Design considerations in the real-time implementation of multiple expert image classifiers within a modular and flexible multiple-platform design environment

Real-Time Imaging

Volumn 4, Issue 5, 1998, Pages 361-376

  Rahman, A F R ^a   Fairhurst, M C ^a   Lee, P ^a  

^a UNIVERSITY OF KENT   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPUTATIONAL COMPLEXITY; COMPUTER SYSTEMS PROGRAMMING; COMPUTER VISION; CONSTRAINT THEORY; EXPERT SYSTEMS; IMAGE ANALYSIS; PIPELINE PROCESSING SYSTEMS; REAL TIME SYSTEMS;

MULTIPLE EXPERT IMAGE CLASSIFIERS;

PATTERN RECOGNITION SYSTEMS;

EID: 0032181488     PISSN: 10072014     EISSN: None     Source Type: Journal    
DOI: 10.1016/s1077-2014(98)90005-5     Document Type: Article

References (28)

1. Cao, J., Ahmadi, M. & Shridhar, M. (1995) Recognition of handwritten numerals with multiple feature and multistage classifier. Pattern Recognition 28: 153-160.
2. Kovács, Zs. M., Guerrieri, R., & Baccarani, G. (1994) Cooperative classifiers for high-quality handprinted character-recognition. Biosensors & Bioelectronics 9: 611-615.
3. Kittler, J. & Hatel, M. (1996) Improving recognition rates by classifier combination. In: Proc. 5th Int. Workshop on Frontiers of Handwriting Recognition, pp. 81-102.
4. Fairhurst, M. C. & Rahman, A. F. R. (1997) A generalised approach to the recognition of structurally similar handwritten characters. IEEE Proc. on Vision, Image and Signal Processing 144: 15-22.
5. Ho, T. K., Hull, J. J. & Srihari, S. N. (1994) Decision combination in multiple classifier systems. IEEE Trans. Pattern Analysis and Machine Intelligence 16: 66-75.
6. Hull, J. J., Commike, A. A. & Ho., T. K. Multiple algorithms for handwritten character recognition. In: Proc. 1st Int. Workshop on Frontiers in Handwriting Recognition, pp. 117-124.
7. Zhou, J. & Pavlidis, T. (1994) Discrimination of characters by a multi-stage recognition process. Pattern Recognition 27: 1539-1549.
8. Errico, G. D., Dimauro, G., Impedovo, S. & Pirlo, G. (1996) A new multi-expert system for handwritten digit recognition. In: Proc. 5th Int. Workshop on Frontiers of Handwriting Recognition, pp. 341-344.
9. Chi, Z., Wu, J. & Yan, H. (1995) Handwritten numeral recognition using self-organising maps and fuzzy rules. Pattern Recognition 28: 59-66.
10. Waterhouse, S. R. & Robinson, A. J. (1994) Classification using hierarchical mixtures of experts. In: Proc. IEEE Workshop on Neural Networks for Signal Processing IV, pp. 177-186.
11. Kimura, F. & Shridhar, M. (1991) Handwritten numeral recognition based on multiple algorithms. Pattern Recognition 24: 969-983.
12. Rahman, A. F. R. & Fairhurst, M. C. (1997) Introducing new multiple expert decision combination topologies: a case study using recognition of handwritten characters. In: Proc. 4th Int. Conference on Document Analysis and Recognition ICDAR97 2: 886-891.
13. Fairhurst, M. C. & Abdel Wahab, H. M. S. (1990) An interactive two-level architecture for a memory network pattern classifier. Pattern Recognition Letters 11: 537-540.
14. Rahman, A. F. R. & Fairhurst, M. C. (1997) Exploiting second order information to design a novel multiple expert decision combination platform for pattern classification. Electronics Letters 33: 476-477.
15. Tang, Y. Y., Lee, S. W. & Suen, C. Y. (1996) Automatic document processing: a survey. Pattern Recognition 29: 1931-1952.
16. NIST Special Databases 1-3, 6-8, 19, 20. National Institute of Standards and Technology, Gaithersburg, MD 20899, USA.
17. Zucker, S. W. (1976) Region growing: childhood and adolescence. Comput Graph Image Process, 5: 382-399.
18. Schalkoff, R. J. (1989) Digital processing and computer vision. New York Chichester Brisbane Toronto Singapore: John Wiley & Sons.
19. Davies, E. R. (1990) Machine vision: theory, algorithms and practicalities. London San Diego New York Boston Sydney Tokyo Toronto: Academic Press.
20. Reiss, T. H. (1993) Recognizing planer objects using invariant image features. Berlin Heidelberg: Springer-Verlag.
21. Tou, J. T. & Gonzalez, R. C. (1974) Pattern Recognition Principles. Reading, MA: Addison-Wesley.
22. Fairhurst, M. C. & Mattaso Maia, M. A. G. (1986) Performance comparison in hierarchical architectures for memory network pattern classifiers. Pattern Recognition Letters 4: 121-124.
23. Fairhurst, M. C. & Stonham, T. J. (1976) A classification system for alphanumeric characters based on learning network techniques. Digital Processes 2: 321-339.
24. Devijver, P. A. & Kittler, J. (1982) Pattern Recognition. London: Prentice Hall Int.
25. Fairhurst, M. C. & Rahman, A. F. R. (1996) A new multi-expert architecture for high performance object recognition. In: Proc. Int. Sym. on Intelligent Systems and Advanced Manufacturing, Machine Vision Applications, Architectures, and Systems Integration V, SPIE 2908, pp. 140-151.
26. Rahman, A. F. R. & Fairhurst, M. C. (1997) A novel pair-wise recognition scheme for handwritten characters in the frame-work of a multi-expert configuration. In: Del Bimbo, A. (ed.). Lecture Notes in Computer Science: 1311. Proc 9th Int Conf on Image Analysis and Processing, 2: 624-631.
27. Leroux, M., Lethélier, E., Gilloux, M. & Lemarié, B. (1997) Automatic reading of handwritten amounts on French checks. Int Journal of Pattern Recognition and Artificial Intelligence 11: 619-638.
28. Knerr, S., Anisimov, V., Baret, O., Gorski, N., Price, D. & Simon, J. C. (1997) The A2iA Intercheque System: courtesy amount and legal amount recognition for French checks. Int Journal of Pattern Recognition and Artificial Intelligence 11: 505-548.