This section addresses many of the low-level processing as well as imaging fundamentals.

Chapter 1: Software Tools for Imaging

Chapter 2: Image Generation, Acquisition, and Processing

Chapter 3: Image-based Modeling and Algorithms

Chapter 4: Mathematical Morphology

Chapter 5: Image Geometry and Multi-view Geometry

Chapter 6: 3D Imaging

Chapter 7: Novel Noise Reduction Algorithms

Chapter 8: Image Restoration

Chapter 9: Enhancement Techniques

Chapter 10: Segmentation Techniques

Chapter 11: Motion and Tracking Algorithms and Applications

Chapter 12: Watermarking Methods and Protection + Wavelet Methods

Chapter 13: Image Data Structures and Databases

Chapter 14: Image Compression, Coding, and Encryption

Chapter 15: Video Analysis

Chapter 16: Multi-resolution Imaging Techniques

Chapter 17: Performance Analysis and Evaluation

Chapter 18: Multimedia Systems and Applications

Chapter 19: Novel Image Processing Applications

Section 2: COMPUTER VISION (about 25 articles)

This section addresses many of the mid- to high-level processing as well as vision fundamentals.

Chapter 20: Camera Networks and Vision

Chapter 21: Sensors and Early Vision

Chapter 22: Machine Learning Technologies for Vision

Chapter 23: Image Feature Extraction

Chapter 24: Cognitive and Biologically Inspired Vision

Chapter 25: Object Recognition

Chapter 26: Soft Computing Methods in Image Processing and Vision

Chapter 27: Stereo Vision

Chapter 28: Active and Robot Vision

Chapter 29: Face and Gesture Recognition

Chapter 30: Fuzzy and Neural Techniques in Vision

Chapter 31: Medical Image Processing and Analysis

Chapter 32: Novel Document Image Understanding Techniques

Chapter 33: Special-purpose Machine Architectures for Vision

Chapter 34: Biometric Authentication

Chapter 35: Novel Vision Application and Case Studies

Section 3: PATTERN RECOGNITION (about 20 articles)

This section presents a number of pattern recognition algorithms and methodologies that are of value to the image processing and computer vision research communities.

Chapter 36: Supervised and Un-supervised Classification Algorithms

Chapter 37: Clustering Techniques

Chapter 38: Dimensionality Reduction Methods in Pattern Recognition

Chapter 39: Symbolic Learning

Chapter 40: Ensemble Learning Algorithms

Chapter 41: Parsing Algorithms

Chapter 42: Bayesian Methods in Pattern Recognition and Matching

Chapter 43: Statistical Pattern Recognition

Chapter 44: Invariance in Pattern Recognition

Chapter 45: Knowledge-based Recognition

Chapter 46: Structural and Syntactic Pattern Recognition

Chapter 47: Applications Including: Security, Medicine, Robotic, GIS, Remote Sensing, Industrial Inspection, Nondestructive Evaluation (or NDE), ...

Chapter 48: Case studies and Emerging technologies