A supervised figure-ground segmentation method using genetic programming

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 9028, Issue , 2015, Pages 491-503

  Liang, Yuyu ^a   Zhang, Mengjie ^a   Browne, Will N ^a  

^a VICTORIA UNIVERSITY OF WELLINGTON   (New Zealand)

Author keywords

Genetic programming; Grayscale statistics; Image segmentation; Raw pixel Values

Indexed keywords

COMPUTER PROGRAMMING; COMPUTER VISION; GENETIC ALGORITHMS; IMAGE SEGMENTATION; PIXELS;

ACTIVE CONTOUR MODEL; COMPUTER VISION APPLICATIONS; CONVENTIONAL TECHNIQUES; FIGURE-GROUND SEGMENTATIONS; GRAY SCALE; PIXEL VALUES; PREPROCESSING PHASE; SEGMENTATION SYSTEM;

GENETIC PROGRAMMING;

EID: 84925849643     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-16549-3_40     Document Type: Conference Paper

References (24)

1. Zou, W., Bai, C., Kpalma, K., Ronsin, J.: Online glocal transfer for automatic figure-ground segmentation. IEEE Trans. Image Process. 23(5), 2109–2121 (2014)
2. Borenstein, E., Ullman, S.: Learning to segment. In: Pajdla, T., Matas, J.G. (eds.) ECCV 2004. LNCS, vol. 3023, pp. 315–328. Springer, Heidelberg (2004)
3. Borenstein, E., Ullman, S.: Class-specific, top-down segmentation. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002, Part II. LNCS, vol. 2351, pp. 109–122. Springer, Heidelberg (2002)
4. Liu, J., Wang, J.: Application of snake model in medical image segmentation. J. Convergence Inf. Technol. 9(1), 105–109 (2014)
5. Liu, C.Y., Iglesias, J.E., Tu, Z.: Deformable templates guided discriminative models for robust 3D brain MRI segmentation. Neuroinformatics 11(4), 447–468 (2013)
6. Borenstein, E., Sharon, E., Ullman, S.: Combining top-down and bottom-up segmentation. In: Proceedings IEEE workshop on Perceptual Organization in Computer Vision, CVPR 2004 (2004)
7. Poli, R., Langdon, W.B., McPhee, N.F.: A Field Guide to Genetic Programming (2008). http://lulu.com
8. Poli, R.: Genetic Programming for feature detection and image segmentation. In: Fogarty, T.C. (ed.) AISB-WS 1996. LNCS, vol. 1143, pp. 110–125. Springer, Heidelberg (1996)
9. Song, A., Ciesielski, V.: Fast texture segmentation using genetic programming. In: The 2003 Congress on Evolutionary Computation, pp. 2126–2133. IEEE (2003)
10. Song, A., Ciesielski, V.: Texture segmentation by genetic programming. Evol. Comput. 16(4), 461–481 (2008)
11. Singh, T., Nawwaf, K., Mohmmad, D., Rabab, W.: Genetic programming based image segmentation with applications to biomedical object detection. In: Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation, pp. 1123–1130. ACM (2009)
12. Roberts, M.E.: The effectiveness of cost based subtree caching mechanisms in typed genetic programming for image segmentation. In: Raidl, G.R., Cagnoni, S., Cardalda, J.J.R., Corne, D.W., Gottlieb, J., Guillot, A., Hart, E., Johnson, C.G., Marchiori, E., Meyer, J.-A., Middendorf, M. (eds.) EvoIASP 2003, EvoWorkshops 2003, EvoSTIM 2003, EvoROB/EvoRobot 2003, EvoCOP 2003, EvoBIO 2003, and EvoMUSART 2003. LNCS, vol. 2611, pp. 444–454. Springer, Heidelberg (2003)
13. Geng, J., Liu, J.: Image texture classification using a multiagent genetic clustering algorithm. In: Evolutionary Computation (CEC), pp. 504–508 (2011)
14. Luke, S.: The ECJ Owner’s Manual (2014)
15. Picard, R.W., Kabir, T., Liu, F.: Real-time recognition with the entire Brodatz texture database. In: IEEE Conference on CVPR, pp. 638–639 (1993)
16. Weizmann Horses. http://avaminzhang.wordpress.com/2012/12/07/
17. The PASCAL Visual Object Classes Homepage. http://pascallin.ecs.soton.ac.uk/challenges/VOC/
18. Powers, D.M.: Evaluation: from precision, recall and F-factor to ROC, informedness, markedness correlation. Technical report SIE-07-001, School of Informatics and Engineering, Flinders University, Australia (2007)
19. Ashburner, J., Friston, K.J.: Unified segmentation. Neuroimage 26(3), 839–851 (2005)
20. Validation. http://research.cs.tamu.edu/prism/lectures/iss/issl13.pdf
21. Thresholding Segmentation. http://au.mathworks.com/help/images/examples/correcting-nonuniform-illumination.html
22. Active Contour Based Segmentation. http://au.mathworks.com/help/images/ref/activecontour.htmlbtuep4x-7
23. Region Growing. http://www.mathworks.com/matlabcentral/fileexchange/19084-region-growing
24. K-means Image Segmentation. http://www.mathworks.com/matlabcentral/fileexchange/authors/129300