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Image Processing Using Pulse-coupled Neural Networks [Paperback / softback]

(George Mason University, Virginia, USA), ,
  • Format: Paperback / softback, 176 pages, height: 240 mm, 89 figures, index
  • Series: Perspectives in Neural Computing
  • Pub. Date: 31-May-1998
  • Publisher: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • ISBN-10: 3540762647
  • ISBN-13: 9783540762645
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  • Paperback / softback
  • Price: 50,43 €*
  • * This title is out of print. Used copies may be available, but delivery only inside Baltic States
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Image Processing Using Pulse-coupled Neural NetworksLarger Image
  • Format: Paperback / softback, 176 pages, height: 240 mm, 89 figures, index
  • Series: Perspectives in Neural Computing
  • Pub. Date: 31-May-1998
  • Publisher: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • ISBN-10: 3540762647
  • ISBN-13: 9783540762645
Other books in subject:
Permanent link: https://www.kriso.ee/db/9783540762645.html
Keywords:
This volume reviews the theoretical foundations of pulse-coupled neural networks, and then discusses image processing applications including segmentation and foveation. It also looks at the PCNN ability to process logical arguments and at how to implement it into specialized hardware.
1. Introduction and Theory
1(10)
1.1 General Aspects
1(1)
1.2 The State of Traditional Image Processing
2(3)
1.2.1 Generalisation vs. Discrimination
2(1)
1.2.2 The World of Inner Products
3(1)
1.2.3 The Mammalian Visual System
4(1)
1.2.4 Where Do We Go From Here?
5(1)
1.3 Visual Cortex Theories
5(3)
1.3.1 The Eckhorn Model
5(2)
1.3.2 The Rybak Model
7(1)
1.3.3 The Parodi Model
8(1)
1.4 The Visual Cortex and Simulation Theory
8(2)
1.5 Introduction to Applications
10(1)
References
10(1)
2. PCNN Theory
11(10)
2.1 The Original PCNN Model
11(4)
2.2 Time Signal Experiments
15(2)
2.3 PCNN Alterations
17(1)
References
18(3)
3. PCNN Image Processing
21(12)
3.1 Important Features
21(2)
3.2 Image Fundamentals and the PCNN
23(1)
3.3 Blood Cell Identification
24(1)
3.4 Aircraft Identification
25(2)
3.5 Mammography
27(2)
3.6 Aurora Borealis
29(1)
References
30(3)
4. The PCNN Kernel
33(6)
4.1 1/R Connections
34(2)
4.2 Asymmetric Kernel
36(2)
4.3 On-Centre/Off-Surround Kernel
38(1)
4.4 Discussion
38(1)
References
38(1)
5. Target Recognition
39(10)
5.1 Traditional Target Recognition
40(1)
5.2 Traditional Correlation Filter Target Recognition
41(3)
5.3 Employing the PCNN
44(2)
5.4 Image Factorisation using the PCNN
46(1)
References
47(2)
6. Dealing with Noise
49(10)
6.1 Noise and the PCNN
49(2)
6.2 Noise Reduction by a Signal Generator
51(3)
6.3 Fast Linking
54(2)
6.4 Summary
56(1)
References
57(2)
7. Feedback
59(6)
7.1 The Feedback Pulse-Coupled Neural Network
59(2)
7.2 Sample Problems
61(2)
7.3 Summary
63(1)
References
63(2)
8. Object Isolation
65(16)
8.1 The Fractional Power Filter
66(2)
8.2 Object Isolation System
68(1)
8.3 An Example
68(7)
8.4 Dynamic Object Isolation
75(3)
8.5 Shadowed Objects
78(1)
References
79(2)
9. Foveation
81(10)
9.1 The Foveation Algorithm
82(3)
9.2 Target Recognition by a PCNN Based Foveation Model
85(4)
9.3 Summary
89(1)
References
89(2)
10. Image Fusion
91(10)
10.1 The Multi-Spectral PCNN
92(2)
10.2 Pulse-Coupled Image Fusion Design
94(2)
10.3 An Example
96(2)
10.4 Example Fusing a Wavelet Filtered Images
98(1)
10.5 Summary
99(1)
References
100(1)
11. Software and Hardware Realisation
101(30)
11.1 Software
101(19)
11.2 Parallel Implementation
120(2)
11.3 A Simplified Implementation
122(1)
11.4 Optical Implementation
123(2)
11.5 Implementation in VLSI
125(1)
11.6 Implementation in FPGA
126(4)
References
130(1)
12. Summary, Applications and Future Research
131(18)
12.1 Object Isolation
133(1)
12.2 Dynamic Object Isolation
133(1)
12.3 Colour Image Processing
134(1)
12.4 Histogram Considerations
135(1)
12.5 Maze Solution
136(1)
12.6 Foveation
137(2)
12.7 Object Icons
139(1)
12.8 Syntactical Information Processing
140(1)
12.9 Fused PCNN
141(1)
12.10 Hardware Implementations
141(3)
12.11 Future Research
144(1)
References
145(4)
Appendix: Where to Find PCNN Computer Code 149(2)
Index 151