Emerging Trends in Image Processing, Computer Vision and Pattern Recognition

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.

  Autorentext
  Leonidas Deligiannidis is a Professor of Computer Science and Networking at Wentworth Institute of Technology in Boston. His research examines Image Processing, Network Security and Information Visualization. Deligiannidis earned his PhD in Computer Science at Tufts University. Hamid R. Arabnia is currently a Full Professor of Computer Science at University of Georgia where he has been since October 1987. His research interests include Parallel and distributed processing techniques and algorithms, interconnection networks, and applications in Computational Science and Computational Intelligence (in particular, in image processing, medical imaging, bioinformatics, and other computational intensive problems). Dr. Arabnia is Editor-in-Chief of The Journal of is Associate Editor of IEEE Transactions on Information Technology in Biomedicine . He has over 300 publications (journals, proceedings, editorship) in his area of research in addition he has edited two titles Emerging Trends in ICT Security (Elsevier 2013), and Advances in Computational Biology (Springer 2012).

  Inhalt
  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