VisionTutor
Computer Vision Course

Chapter 1 - Introduction

The course starts with an overview of the syllabus and an introduction to computer vision concepts. The Laboratory Guide teaches basic KBVision System interaction, such as runnin tasks and examining output.

Chapter 2 - Image Formation

Explores the basic issues of digital imagery: geometry, radiometry, photometry, and digitization. Emphasis on the digitization process, particulary in terms of how an image may be viewed as a distored version of an actual scene.

Chapter 3 - Image Enhancement

Exploration of linear and non-linear filtering, histogram equalization, and other image enhancement techniques.

Chapter 4 - Edge Detection

Analysis of various types of edge detection schemes, such as first and second derivative, Sobel, Prewitt and facit models. Other related topics such as edge thresholding and edge thinning are also covered.

Chapter 5 - Morphology

Analysis of binary and grayscale morphology. Examples illustrate the theoretical nature of these operations, and how they are used in various applications.

Chapter 6 - Region Segmentation

Explores a variety of region segmentation algorithms, such as region split and merge techniques, region growing, histogram peak / valley analysis, and several thresholding methods.

Chapter 7 - Convolution, Filtering, and Fourier Transform

Exploration of convolution, filtering and Fourier transform as fundamental image processing techniques. Important relationships between the spatial processing approach of convolution and the frequency domain approach of Fourier filtering.

Chapter 8 - Feature Extraction

Introduction to abstract feature extraction techniques. Line and boundary detection, color and shape measurement, boundary classification, and the use of the Hough transform.

VisionTutor Index