Image Processing

6.

Image Segmentation

6.1.

Fundamentals of Segmentation

6.1.1.

Definition and Objectives

6.1.1.1.

Region Homogeneity

6.1.1.2.

Boundary Detection

6.1.2.

Applications of Segmentation

6.1.2.1.

Object Recognition

6.1.2.2.

Medical Image Analysis

6.1.2.3.

Scene Understanding

6.1.3.

Segmentation Challenges

6.1.3.1.

Noise Sensitivity

6.1.3.2.

Illumination Variations

6.1.3.3.

Object Occlusion

6.2.

Detection of Discontinuities

6.2.1.

Point Detection

6.2.1.1.

Point Detection Masks

6.2.1.2.

Isolated Point Detection

6.2.1.3.

Threshold Selection

6.2.2.

6.2.2.1.

Line Detection Masks

6.2.2.1.1.

Horizontal Lines

6.2.2.1.2.

6.2.2.1.3.

6.2.2.2.

Line Strength Measurement

6.2.3.

6.2.3.1.

6.2.3.1.1.

6.2.3.1.2.

6.2.3.1.3.

6.2.3.1.4.

6.2.3.2.

First-Order Methods

6.2.3.2.1.

Gradient Operators

6.2.3.2.1.1.

6.2.3.2.1.2.

Prewitt Operator

6.2.3.2.1.3.

Roberts Operator

6.2.3.2.2.

Gradient Magnitude and Direction

6.2.3.2.3.

Non-maximum Suppression

6.2.3.3.

Second-Order Methods

6.2.3.3.1.

Laplacian of Gaussian

6.2.3.3.1.1.

Scale Parameter Selection

6.2.3.3.1.2.

Zero-crossing Detection

6.2.3.3.2.

Difference of Gaussians

6.2.3.4.

Canny Edge Detector

6.2.3.4.1.

Gaussian Smoothing

6.2.3.4.2.

Gradient Computation

6.2.3.4.3.

Non-maximum Suppression

6.2.3.4.4.

Double Thresholding

6.2.3.4.5.

Hysteresis Edge Tracking

6.3.

6.3.1.

Global Thresholding

6.3.1.1.

Simple Thresholding

6.3.1.2.

Basic Global Thresholding Algorithm

6.3.1.3.

Threshold Selection Methods

6.3.2.

Optimal Global Thresholding

6.3.2.1.

6.3.2.1.1.

Between-class Variance Maximization

6.3.2.1.2.

Within-class Variance Minimization

6.3.2.1.3.

Multi-level Thresholding

6.3.2.2.

Entropy-based Methods

6.3.3.

Adaptive Thresholding

6.3.3.1.

Local Mean Thresholding

6.3.3.2.

Local Gaussian Thresholding

6.3.3.3.

Variable Window Size

6.3.3.4.

Threshold Surface Fitting

6.4.

Region-Based Segmentation

6.4.1.

6.4.1.1.

Seed Selection Strategies

6.4.1.1.1.

Manual Seed Selection

6.4.1.1.2.

Automatic Seed Detection

6.4.1.2.

Growth Criteria

6.4.1.2.1.

Intensity Similarity

6.4.1.2.2.

Texture Similarity

6.4.1.2.3.

Statistical Tests

6.4.1.3.

Stopping Criteria

6.4.2.

Region Splitting and Merging

6.4.2.1.

Quadtree Decomposition

6.4.2.1.1.

Recursive Splitting

6.4.2.1.2.

Homogeneity Testing

6.4.2.2.

Merging Criteria

6.4.2.2.1.

Statistical Similarity

6.4.2.2.2.

Adjacency Requirements

6.4.2.3.

Split-and-Merge Algorithm

6.5.

Watershed Segmentation

6.5.1.

Watershed Algorithm Principles

6.5.1.1.

Topographic Interpretation

6.5.1.2.

Flooding Process

6.5.1.3.

Watershed Lines

6.5.2.

Marker-based Watershed

6.5.2.1.

Internal Markers

6.5.2.2.

External Markers

6.5.2.3.

Marker Selection Strategies

6.5.3.

Watershed Transform

6.5.3.1.

Distance Transform Preprocessing

6.5.3.2.

Gradient Magnitude Images

6.5.3.3.

Over-segmentation Issues

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