Intended for undergraduate and graduate students and all who are interested in computer vision, medical imaging, and human visual perception, this interactive book presents a tutorial approach to mastering the mathematics of computer vision and image analysis. One purpose of the book is to bridge the gap between the world of human visual perception and the world of multi-scale computer vision theory and applications. All chapters are written in

*Mathematica*, and the text and code included invite readers to explore computer vision on their own.

Front-End Vision and Multi-Scale Image Analysis | Apertures and the Notion of Scale | Foundations of Scale-Space | The Gaussian Kernel | Gaussian Derivatives | Multi-scale Derivatives: Implementations | Differential Structure of Images | Natural Limits on Observations | Differentiation and Regularization | The Front-End Visual System--the Retina | A Scale-Space Model for the Retinal Sampling | The Front-End Visual System--LGN and Cortex | The Front-End Visual System--Cortical Columns | Deep Structure I. Watershed Segmentation | Deep Structure II. Catastrophe Theory | Deep Structure III. Topological Numbers | Deblurring Gaussian Blur | Multi-scale Optic Flow | Color Differential Structure | Steerable Kernels | Scale-Time | Geometry-Driven Diffusion | Epilog: Introduction to

*Mathematica*, The Concept of Convolution, Installing the Book and Packages, First Start with

*Mathematica*: Tips & Tricks

Differential Equations,

Discrete Mathematics,

Geometry,

Graphics,

Life Sciences,

Modeling and Simulation,

Physics,

Social Sciences