4 Computational Vision, Regularization, Connectionist, Morphology, Scale-Space, Perceptual Grouping, Wavelets, Color, Sensors, Optical, Laser, Radar Table of Contents (Back)
Keyword list at end

• 4.1 Regularization Theory and Practice
• 4.2 Connectionist Approaches to Computer Vision
• 4.3 Morphology
  • 4.3.1 Morphology - General, Surveys
  • 4.3.2 Morphology - Theory
    • 4.3.2.1 Morphological Operator Decomposition
    • 4.3.2.2 Color Morphology, Gray Scale Morphology
    • 4.3.2.3 Morphology for Range and 3-D data
  • 4.3.3 Morphology - Techniques and Applications
• 4.4 Scale Space and Multi-Scale Techniques
  • 4.4.1 Scale Space for Descriptions
  • 4.4.2 Scale Space Computation of Features
    • 4.4.2.1 Filters in Scale Space
  • 4.4.3 Scale Space Theory
    • 4.4.3.1 Scale Space -- Lindeberg References
• 4.5 Diffusion Process, Mechanism, or Technique
  • 4.5.1 Diffusion Process, Survey, Overview
  • 4.5.2 Diffusion Process for Enhancement, Restoration and Smoothing
• 4.6 General Computational Vision
• 4.7 Physics Based Vision
• 4.8 Perceptual Grouping, Perceptual Organization Techniques
  • 4.8.1 Perceptual Grouping, Saliency, Theory
  • 4.8.2 Perceptual Grouping, General Systems
  • 4.8.3 Grouping, Lines and Curves
    • 4.8.3.1 Contour Completion, Subjective Contours
  • 4.8.4 Grouping, Figure-Ground, Background, Foreground
• 4.9 Sensors for Machine Vision, Image Sensors
  • 4.9.1 Complex Log Mapping, Algorithms and Sensors
  • 4.9.2 Hilbert Scan for Compression and Representation
  • 4.9.3 Other Space Variant Sensors and Models
  • 4.9.4 Optical Sensors for Machine Vision
    • 4.9.4.1 Omnidirectional and Panoramic Sensors
      • 4.9.4.1.1 Catadioptric Cameras
  • 4.9.5 Range Sensors for Machine Vision
    • 4.9.5.1 Lasers Sensors for Range
    • 4.9.5.2 Acoustic, Sonar Sensors for Range
  • 4.9.6 Electron Microscope Images and Sensors
  • 4.9.7 Phase Unwrapping, Stereo Depth unwrapping
  • 4.9.8 Beamforming in SAR and Radar Analysis
• 4.10 Image Representation Techniques
  • 4.10.1 Wavelet Representations
    • 4.10.1.1 Wavelets, Surveys, Reviews, Overviews, Evaluations, General
    • 4.10.1.2 Wavelets Filter Design, Bases, and Implementations
    • 4.10.1.3 Wavelets, Mallat Papers
    • 4.10.1.4 Wavelets for Curves and Surfaces
    • 4.10.1.5 Using Wavelets for Detection, Recognition, Fusion
    • 4.10.1.6 Wavelets for Image Coding
      • 4.10.1.6.1 Wavelets for Image Coding -- Block, Region and Shape Based
      • 4.10.1.6.2 Wavelets for Image Coding -- Zero Tree Code
      • 4.10.1.6.3 Wavelets for Image Coding -- Quantization Issues
      • 4.10.1.6.4 Lossless, Reconstructions from Wavelet Coded Images
      • 4.10.1.6.5 Lifting Operator, Transform
      • 4.10.1.6.6 Wavelet Coding, Compression Applications, Hardware
    • 4.10.1.7 Wavelets for Motion and Video Coding
    • 4.10.1.8 Noise Removal, Wavelet Techniques
  • 4.10.2 Fractal Representations, Fractal Dimension
    • 4.10.2.1 Fractal Based Coding and Compression, Fractal Coding, Fractal Compression
• 4.11 Color and Its Use in Computer Vision
  • 4.11.1 Color, General Issues
  • 4.11.2 Color Models, Color Representation
    • 4.11.2.1 Reflections and Color Models, Reflectance
    • 4.11.2.2 Color Correction, Weather Effects
  • 4.11.3 Infrared Imagery Analysis
  • 4.11.4 Polarization Effects
  • 4.11.5 Color Constancy, Retinex
    • 4.11.5.1 Color Constancy, Recognition, Simon Fraser Univ. (Funt and Finlayson) Papers
    • 4.11.5.2 Color Constancy, Recognition, Healey Papers
  • 4.11.6 Recognition by Color Indexing
• 4.12 Radar, SAR Analysis
  • 4.12.1 Radar, Extraction of Features, Segmentation
  • 4.12.2 SAR, Generation, Image Construction, Reconstruction
  • 4.12.3 Radar, Speckle Analysis and Removal, Speckle Reduction, Despeckle
  • 4.12.4 Interferometric SAR Analysis, InSAR, IFSAR, ISAR
  • 4.12.5 Slope and Shape from Radar and SAR
    • 4.12.5.1 Surface Deformation From SAR, InSAR, IFSAR, Interferometry
  • 4.12.6 Microwave Sensors and Analysis