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Flexible Shape Models for Marine Animal Detection in Underwater Images

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Kelsall, A (2005) Flexible Shape Models for Marine Animal Detection in Underwater Images. Honours thesis, University of Tasmania. (Unpublished)

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Abstract

Many industries are benefiting from computer automation, however the area of image analysis is still limited. The process of finding a potential object in an image is hard in itself, let alone classifying it. Automating these tasks would significantly reduce the time it takes to complete them thus allowing much more data to be processed. This becomes a problem when data is collect faster than it can be analysed. Images and video sequences are captured for different purposes and need to be manually processed in order to discover their contents. The fishing industry is a perfect example of this. A fish farm needs to know the average size of the fish in a ring. At present, this involves either manually taking a sample of fish from the ring and measuring them, or taking a series of stereoscopic images and manually tracing a sample of fish. By using active shape models, the process of tracing a fish sample can be automated. The Active Shape Model (ASM) Toolkit is an implementation of active appearance models, an advanced type of active shape model. The wrapper application that was written as part of this research allows a more streamlined process to input region data into the ASM Toolkit for searching. Once a sample has been matched, it is possible to use the key points around it to base further calculations on such as its size and weight. The ASM Toolkit and the wrapper program demonstrate how the process of identifying a fish in an image can be automated and that it is possible to calculate the size and weight of fish. In an ideal manual test, the most effective model matched 68% of samples, and in the automated test matched 50% of the samples. If the program can run over several days collecting appropriate samples, the model will be able to match enough fish to estimate the average size and weight within a ring. It is shown that the types of samples used in training the model affects the performance more than the number of samples used.

Item Type: Other
Keywords: Image analysis, fish farm, fish sizing, fish models
Publisher: Honours thesis, University of Tasmania
Date Deposited: 07 Jan 2006
Last Modified: 18 Nov 2014 03:10
URI: http://eprints.utas.edu.au/id/eprint/248
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