Book contents
- Frontmatter
- Contents
- Acknowledgements
- 1 Introduction
- 2 Notation, definitions, and mathematical foundation
- 3 Characteristics and analysis of simple CNN templates
- 4 Simulation of the CNN dynamics
- 5 Binary CNN characterization via Boolean functions
- 6 Uncoupled CNNs: unified theory and applications
- 7 Introduction to the CNN Universal Machine
- 8 Back to basics: Nonlinear dynamics and complete stability
- 9 The CNN Universal Machine (CNN-UM)
- 10 Template design tools
- 11 CNNs for linear image processing
- 12 Coupled CNN with linear synaptic weights
- 13 Uncoupled standard CNNs with nonlinear synaptic weights
- 14 Standard CNNs with delayed synaptic weights and motion analysis
- 15 Visual microprocessors – analog and digital VLSI implementation of the CNN Universal Machine
- 16 CNN models in the visual pathway and the “Bionic Eye”
- Notes
- Bibliography
- Exercises
- Appendices
- Index
Exercises
Published online by Cambridge University Press: 28 May 2010
- Frontmatter
- Contents
- Acknowledgements
- 1 Introduction
- 2 Notation, definitions, and mathematical foundation
- 3 Characteristics and analysis of simple CNN templates
- 4 Simulation of the CNN dynamics
- 5 Binary CNN characterization via Boolean functions
- 6 Uncoupled CNNs: unified theory and applications
- 7 Introduction to the CNN Universal Machine
- 8 Back to basics: Nonlinear dynamics and complete stability
- 9 The CNN Universal Machine (CNN-UM)
- 10 Template design tools
- 11 CNNs for linear image processing
- 12 Coupled CNN with linear synaptic weights
- 13 Uncoupled standard CNNs with nonlinear synaptic weights
- 14 Standard CNNs with delayed synaptic weights and motion analysis
- 15 Visual microprocessors – analog and digital VLSI implementation of the CNN Universal Machine
- 16 CNN models in the visual pathway and the “Bionic Eye”
- Notes
- Bibliography
- Exercises
- Appendices
- Index
Summary
Chapter 2
Exercise 2.1 (Simple morph)
Given: two gray-scale images: P1 and P2
Input: U(t) = P1
Initial state: X(0) = P2
Boundary conditions: white frame
Output: Y(t) = a transition from P2 to P1.
Task
Design a single template, which implements this transition.
Example
Exercise 2.2 (Hexagonal neighborhood)
The standard CNN definition specifies that the cells form a rectangular grid. Anther feasible form could be a hexagonal grid.
Task
Give a formula for the side length and the area of a hexagon (measured in cells) in the case of a hexagonal cell grid, when the sphere of influence equals r.
Exercise 2.3 (Triangular neighborhood)
The standard CNN definition specifies that the cells form a rectangular grid. There are only three possibilities to cover the plane. These are rectangular, hexagonal, and triangular.
Task
Give a formula for the area of a triangle in the case of a triangular cell grid, when the sphere of influence equals r.
Chapter 3
Exercise 3.1 (Separate connected objects)
The problem to be solved is to separate connected objects. The example shows a test image where objects are all similar in size. All objects should be separated but their sizes must be preserved.
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- Information
- Cellular Neural Networks and Visual ComputingFoundations and Applications, pp. 361 - 388Publisher: Cambridge University PressPrint publication year: 2002