Implementation of Image Segmentation Using FPGA

The proposed work presents FPGA based architecture for image segmentation. It has found application in forensic science and also in digital multimedia for creating image dazzling effect. Currently the image processing algorithms are limited to software implementation which is slower due to the limited processor speed. So a dedicated processor for segmentation was required which was not possible until advancement in VLSI technology. Now more complex system can be integrated on a single chip providing a platform to process real time algorithms on hardware. Image Segmentation is an important technique in the area of image processing with wide applications in Medicine, Remote sensing to mention a few. A lot of research work is in progress in various areas resulting in many computationally efficient algorithms. There are conventional as well as improvised segmentation algorithms depending on the application. The choice of the technique in most cases depends on the application and image in question rather than a generalized method. The proposed work uses histogram method for segmentation. The conventional histogram method is modified to adopt for automatically determining the threshold for different regions in the image. The objective of this project is to realize the segmentation algorithm on FPGA. FPGA implementation renders it more useful for real time applications.

[1]  Andrea Prati,et al.  Image convolution on FPGAs: the implementation of a multi-FPGA FIFO structure , 1998, Proceedings. 24th EUROMICRO Conference (Cat. No.98EX204).

[2]  Wilhelm Burger,et al.  Digital Image Processing - An Algorithmic Introduction using Java , 2008, Texts in Computer Science.

[3]  Wen-Xiong Kang,et al.  The Comparative Research on Image Segmentation Algorithms , 2009, 2009 First International Workshop on Education Technology and Computer Science.

[4]  Wang Qing,et al.  FPGA based Sobel algorithm as vehicle edge detector in VCAS , 2003, International Conference on Neural Networks and Signal Processing, 2003. Proceedings of the 2003.

[5]  William K. Pratt,et al.  Digital image processing, 2nd Edition , 1991, A Wiley-Interscience publication.

[6]  Hans Jürgen Mattausch,et al.  Image segmentation and pattern matching based FPGA/ASIC implementation architecture of real-time object tracking , 2006, Asia and South Pacific Conference on Design Automation, 2006..

[7]  R.J. Almeida,et al.  Comparison of fuzzy clustering algorithms for classification , 2006, 2006 International Symposium on Evolving Fuzzy Systems.

[8]  Ai Yangli.  Study of FPGA-based Parallel Processing of Sobel Operator , 2005 .

[9]  H Ei.  An Improved Thread Edge Detection Method Based On Sobel Algorithm , 2008 .

[10]  K.J. Shanthi,et al.  International Conference on Intelligent and Advanced Systems 2007 Skull Stripping and Automatic Segmentation of Brain Mri Using Seed Growth and Threshold Techniques , 2022 .

[11]  Tamal Bose,et al.  Digital Signal and Image Processing , 2003 .

[12]  K. J. Shanthi,et al.  Segmentation of Brain MRI and Comparison Using Different Approaches of 2D Seed Growing , 2009 .

[13]  John F. Haddon,et al.  Generalised threshold selection for edge detection , 1988, Pattern Recognit..

[14]  Tsutomu Maruyama,et al.  How fast is an FPGA in image processing? , 2008, 2008 International Conference on Field Programmable Logic and Applications.

[15]  O.R.P. Bellon,et al.  New improvements to range image segmentation by edge detection , 2002, IEEE Signal Processing Letters.

[16]  Douglas L. Perry,et al.  VHDL: Programming by Example , 2002 .

[17]  Sudhakar Yalamanchili.  Introductory VHDL: From Simulation to Synthesis , 2000 .

[18]  Hans Jurgen Mattausch,et al.  Pixel-parallel digital CMOS implementation of image segmentation by region growing , 2005 .