Sahoo, and A. K. C. Wong, “A new method for gray-level picture thresholding using the entropy of the histogram,” Comput. Vis. Graph. Image Process 29(3), 273–285 (1985).
[Crossref]

Computer Vision and Image Understanding (1)

K. Hammouche, M. Diaf, and P. Siarry, “A multilevel automatic thresholding method based on a genetic algorithm for a fast image segmentation,” Computer Vision and Image Understanding 109(2), 163–175 (2008).
[Crossref]

Expert. Syst. Appl. (1)

E. Cuevas, D. Zaldivar, and M. Pérez-Cisneros, “A novel multi-threshold segmentation approach based on differential evolution optimization,” Expert. Syst. Appl. 37(7), 5265–5271 (2010).
[Crossref]

IEEE Intell. Syst. Their Appl. (1)

M. A. Hearst, S. T. Dumais, E. Osuna, J. Platt, and B. Scholkopf, “Support vector machines,” IEEE Intell. Syst. Their Appl. 13(4), 18–28 (1998).
[Crossref]

IEEE Trans. Broadcast Telev. Receivers (1)

S. Dong-Hyuk, P. Rae-Hong, Y. Seungjoon, and J. Jae-Han, “Block-based noise estimation using adaptive Gaussian filtering,” IEEE Trans. Broadcast Telev. Receivers 51(1), 218–226 (2005).
[Crossref]

IEEE Trans. Geosci. Electron. (1)

P. Lassalle, J. Inglada, J. Michel, M. Grizonnet, and J. Malik, “A Scalable Tile-Based Framework for Region-Merging Segmentation,” IEEE Trans. Geosci. Electron. 53(10), 5473–5485 (2015).
[Crossref]

IEEE Trans. on Image Process. (1)

M. Gong, Y. Liang, J. Shi, W. Ma, and J. Ma, “Fuzzy C-means clustering with local information and kernel metric for image segmentation,” IEEE Trans. on Image Process. 22(2), 573–584 (2013).
[Crossref]

IEEE Trans. Pattern Anal. Mach. Intell. (3)

P. Arbelaez, M. Maire, C. Fowlkes, and J. Malik, “Contour detection and hierarchical image segmentation,” IEEE Trans. Pattern Anal. Mach. Intell. 33(5), 898–916 (2011).
[Crossref]

H. Stokman and T. Gevers, “Selection and fusion of color models for image feature detection,” IEEE Trans. Pattern Anal. Mach. Intell. 29(3), 371–381 (2007).
[Crossref]

D. T. Kuan, A. A. Sawchuk, , T. C. Strand, and P. Chavel, “Adaptive Noise Smoothing Filter for Images with Signal-Dependent Noise,” IEEE Trans. Pattern Anal. Mach. Intell. PAMI-7(2), 165–177 (1985).
[Crossref]

IEEE Trans. Syst., Man, Cybern. (1)

N. Otsu, “A Threshold Selection Method from Gray-Level Histograms,” IEEE Trans. Syst., Man, Cybern. 9(1), 62–66 (1979).
[Crossref]

J. Global Optim. (1)

R. Storn and K. Price, “Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces,” J. Global Optim. 11(4), 341–359 (1997).
[Crossref]

Neurocomputing (1)

F. Garcia-Lamont, J. Cervantes, A. López, and L. Rodriguez, “Segmentation of images by color features: A survey,” Neurocomputing 292, 1–27 (2018).
[Crossref]

Pattern Recognition (6)

S. M. Erfani, S. Rajasegarar, S. Karunasekera, and C. Leckie, “High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning,” Pattern Recognition 58, 121–134 (2016).
[Crossref]

H. Guo and W. Wang, “An active learning-based SVM multi-class classification model,” Pattern Recognition 48(5), 1577–1597 (2015).
[Crossref]

Y. Qian Zhao, X. Hong Wang, X. Fang Wang, and F. Y. Shih, “Retinal vessels segmentation based on level set and region growing,” Pattern Recognition 47(7), 2437–2446 (2014).
[Crossref]

N. R. Pal and S. K. Pal, “A review on image segmentation techniques,” Pattern Recognition 26(9), 1277–1294 (1993).
[Crossref]

S. Liu and Y. Peng, “A local region-based Chan–Vese model for image segmentation,” Pattern Recognition 45(7), 2769–2779 (2012).
[Crossref]

L. Wang and C. Pan, “Robust level set image segmentation via a local correntropy-based K-means clustering,” Pattern Recognition 47(5), 1917–1925 (2014).
[Crossref]

Sadhana (1)

R. K. Thakur and M. V. Deshpande, “Kernel Optimized-Support Vector Machine and Mapreduce framework for sentiment classification of train reviews,” Sadhana 44(1), 6 (2019).
[Crossref]

Other (8)

J. Canny, “A Computational Approach to Edge Detection,” in Readings in Computer Vision, M. A. Fischler and O. Firschein, eds. (Morgan Kaufmann, San Francisco (CA), 1987), pp. 184–203.

