Module Information
Course Delivery
| Delivery Type | Delivery length / details |
|---|---|
| Lecture | 30 x 1 hour lecture |
Assessment
| Assessment Type | Assessment length / details | Proportion |
|---|---|---|
| Semester Exam | 2 Hours Written exam | 70% |
| Semester Assessment | Presentation of a selected topic covered. Partial peer assessment. | 30% |
| Supplementary Assessment | Resubmission of failed components (or equivalent). | 100% |
Learning Outcomes
On successful completion of this module students should be able to:
Demonstrate and understanding of the fundamental techniques for 3D data acquisition and analysis.
Show the knowledge of 3D data capture and extraction of information from 3D data.
Demonstrate awareness of relevant literature on 3D imaging and analysis.
Demonstrate capability to communicate concepts and ideas within 3D data processing.
Brief description
This module will equip students with the fundamental concepts and technniques for 3D data acquisition and analysis mainly through lecture delivery, programme demonstration and laboratory practicals. It will then help the students gain clear understanding of the interactions between these two components and inspiration to develop novel hardware and software for 3D data acquisition and processing in the real world.
Content
1. Introduction
Necessity and possibility to capture 3D data, module assessment, reading list.
2. Fundamental mathematics.
Vector, matrix, eigenvalue and eigenvector, singular value decomposition, least squares, non-linear squares, projective geometry.
3. 3D programming concepts.
Introduction, scene graph, geometry creation (eg. using Java3D, OpenGL, etc).
4. Camera calibration and correction.
Camera modeling, distortion modeling, rig based method, projective geometry based method.
5. Triangulation based data acquisition and stereo vision.
Configurations, principles, issues.
6. Time of flight based data acquisition.
Configurations, principles, issues.
7. Motion capture.
Configurations, principles, issues.
8. 3D data visualisation.
Points, mesh, volumetric, saliency, simplification.
9. Segmentation and clustering.
Region growing, minimum spanning tree, K-means, hierarchical, consensus clustering techniques.
10. 3D data matching.
Point combination based, feature based, population based, local and global registration.
11. 3D data integration.
Point based method, mesh based method volumetric method.
12. Object classification and recognition.
Representation, local and global feature based matching, content-based image retrieval.
Module Skills
| Skills Type | Skills details |
|---|---|
| Application of Number | Inherent to subject. |
| Communication | Class discussion, presentation. |
| Improving own Learning and Performance | Inherent to subject. |
| Information Technology | Inherent to subject. |
| Personal Development and Career planning | Encourages students to see roles in subject for career and personal development. |
| Problem solving | Inherent to subject |
| Research skills | Presentation, after class reading. |
| Subject Specific Skills | Digital data acquisition and processing about the real world. |
| Team work |
Reading List
Essential ReadingCyganek, B.[Boguslaw], Siebert, J.P.[J. Paul], (March 2009) An Introduction to 3D Computer Vision Techniques and Algorithms Wiley Primo search Nick Pears, Yonghuai Liu, Pete Bunting (editors) (2012) 3D Imaging, Analysis and Applications (text book) Springer Primo search Supplementary Text
O. Faugeras (1993) Three-Dimensional Computer Vision MIT Press Primo search
Notes
This module is at CQFW Level 7