Image Segmentation Essay

Fingerprint Image Segmentation: A Review of State of the Art Techniques
Krishna Prasad K. #1 & Dr. P.S. Aithal*2
# Research Scholar, College of Computer and Information Science, Srinivas University, Mangaluru-575001, Karnataka, India
* College of Computer and Information Science, Srinivas University, Mangaluru-575001, Karnataka, India
E-mail: [e-mail]

ABSTRACT
In Automatic Fingerprint Identification System (AFIS), preprocessing of the image is crucial process in deciding the quality and performance of system. Preprocessing is consists many stages as Segmentation, Enhancement, Binarisation, and Thinning. In these segmentation is one of the steps of preprocessing which differentiates foreground and background region of fingerprint

There is enough amount of literature with respect to image segmentation process or approach dating back over thirty years. Jain & Dubes, (1988) [15], explains algorithm for clustering in his book, these early approaches for clustering can be used for segmentation, which acts as basis for many new methods including boundary based segmentation such as Canny edge detection Canny, 1986 [16]. In this method researcher defines comprehensive set of goals for the computation of edge detection points. Adams and Bishof, (1994) [17], proposed segmentation algorithm for images, which are intensity images with certain characteristics like robust, rapid, and free of tuning parameters. This algorithm can take input as either individual pixels or regions and points these inputs to some region formed by the algorithm. The algorithms explains two methods in which input corresponds to region, either by using manual seed or by automated procedure. Chakraborty et al., (1996) [18], proposed a method which combines region based segmentation and boundary finding to form new method which is more robust to noise and high performance. The literature covered above is some general segmentation algorithms which will apply for any types of images. In literature there are many studies available, which mainly focuses on fingerprint image

Bazen and Sabih H. Gerez. 2001. Segmentation of Fingerprint Images, Workshop on Circuits, Systems and Signal Processing, Veldhoven. The Netherlands.
[13] C. Domeniconi, S. Tari, and P. Liang. (1998). Direct Gray Scale Ridge Reconstruction in Fingerprint Images. International Conference on Acoustics, Speech, and Signal Processing, 5, 2941-2944.
[14] Short, N. J., Abbott, A. L., Hsiao, M. S., & Fox, E. A. (2011, October). A Bayesian approach to fingerprint minutia localization and quality assessment using adaptable templates. International Joint Conference on Biometrics, (IJCB) 2011 (pp. 1-7).
[15] Jain, A. K., & Dubes, R. C. (1988). Algorithms for clustering data. Prentice-Hall, Inc..
[16] Canny, J. (1986). A computational approach to edge detection. IEEE Transactions on pattern analysis and machine intelligence, (6), 679-698.
[17] Adams, R., & Bischof, L. (1994). Seeded region growing. IEEE Transactions on pattern analysis and machine intelligence, 16(6), 641-647.
[18]Chakraborty, A., Staib, L. H., & Duncan, J. S. (1996). Deformable boundary finding in medical images by integrating gradient and region information. IEEE Transactions on Medical Imaging, 15(6),