Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Efficient Processing of a Rainfall Simulation Watershed on an FPGA-Based Architecture with Fast Access to Neighbourhood Pixels | Hindawi Publishing Corporation EURASIP Journal on Embedded Systems Volume 2009 Article ID318654 19 pages doi 2009 318654 Research Article Efficient Processing of a Rainfall Simulation Watershed on an FPGA-Based Architecture with Fast Access to Neighbourhood Pixels Lee Seng Yeong Christopher Wing Hong Ngau Li-Minn Ang and Kah Phooi Seng School of Electrical and Electronics Engineering The University of Nottingham 43500 Selangor Malaysia Correspondence should be addressed to Lee Seng Yeong yls@ Received 15 March 2009 Accepted 9 August 2009 Recommended by Ahmet T. Erdogan This paper describes a hardware architecture to implement the watershed algorithm using rainfall simulation. The speed of the architecture is increased by utilizing a multiple memory bank approach to allow parallel access to the neighbourhood pixel values. In a single read cycle the architecture is able to obtain all five values of the centre and four neighbours for a 4-connectivity watershed transform. The storage requirement of the multiple bank implementation is the same as a single bank implementation by using a graph-based memory bank addressing scheme. The proposed rainfall watershed architecture consists of two parts. The first part performs the arrowing operation and the second part assigns each pixel to its associated catchment basin. The paper describes the architecture datapath and control logic in detail and concludes with an implementation on a Xilinx Spartan-3 FPGA. Copyright 2009 Lee Seng Yeong et al. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. 1. Introduction Image segmentation is often used as one of the main stages in object-based image processing. For example it is often used as a preceding stage in object classification 1-3 and object-based image compression 4-6 . In both these examples image .
