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Digital Image Processing

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Topics 10

Introduction to Digital Image Processing
This topic will cover the basic concepts and fundamentals of digital image processing, inc...
Image Enhancement Techniques
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Image Filtering and Restoration
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Image Segmentation
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Feature Extraction and Object Recognition
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Morphological Image Processing
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Image Compression Techniques
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Color Image Processing
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Medical Image Processing
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Image Processing Applications
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Unit Outline 45h

Learning Objectives

5 objectives
  • Understand the fundamental concepts and systems of digital image processing.
  • Apply various image enhancement, filtering, and restoration techniques.
  • Analyze and implement image segmentation and feature extraction methods.
  • Explore advanced topics including morphological processing, compression, and color image techniques.
  • Examine real-world applications of digital image processing, especially in medical and industrial domains.

Content Outline

Preview

1. Introduction to Digital Image Processing

  • 1.1 Analog vs Digital Images
    • Definition and characteristics
    • Advantages of digital images
  • 1.2 Image Representation
    • Pixels and resolution
    • Image types (binary, grayscale, color)
  • 1.3 Components of Digital Image Processing Systems
    • Image acquisition
    • Storage and retrieval
    • Processing and display

2. Image Enhancement Techniques

  • 2.1 Contrast Enhancement
    • Contrast stretching
    • Intensity transformations
  • 2.2 Histogram Equalization
    • Histogram concepts
    • Global and adaptive methods
  • 2.3 Spatial Domain Methods
    • Point processing
    • Neighborhood processing
  • 2.4 Frequency Domain Methods
    • Fourier Transform basics
    • Filtering in frequency domain

3. Image Filtering and Restoration

  • 3.1 Image Filters
    • Linear filters: averaging, Gaussian
    • Non-linear filters: median, adaptive filters
  • 3.2 Smoothing Filters
    • Noise reduction techniques
  • 3.3 Sharpening Filters
    • Edge enhancement
  • 3.4 Image Restoration Techniques
    • Noise modeling
    • Inverse filtering
    • Wiener filtering

4. Image Segmentation

  • 4.1 Thresholding Techniques
    • Global and adaptive thresholding
  • 4.2 Edge-Based Segmentation
    • Gradient operators
    • Edge linking and boundary detection
  • 4.3 Region-Based Segmentation
    • Region growing
    • Region splitting and merging
  • 4.4 Clustering Techniques
    • K-means clustering
    • Fuzzy clustering

5. Feature Extraction and Object Recognition

  • 5.1 Feature Types
    • Corners, edges, textures
  • 5.2 Feature Extraction Methods
    • Harris corner detector
    • SIFT and SURF (overview)
  • 5.3 Object Recognition and Classification
    • Pattern recognition basics
    • Template matching
    • Machine learning approaches

6. Morphological Image Processing

  • 6.1 Morphological Operations
    • Dilation
    • Erosion
    • Opening
    • Closing
  • 6.2 Applications
    • Noise removal
    • Image segmentation
    • Shape analysis

7. Image Compression Techniques

  • 7.1 Fundamentals of Compression
    • Lossless vs lossy compression
  • 7.2 Popular Algorithms
    • JPEG (Discrete Cosine Transform based)
    • PNG (lossless compression)
    • GIF (palette-based compression)
  • 7.3 Trade-offs
    • Compression ratio vs image quality

8. Color Image Processing

  • 8.1 Color Models
    • RGB, CMYK
    • HSI (Hue, Saturation, Intensity)
    • YUV
  • 8.2 Processing Techniques
    • Color enhancement
    • Color segmentation
    • Color space transformations

9. Medical Image Processing

  • 9.1 Medical Imaging Modalities
    • X-ray, MRI, CT, Ultrasound
  • 9.2 Enhancement Techniques for Medical Images
  • 9.3 Segmentation and Registration in Medical Imaging
  • 9.4 Applications
    • Disease diagnosis
    • Treatment planning

10. Image Processing Applications

  • 10.1 Satellite Imaging and Remote Sensing
  • 10.2 Facial Recognition Systems
  • 10.3 Autonomous Vehicles
  • 10.4 Forensic Analysis
  • 10.5 Multimedia Systems
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