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ITK Second Order Derivative for Image Processing

Explore the second-order derivative with ITK filters for edge detection and enhancement in image processing. Learn about Laplacian filters, noise reduction techniques, and view experimental results with different sigma values.

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ITK Second Order Derivative for Image Processing

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  1. Second Order Derivative 學生：謝宗佑 指導教授：張顧耀 教授 日期：2007/06/05

  2. Outline • Introduction • Using ITK’s Second Order Filter • Experiment Results • Demo

  3. Introduction • What is the second order derivative? • Give the gradient magnitude. • Purpose in image processing: • Detect edge • Edge enhancement • e.g. • Laplacian filter

  4. Introduction

  5. Introduction • Mask:

  6. Introduction • Noise problem: • How to solve: • Smooth the image first • Erosive noise

  7. Using ITK’s Second Order Filter • Description: • This filter is implemented using the recursive Gaussian filters. • Header: • #include “itkLaplacianRecursiveGaussianImageFilter.h” • Function: • SetSigma(RealType sigma)

  8. Using ITK’s Second Order Filter • Flowchart:

  9. Experiment Results σ=1 σ=3 σ=5 σ=1 σ=3 σ=5

  10. Demo • Flowchart: Read image flowchart Filter flowchart

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