Genetic Algorithms: Colour Image Segmentation Literature Review

Image segmentation is a crucial problem in image processing and can determine the final outcome of many image processing tasks. Lots of research has gone into segmentation of monotone images, however, recently more research has gone into segmenting colour images. Genetic algorithms have been shown to be a viable method to segment an image. The island model has been shown to be the most effective model for parallel genetic algorithms. However, little research has gone into developing a PGA for a Grid computing environment, which consists of heterogeneous, nondedicated resources. This paper presents some of the key segmentation techniques that have been developed. It then goes into genetic algorithms and how they have been parallelised. It finally looks at the little research that has been done on Grid-based GAs.

[1]  Rolf Adams,et al.  Seeded Region Growing , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  S. Beucher.  Use of watersheds in contour detection , 1979 .

[3]  David E. Goldberg,et al.  Efficient Parallel Genetic Algorithms: Theory and Practice , 2000 .

[4]  Ami Marowka,et al.  The GRID: Blueprint for a New Computing Infrastructure , 2000, Parallel Distributed Comput. Pract..

[5]  Andreas Koschan,et al.  Colour Image Segmentation: A Survey , 1994 .

[6]  Guner S. Robinson,et al.  Color Edge Detection , 1976, Optics & Photonics.

[7]  Kenneth E. Barner,et al.  Tactile imaging using watershed-based image segmentation , 2000, Assets '00.

[8]  P.K Sahoo,et al.  A survey of thresholding techniques , 1988, Comput. Vis. Graph. Image Process..

[9]  Ching Y. Suen,et al.  A recursive thresholding technique for image segmentation , 1998, IEEE Trans. Image Process..

[10]  Heggere S. Ranganath,et al.  Solving discontinuity and distortion problems in boundary based image segmentation and representation , 2007, ACM-SE 45.

[11]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  José Ignacio Hidalgo,et al.  Is the island model fault tolerant? , 2007, GECCO '07.

[13]  Nobuyuki Otsu,et al.  ATlreshold Selection Method fromGray-Level Histograms , 1979 .

[14]  Edmund K. Burke,et al.  The Speciating Island Model: An alternative parallel evolutionary algorithm , 2006, J. Parallel Distributed Comput..

[15]  Francine Berman,et al.  Master/slave computing on the Grid , 2000, Proceedings 9th Heterogeneous Computing Workshop (HCW 2000) (Cat. No.PR00556).

[16]  Andrew Mehnert,et al.  An improved seeded region growing algorithm , 1997, Pattern Recognit. Lett..

[17]  S. Beucher,et al.  Morphological segmentation , 1990, J. Vis. Commun. Image Represent..

[18]  Zheng Lin,et al.  Unseeded Region Growing for 3D Image Segmentation , 2000, VIP.

[19]  Yee-Hong Yang,et al.  Multiresolution Color Image Segmentation , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[20]  Zbigniew Skolicki,et al.  The influence of migration sizes and intervals on island models , 2005, GECCO '05.

[21]  Jing Li Wang,et al.  Color image segmentation: advances and prospects , 2001, Pattern Recognit..