Module Database Search
| MODULE DESCRIPTOR | |||
|---|---|---|---|
| Module Title | |||
| Machine Vision | |||
| Reference | CM4610 | Version | 1 |
| Created | February 2024 | SCQF Level | SCQF 10 |
| Approved | April 2024 | SCQF Points | 15 |
| Amended | ECTS Points | 7.5 | |
| Aims of Module | |||
|---|---|---|---|
| To develop understanding of specific concepts related to image processing, computer vision and machine learning along with associated methods and algorithms with emphasis on building practical and real-time machine vision systems. | |||
| Learning Outcomes for Module | |
|---|---|
| On completion of this module, students are expected to be able to: | |
| 1 | Examine a range of image processing and concepts underlying image analysis and features extraction. |
| 2 | Illustrate concepts of Machine Learning, image similarity matching and multi-resolution image analysis. |
| 3 | Execute computer vision algorithms to facilitate capturing, filtering and pre-processing of image and video data. |
| 4 | Execute knowledge to develop intelligent machine vision systems, with results in written and oral form. |
| Indicative Module Content |
|---|
| Image formation and representation, image enhancement techniques- Contrast stretching, histogram equalization, convolution, image smoothing and sharpening, edge detection- Sobel, Canny, Laplacian, Laplacian of Gaussian (LoG), image segmentation, contours, Fourier transform based image processing, Low pass and high pass filters, Morphological image processing- Erosion, dealation, opening, closing, Hit or miss, template matching, image similarity measures, neural networks, NN based image processing, Convolutional neural networks (CNN), Vision transformers, object detection R-CNN, YOLO, generative models GAN. |
| Module Delivery |
|---|
| Key concepts are introduced and illustrated through the medium of lecture. Tutorials assist with assimilation and understanding of material and the laboratory sessions offer appropriate tools and programming environments to develop proficiency in applying the techniques in practical situations. |
| Indicative Student Workload | Full Time | Part Time |
|---|---|---|
| Contact Hours | 48 | N/A |
| Non-Contact Hours | 102 | N/A |
| Placement/Work-Based Learning Experience [Notional] Hours | N/A | N/A |
| TOTAL | 150 | N/A |
| Actual Placement hours for professional, statutory or regulatory body |
| ASSESSMENT PLAN | |||||
|---|---|---|---|---|---|
| If a major/minor model is used and box is ticked, % weightings below are indicative only. | |||||
| Component 1 | |||||
| Type: | Coursework | Weighting: | 100% | Outcomes Assessed: | 1, 2, 3, 4 |
| Description: | Individual Coursework covering all learning outcomes. | ||||
| MODULE PERFORMANCE DESCRIPTOR | |
|---|---|
| Explanatory Text | |
| The calculation of the overall grade for this module is based on 100% weighting of C1. An overall minimum grade of D is required to pass the module. | |
| Module Grade | Minimum Requirements to achieve Module Grade: |
| A | The student needs to achieve an A in C1. |
| B | The student needs to achieve a B in C1. |
| C | The student needs to achieve a C in C1. |
| D | The student needs to achieve a D in C1. |
| E | The student needs to achieve an E in C1. |
| F | The student needs to achieve an F in C1. |
| NS | Non-submission of work by published deadline or non-attendance for examination |
| Module Requirements | |
|---|---|
| Prerequisites for Module | CM2602, CM2604, and CM3604 or equivalents. |
| Corequisites for module | None. |
| Precluded Modules | None. |
| INDICATIVE BIBLIOGRAPHY | |
|---|---|
| 1 | Gonzales, R. et al. 2017. Digital Image Processing. 4th ed. Prentice Hall. |
| 2 | Perez, J. and Pascau, P. 2013. Image Processing and ImagJ. Packt Publishing. |
| 3 | Steger, C. et al. 2018. Machine Vision Algorithms and Applications. 2nd edn. Wiley. |
| 4 | DAVIESE, E. 2017. Computer Vision: Principles, Algorithms, Applications, Learning. 5th ed, Academic Press. |