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COMP9074 - Machine Vision

Title:Machine Vision
Long Title:Machine Vision
Module Code:COMP9074
Duration:1 Semester
Credits: 5
NFQ Level:Expert
Field of Study: Computer Science
Valid From: Semester 2 - 2019/20 ( January 2020 )
Module Delivered in 1 programme(s)
Next Review Date: January 2025
Module Coordinator: Sean McSweeney
Module Author: Christian Beder
Module Description: The module will provide a comprehensive overview of the application and implementation of computer vision techniques, which are the foundation of many AI and machine learning applications. The module will focus on three specific areas: the first part will look at basic image capturing and image processing techniques. The second part will focus on the extraction of relevant content from images and videos, while the third part will look into image geometry and estimation techniques for scene reconstruction from image and video data.
Learning Outcomes
On successful completion of this module the learner will be able to:
LO1 Apply computer vision methodologies to facilitate capturing, filtering and pre-processing of image and video data.
LO2 Select and apply appropriate computer vision algorithms to solve real-world problems involving image and video data.
LO3 Implement computer vision algorithms to extract and track relevant features from image and video data.
LO4 Apply projective modelling techniques to implement parameter estimation algorithms for inferring and measuring scene geometry from image and video data.
LO5 Analyse and evaluate the performance of computer vision and photogrammetry algorithms.
Pre-requisite learning
Module Recommendations
This is prior learning (or a practical skill) that is strongly recommended before enrolment in this module. You may enrol in this module if you have not acquired the recommended learning but you will have considerable difficulty in passing (i.e. achieving the learning outcomes of) the module. While the prior learning is expressed as named CIT module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
Incompatible Modules
These are modules which have learning outcomes that are too similar to the learning outcomes of this module. You may not earn additional credit for the same learning and therefore you may not enrol in this module if you have successfully completed any modules in the incompatible list.
No incompatible modules listed
Co-requisite Modules
No Co-requisite modules listed

This is prior learning (or a practical skill) that is mandatory before enrolment in this module is allowed. You may not enrol on this module if you have not acquired the learning specified in this section.

No requirements listed

Module Content & Assessment

Indicative Content
Image processing
Introduction to image capturing methodologies, such as sampling of raster images, colour representation and histogram operations. Image processing algorithms for binary images and morphological operations for image segmentation. Theory of linear filters and the application of convolution filters using Fourier transformation. Application of linear low-pass filtering to smoothing and image pyramid computation. Theory of sampling and its relation to local frequency and aliasing effects with application to super/sub-sampling and anti-aliasing. Application of linear high-pass filtering to edge detection, the computation of image gradients and methods for feature extraction based on local gradients.
Content extraction
Introduction to algorithms for extracting local descriptors of feature points, such as scale-invariant feature transformation (SIFT) and SURF. Algorithms for image segmentation based on mathematical representations such as level-sets and Markov-random-fields. Optimisation techniques for solving image segmentation problems. Photometric stereo algorithms for extracting 3d information from single images, such as shape-from-shading. Image sequence analysis using optical flow algorithms. Feature tracking algorithms for image sequences, such as KLT, to facilitate multi-view scene reconstruction and automatic camera calibration.
Parameter estimation
Theory of statistical parameter estimation. Maximum Likelihood and iterative Least-Squares estimation techniques, such as Gauss-Helmert- and Gauss-Markov-models as well as online estimation methods such as Kalman filters. Robust estimation techniques to cope with outliers in data including robust cost functions and random sampling consensus (RANSAC). Performance analysis techniques like the estimation of empirical parameter accuracy and variance components.
Projective geometry
Introduction to the projective geometry of 2d and 3d space. Geometric modelling of the imaging process using linear projective camera models and forward/backward projection of image point and image line features. Estimation of planar homographies and their application to plane-to-plane image mapping, image registration and stitching of panoramic views from multiple images. Epipolar geometry of two and three projective images. Estimation of the fundamental matrix and the trifocal tensor to describe the un-calibrated multi-view geometry. Application of un-calibrated multi-view geometry to projective 3d scene reconstruction from multiple images. Algorithms and techniques for camera calibration. Estimation of the essential matrix to describe calibrated two-view geometry with applications to metric 3d scene reconstruction. Simultaneous localisation and mapping (SLAM) algorithms using bundle adjustment to recover camera and scene geometry from image sequences. Auto-calibration techniques for recovering metric information from video sequences of un-calibrated images.
Assessment Breakdown%
Course Work100.00%
Course Work
Assessment Type Assessment Description Outcome addressed % of total Assessment Date
Project For a given case-study apply computer vision methodologies and implement algorithms to capture, process, and extract relevant information from image or video data. 1,2,3 50.0 Week 8
Project For a given case-study implement algorithms to extract and measure features from image or video data, estimate scene geometry parameters, and evaluate the accuracy of the results. 3,4,5 50.0 Sem End
No End of Module Formal Examination
Reassessment Requirement
Coursework Only
This module is reassessed solely on the basis of re-submitted coursework. There is no repeat written examination.

The institute reserves the right to alter the nature and timings of assessment


Module Workload

Workload: Full Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture Presentation of theory 2.0 Every Week 2.00
Lab Lab supporting lectures 2.0 Every Week 2.00
Independent Learning Student reads recommended materials and practices implementation 3.0 Every Week 3.00
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 4.00
Workload: Part Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture Presentation of theory 2.0 Every Week 2.00
Lab Lab supporting lectures 2.0 Every Week 2.00
Independent Learning Student reads recommended materials and practices implementation 3.0 Every Week 3.00
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 4.00

Module Resources

Recommended Book Resources
  • McGlone, J. C., Mikhail, E. M., Bethel, J. S., Mullen, R., & American Society for Photogrammetry and Remote Sensing 2004, Manual of photogrammetry, 5th edition Ed., American Society for Photogrammetry and Remote Sensing [ISBN: 1570830711]
Supplementary Book Resources
  • Nikos Paragios, Yunmei Chen, and Olivier Faugeras 2005, Handbook of Mathematical Models in Computer Vision, Springer [ISBN: 1441938850]
  • Berthold Klaus Paul Horn 1986, Robot Vision, MIT Press [ISBN: 0262081598]
  • Richard Hartley and Andrew Zisserman 2004, Multiple View Geometry in Computer Vision, 2nd edition Ed., Cambridge University Press [ISBN: 0521540518]
  • Rafael C. Gonzalez and Richard E. Woods 2017, Digital Image Processing, 4th edition Ed., Pearson [ISBN: 1292223049]
  • Olivier Faugeras, Quang-Tuan Luong, and T. Papadopoulou 2001, The Geometry of Multiple Images: The Laws that Govern the Formation of Images of a Scene and some of their Applications, MIT Press [ISBN: 0262562049]
  • Olivier Faugeras 1993, Three-Dimensional Computer Vision: A Geometric Viewpoint, MIT Press [ISBN: 0262061589]
  • Gerhard Winkler 2012, Image Analysis, Random Fields and Dynamic Monte Carlo Methods: A Mathematical Introduction, 2nd edition Ed., Springer [ISBN: 3642629113]
This module does not have any article/paper resources
Other Resources

Module Delivered in

Programme Code Programme Semester Delivery
CR_KARIN_9 Master of Science in Artificial Intelligence 2 Elective

Cork Institute of Technology
Rossa Avenue, Bishopstown, Cork

Tel: 021-4326100     Fax: 021-4545343
Email: help@cit.edu.ie