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Title: Automatic fish species grading using image processing and pattern recognition techniques
Author: Strachan, N. J. C.
ISNI:       0000 0001 2412 5770
Awarding Body: University of Aberdeen
Current Institution: University of Aberdeen
Date of Award: 1990
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Size and species grading of fish (eg on board a fishing vessel) might in future be done entirely automatically using image analysis and pattern recognition techniques. Three methods of discriminating between pictures of seven different species of fish have been compared: using invariant moments, optimisation of the mismatch, and shape descriptors. A novel method of obtaining the moments of a polygon is described. It was found that the shape descriptors gave the best results with a sorting reliability of 90&'37. Different methods of producing symmetry lines from the shape of fish have been studied in order to describe fish bending and deformations. The simple thinning algorithm was found to work best to provide a reference axis. This axis was then used as a basis for constructing a deformation independent position reference system. Using this reference system position specific colour measurements of fish could be taken. For this to be done the video digitising system was firstly calibrated in the CIELUV colour space using the Macbeth colour chart. Colour and shape measurements were then made on 18 species of demersal and 5 species of pelagic fish. The simple shape measurements of length/width and front area/back area ratios were used to do some introductory separation of the fish. Then the variables produced by the shape descriptors and colour measurements were analysed by discriminant analysis. It was found that all of the demersal fish were sorted correctly (sorting reliability of 100&'37) and all of the pelagic fish were sorted correctly except one (sorting reliability of 98&'37). A prototype machine is now being constructed based on the methods described in this work.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Pattern recognition & image processing Pattern recognition systems Pattern perception Image processing Computer integrated manufacturing systems Aquaculture Fisheries