Certificate of Achievement
has completed the following course:
Introduction to Image Analysis for Plant Phenotyping
The University of Nottingham and University of Lincoln
On this course, we looked at some ways to approach plant phenotyping from images, and how to think about automating some of these approaches. This included starting to write code to collect data from images. In addition, we looked at image capture itself, and the data that we often work with in image analysis. Some basic principles of image processing and analysis were introduced. We also looked at some more advanced scenarios, such as 3D and hyperspectral imaging.
6 weeks, 3 hours per week
Andrew French
Professor of Computer Science, Computer Vision Lab, Computer Science,
The University of Nottingham
Tony Pridmore
Professor of Computer Science, Computer Vision Lab, Computer Science,
The University of Nottingham
Transcript
Learning outcomes
- Explain what image analysis is, and how it relates to plant phenotyping
- Explore the kind of data found in a digital images
- Practice using software tools which can be used in image analysis
- Code some simple tasks to access data in images
- Describe some common tasks in image analysis, such as noise removal, labelling of objects and counting of items
- Investigate some more advanced techniques, such as 3D data and hyperspectral image analysis concepts
Syllabus
- What is image analysis and what are digital images?
- Typical image analysis application in plant phenotyping
- Image data and formats
- Capturing quality digital images
- Software tools for image analysis
- Working with images in Fiji (ImageJ)
- Coding for image analysis in Python
- Example Python packages e.g. OpenCV
- Common tasks in image analysis
- Noise removal and filtering
- Counting and labelling
- Model-based and feature-based segmentation
- Beyond 2D image data
- Motion in image analysis (video or time sequenced data)
- Volumetric data (3D data)
- Hyperspectral image data
- 3D reconstruction
- Example applications
- An overview of 3D reconstruction approaches
- Conclusions and next steps
Issued on 28th August 2022
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