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Full Description
Emerging Trends in Image Processing, Computer Vision, and Pattern Recognition discusses the latest in trends in imaging science which at its core consists of three intertwined computer science fields, namely: Image Processing, Computer Vision, and Pattern Recognition. There is significant renewed interest in each of these three fields fueled by Big Data and Data Analytic initiatives including but not limited to; applications as diverse as computational biology, biometrics, biomedical imaging, robotics, security, and knowledge engineering. These three core topics discussed here provide a solid introduction to image processing along with low-level processing techniques, computer vision fundamentals along with examples of applied applications and pattern recognition algorithms and methodologies that will be of value to the image processing and computer vision research communities.
Drawing upon the knowledge of recognized experts with years of practical experience and discussing new and novel applications Editors' Leonidas Deligiannidis and Hamid Arabnia cover;
Many perspectives of image processing spanning from fundamental mathematical theory and sampling, to image representation and reconstruction, filtering in spatial and frequency domain, geometrical transformations, and image restoration and segmentation
Key application techniques in computer vision some of which are camera networks and vision, image feature extraction, face and gesture recognition and biometric authentication
Pattern recognition algorithms including but not limited to; Supervised and unsupervised classification algorithms, Ensemble learning algorithms, and parsing algorithms.
How to use image processing and visualization to analyze big data.
Contents
IMAGE PROCESSING (about 30 articles)
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
