FOUNDATION OF MEDIA COMPUTING

This comprehensive book presents the foundation of media computing that cuts across various areas, including computer vision, computational photography, computational geometry, 3D computer graphics modeling, surgery planning and simulation, and music computing. It emphasizes both mathematical foundation and practical applications.

The wide range of illustrated examples and case studies will make this reference text useful for professionals, researchers, academics, graduate students and advanced undergraduate students in media computing and related areas.

Contents:

• Why Study Foundation?
• Mostly Linear Methods:
  • Linear Fitting
  • Matrix Differentiation
  • Simple Nonlinear Fitting
  • Vector Geometry
  • Spatial Transformation
• Mostly Nonlinear Methods:
  • Interpolation
  • Non-Rigid Transformation
  • Spatial Registration
  • Temporal Registration
  • Robust Methods
• Optimisation Methods:
  • Gradient Descent
  • Newton's Family
  • Optimisation without Derivatives
  • Constrained Optimisation
  • Stochastic Optimisation
• Structure Discovery:
  • Principal Component Analysis
  • Manifold Learning
• Media Problem Solving:
  • Problem Formulation
  • Algorithm Design
  • Case Study 1: Colour Calibration
  • Case Study 2: 3D Reconstruction
  • Case Study 3: Skull Reconstruction
  • Case Study 4: Face Space
  • Case Study 5: Melody Harmonisation
• Advanced and Related Topics:
  • Multi-Objective Optimisation
  • Mesh Parameterisation
  • Convex Optimisation
  • Connections to Neural Networks
  • The Big Picture

Readership: Graduate students, advanced graduate students, professionals, researchers and academics in computer vision and image analysis.