Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.760036
Title: A shape-based image classification and identification system for digital holograms of marine particles and plankton
Author: Liu, Zonghua
ISNI:       0000 0004 7432 0533
Awarding Body: University of Aberdeen
Current Institution: University of Aberdeen
Date of Award: 2018
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Abstract:
The objective of this project is to develop a shape-based image analysis system, which allows classification and identification of holographic images of marine particles and plankton recorded by an underwater digital holographic camera. In order to achieve this goal, the first step is to extract shape regions of objects from images and to describe the regions by polygonal boundaries. After extraction of the polygonal boundary curve of an object, affine-invariant curve normalisation is implemented on the curve to reduce the influence of object shape deformations on object identification and classification. Six numeric features are then selected to describe shape properties of an object. Before these six shape features are used as a numeric interpretation of an object for image analysis, some processing of them is necessary, consisting of selecting the number of items in each feature and rescaling the selected feature vectors. Afterwards, Gaussian rescaling is adopted to rescale the feature data. Lastly, a shape-based image classification and identification system is built. The system contains two components: semi-automatic image classification (imCLASS) and automatic image identification (imIDENT). In imCLASS, an image retrieval method based on the support vector machine with a feedback mechanism has been developed. The function of imCLASS is to classify given images into different folders with the corresponding labels from the user. These labelled folders can be used to train the artificial neural network in imIDENT. A set of analyses of effects of the proposed methods in the process chain on image analysis are carried out. The whole performance of the system for classifying and identifying marine particles and plankton is also evaluated in terms of the time-cost and accuracy performance. In the end, some main conclusions are listed. The areas of weakness of the system are also highlighted for future work.
Supervisor: Watson, John ; Allen, Alastair ; Thevar, Thangavel Sponsor: University of Aberdeen
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.760036  DOI: Not available
Keywords: Image processing ; Holography ; Marine sediments ; Suspended sediments ; Plankton
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