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Scope

Prologue: The broad area of Imaging Science is a field that is mainly concerned with the generation, collection, analysis, modification, and visualization of images. The field is multidisciplinary in that it includes topics that are traditionally covered in computer science, physics, mathematics, electrical engineering, AI, psychology and information theory where computer science acts as the topical bridge between all such diverse areas (for a formal definition of Imaging Science, refer to relevant wiki pages.) From the computer science perspective, the core of Imaging Science includes the following three intertwined computer science fields, namely: Image Processing, Computer Vision, and Pattern Recognition. This conference will cover the research trends in these three important areas. The list of topics of interest that appears below is not meant to be exhaustive.

    • IMAGE PROCESSING:

(Addresses ow-level processing as well as imaging fundamentals.)

- Software Tools for Imaging
- Image Generation, Acquisition, and Processing
- Image-based Modeling and Algorithms
- Mathematical Morphology
- Image Geometry and Multi-view Geometry
- 3D Imaging
- Novel Noise Reduction Algorithms
- Image Restoration
- Enhancement Techniques
- Segmentation Techniques
- Motion and Tracking Algorithms and Applications
- Watermarking Methods and Protection + Wavelet Methods
- Image Data Structures and Databases
- Image Compression, Coding, and Encryption
- Video Analysis
- Multi-resolution Imaging Techniques
- Performance Analysis and Evaluation
- Multimedia Systems and Applications
- Novel Image Processing Applications

    • COMPUTER VISION:

(Addresses mid- to high-level processing as well as vision fundamentals.) 

- Camera Networks and Vision
- Sensors and Early Vision
- Machine Learning Technologies for Vision
- Image Feature Extraction
- Cognitive and Biologically Inspired Vision
- Object Recognition
- Soft Computing Methods in Image Processing and Vision
- Stereo Vision
- Active and Robot Vision
- Face and Gesture Recognition
- Fuzzy and Neural Techniques in Vision
- Medical Image Processing and Analysis
- Novel Document Image Understanding Techniques
- Special-purpose Machine Architectures for Vision
- Biometric Authentication
- Novel Vision Application and Case Studies

    • PATTERN RECOGNITION:

(Addresses pattern recognition algorithms and methodologies that are of value to the image processing and computer vision research communities.) 

- Supervised and Un-supervised Classification Algorithms
- Clustering Techniques
- Dimensionality Reduction Methods in Pattern Recognition
- Symbolic Learning
- Ensemble Learning Algorithms
- Parsing Algorithms
- Bayesian Methods in Pattern Recognition and Matching
- Statistical Pattern Recognition
- Invariance in Pattern Recognition
- Knowledge-based Recognition
- Structural and Syntactic Pattern Recognition
- Applications Including: Security, Medicine, Robotic, GIS, Remote Sensing, Industrial Inspection, Nondestructive Evaluation (or NDE), ... 
- Case studies and Emerging technologies

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