L. Yuan and X. Xu, “Adaptive Image Edge Detection Algorithm Based on Canny Operator,” in2015 4th International Conference on Advanced Information Technology and Sensor Application (AITS), (2015), pp. 28–31.

O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical image computing and computer-assisted intervention, (Springer, 2015), 234–241.

L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, “Semantic image segmentation with deep convolutional nets and fully connected crfs,” arXiv preprint arXiv:1412.7062 (2014).

H. Noh, S. Hong, and B. Han, “Learning Deconvolution Network for Semantic Segmentation,” in 2015 IEEE International Conference on Computer Vision (ICCV), 2015), 1520–1528.

L.-C. Chen, J. T. Barron, G. Papandreou, K. Murphy, and A. L. Yuille, “Semantic image segmentation with task-specific edge detection using cnns and a discriminatively trained domain transform,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016), 4545–4554.

A. F. M. G. Kibria and M. M. Islam, “Color image segmentation using visible color difference and canny edge detector,” in 2012 15th International Conference on Computer and Information Technology (ICCIT), 2012), 138–143.

J. Kennedy, “Particle swarm optimization,” in Encyclopedia of Machine Learning (Springer, 2011), pp. 760–766.

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Figures (10)

Fig. 1.
Fig. 1. Image segmentation using edge and color features
Fig. 2.
Fig. 2. Comparison of different Gaussian filtering methods. (a) original image. (b) Gaussian filtering image in window $5 \times 5$, $\sigma = 1$ . (c) gray histogram of image b. (d) adaptive Gaussian filtering image. (e) gray histogram of image d.
Fig. 3.
Fig. 3. Adaptive Canny edge detection result. (a) Original image. (b) Hue image. (c) Edge gray histogram. (d) Edge extraction image. (e) Edge detection result.
Fig. 4.
Fig. 4. Edge detection result of adaptive Canny operator
Fig. 5.
Fig. 5. Erroneous recognition result of single object
Fig. 6.
Fig. 6. Erroneous recognition result of multiple objects
Fig. 7.
Fig. 7. Segmentation result in scenario 1
Fig. 8.
Fig. 8. Segmentation result in scenario 2
Fig. 9.
Fig. 9. Region Roundness of the segmentation results
Fig. 10.
Fig. 10. Boundary Centre Deviation of the segmentation results

Tables (4)

Tables Icon

Table 1. Recognition accuracy of two training models in different color spaces

Tables Icon

Table 2. Comparison of the performance of different methods in scenario 1

Tables Icon

Table 3. Comparison of the performance of different methods in scenario 2

Tables Icon

Table 4. Stability parameters of the segmentation methods

Equations (15)

Equations on this page are rendered with MathJax. Learn more.

G ( x , y , σ ) = 1 2 π σ 2 e ( x 2 + y 2 ) 2 σ 2
f s ( x , y ) = G ( x , y , σ ) f ( x , y )
f x ( x , y ) = M x = f ( x + 1 , y ) f ( x , y ) + f ( x + 1 , y + 1 ) f ( x , y + 1 ) 2
f y ( x , y ) = M y = f ( x , y + 1 ) f ( x , y ) + f ( x + 1 , y + 1 ) f ( x + 1 , y ) 2
M ( x , y ) = M x ( x , y ) 2 + M y ( x , y ) 2
θ ( x , y ) = arctan ( M x ( x , y ) M y ( x , y ) )
w ( x , y ) = ϕ [ M ( x , y ) ]
w ( x , y ) = e M ( x , y ) 2 2 m 2
m i , j k = i , j R p i , j k N
d i , j k = i , j R ( p i , j k m i , j k ) 2 N
e i , j k = i = 0 L 1 p ( z i ) log 2 p ( z i )
F i , j = [ m i , j k , d i , j k , e i , j k ]
K ( x , z ) = exp ( | | x z | | 2 2 σ 2 )
R R = 4 π S r P r 2
B C D = i = 1 N ( D i D i ¯ ) 2 N

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