Fraser MacfarlaneJames Hutton Institute · Information and Computational Science
Fraser Macfarlane
Doctor of Philosophy
Developing Machine Learning architectures for plant phenotyping, robotics, diagnostics, and remote sensing.
About
7
Publications
493
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23
Citations
Introduction
I am currently a PhD student at the University of Strathclyde having graduated with a First-Class Honours degree in Electronic and Electrical Engineering from the same institution.
My PhD involves improving the quality of long range sensing algorithms for object detection.
During my studies I developed a keen interest and expertise in digital signal processing, in particular in the fields of digital image and video processing and computer vision and have undertaken multiple projects in these areas.
The scope of these projects ranged from exploring uses for hyperspectral imaging in the biomedical industry to identifying and diagnosing faults for the nuclear energy industry. This was achieved through implementing and utilising a variety of imaging techniques and algorithms.
Additional affiliations
October 2017 - present
October 2017 - present
June 2017 - October 2017
Education
September 2013 - May 2017
Publications
Publications (7)
Segment Anything Model (SAM) is a new foundation model that can be used as a zero-shot object segmentation method with the use of either guide prompts such as bounding boxes, polygons, or points. Alternatively, additional post processing steps can be used to identify objects of interest after segmenting everything in an image. Here we present a met...
The extension of Mathematical Morphology to colour and multivariate images is challenging due to the need to define a total ordering in the colour space. No one general way of ordering multivariate data exists and, therefore, there is no single, definitive way of performing morphological operations on colour images. In this paper, we propose an ext...
Target detection and classification is an important application of hyperspectral imaging in remote sensing. A wide range of algorithms for target detection in hyperspectral images have been developed in the last few decades. Given the nature of hyperspectral images, they exhibit large quantities of redundant information and are therefore compressib...
Hyperspectral imaging for agricultural applications provides a solution for non-destructive, large-area crop monitoring. However, current products are bulky and expensive due to complicated optics and electronics. A linear variable filter was developed for implementation into a prototype hyperspectral imaging camera that demonstrates good spectral...
High resolution aerial and satellite borne hyperspectral imagery provides a wealth of information about an imaged scene allowing for many earth observation applications to be investigated. Such applications include geological exploration, soil characterisation, land usage, change monitoring as well as military applications such as anomaly and targe...
The Hit-or-Miss Transform (HMT) is a common tool in Mathematical Morphology (MM) used in template matching and object detection and subsequent classification applications. The HMT probes a query image with a pair of structuring elements (SEs) which are designed to detect specific objects of interest. The relative size of hyperspectral image data in...
The Hit-or-Miss Transform (HMT) is a powerful morphological operation that can be utilised in many digital image analysis problems. Its original binary definition and its extension to grey-level images have seen it applied to various template matching and object detection tasks. However, further extending the transform to incorporate colour or mult...
