Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Computation in artificially evolved, non-uniform cellular automata
Theoretical Computer Science - Special issue: cellular automata
A new kind of science
Evolution of Parallel Cellular Machines: The Cellular Programming Approach
Evolution of Parallel Cellular Machines: The Cellular Programming Approach
A Cellular-Programming Approach to Pattern Classification
EuroGP '98 Proceedings of the First European Workshop on Genetic Programming
Marching-pixels: a new organic computing paradigm for smart sensor processor arrays
Proceedings of the 2nd conference on Computing frontiers
Theory of Self-Reproducing Automata
Theory of Self-Reproducing Automata
Marching Pixels - Using Organic Computing Principles in Embedded Parallel Hardware
PARELEC '06 Proceedings of the international symposium on Parallel Computing in Electrical Engineering
Realising emergent image preprocessing tasks in cellular-automaton-alike massively parallel hardware
International Journal of Parallel, Emergent and Distributed Systems - Emergent Computation
Emergent algorithms for centroid and orientation detection in high-performance embedded cameras
Proceedings of the 5th conference on Computing frontiers
Improving the Behavior of Creatures by Time-Shuffling
ACRI '08 Proceedings of the 8th international conference on Cellular Automata for Reseach and Industry
Solving All-to-All Communication with CA Agents More Effectively with Flags
PaCT '09 Proceedings of the 10th International Conference on Parallel Computing Technologies
CA Models for Target Searching Agents
Electronic Notes in Theoretical Computer Science (ENTCS)
All-to-all communication with CA agents by active coloring and acknowledging
ACRI'10 Proceedings of the 9th international conference on Cellular automata for research and industry
| Hi-index | 0.00 |
Current industrial applications require fast and robust image processing in systems with low size and power dissipation. One of the main tasks in industrial vision is fast detection of centroids of objects. This paper compares three different approaches for finding geometric algorithms for centroid detection which are appropriate for a fine-grained parallel hardware architecture in an embedded vision chip. The algorithms shall comprise emergent capabilities and high problem-specific functionality without requiring large amounts of states or memory. For that problem, we consider uniform and non-uniform cellular automata (CA) as well as Genetic Programming. Due to the inherent complexity of the problem, an evolutionary approach is applied. The appropriateness of these approaches for centroid detection is discussed.