DocumentCode
1245160
Title
A high speed systolic architecture for labeling connected components in an image
Author
Ranganathan, N. ; Mehrotra, R. ; Subramanian, S.
Author_Institution
Center for Microelectron. Res., Univ. of South Florida, Tampa, FL, USA
Volume
25
Issue
3
fYear
1995
fDate
3/1/1995 12:00:00 AM
Firstpage
415
Lastpage
423
Abstract
Connected components detection and labeling is an essential step in many image analysis techniques. The efficiency of the connected components labeling algorithm is critical for many image processing and machine vision applications that require real time response. The advances in the areas of parallel processing and VLSI technology can be exploited in designing hardware algorithms of high speed and throughput. In this paper, the authors propose a systolic algorithm and architecture for finding connected components in an image. The architecture is simple and can be implemented as a special purpose VLSI chip. Although, the algorithm has a time complexity of O(N2), this is in terms of the actual clock cycle which is estimated as 25 nano seconds. The proposed hardware can process a 128×128 image in 0.85 msec and uses 128 processors whereas the MPP requires 94.6 msec with 16384 processors. The only special purpose hardware that exists requires 300 msec to label a 512×512 image which can be accomplished in 13.5 msec using the authors´ proposed hardware
Keywords
computational complexity; image processing; systolic arrays; VLSI technology; connected components detection; high speed systolic architecture; image analysis techniques; labeling; machine vision; parallel processing; real time response; special purpose VLSI chip; systolic algorithm; Algorithm design and analysis; Clocks; Hardware; Image analysis; Image processing; Labeling; Machine vision; Parallel processing; Throughput; Very large scale integration;
fLanguage
English
Journal_Title
Systems, Man and Cybernetics, IEEE Transactions on
Publisher
ieee
ISSN
0018-9472
Type
jour
DOI
10.1109/21.364855
Filename
364855
Link To Document