Communications of the ACM - Special issue on parallelism
On shrinking binary picture patterns
Communications of the ACM
Fast parallel processing array algorithms for some graph problems(Preliminary Version)
STOC '79 Proceedings of the eleventh annual ACM symposium on Theory of computing
Fast Image Labeling Using Local Operators on Mesh-Connected Computers
IEEE Transactions on Pattern Analysis and Machine Intelligence
Parallel Architectures and Algorithms for Image Component Labeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
Finding connected components on a scan line array processor
Proceedings of the seventh annual ACM symposium on Parallel algorithms and architectures
IPF for real-time image processing on massively parallel architectures
PACT '95 Proceedings of the IFIP WG10.3 working conference on Parallel architectures and compilation techniques
Parallel Image Component Labeling With Watershed Transformation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Parallel computing with generalized cellular automata
Progress in computer research
Parallel computing with generalized cellular automata
Progress in computer research
A duality theorem for two connectivity-preserving parallel shrinking transformations
Future Generation Computer Systems - Cellular automata CA 2000 and ACRI 2000
Parallel Algorithms for Image Processing: Practical Algorithms with Experiments
IPPS '96 Proceedings of the 10th International Parallel Processing Symposium
The design and development of ZPL
Proceedings of the third ACM SIGPLAN conference on History of programming languages
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Two parallel algorithms are presented for the problem of labeling the connected components of a binary image. The machine model is an SIMD two-dimensional mesh-connected computer consisting of an N*N array of processing elements, each containing a single pixel of an N*N image. Both new algorithms use a local shrinking operation defined by S. Levialdi (1972) and have time complexities of O(N log N) bit operations, making them the fastest local algorithms for the problem. Compared to other approaches with similar or better asymptotic time complexities, this local approach greatly simplifies the algorithms and reduces the constants of proportionality by nearly two orders of magnitude, making them the first practical algorithms for the problem. The two algorithms differ in the amount of memory required per processing element; the first uses O(N) bits, while the second uses a novel compression scheme to reduce the requirement to O(log N) bits.