3 research outputs found
An analysis of the feasibility of using image processing to estimate the live weight of sheep
One of the difficulties in successfully managing the supply and use of animal feed in sheep farming is in knowing the live weight of the sheep in the various mobs a farmer may be using. Most farmers make intuitive estimates of whether their sheep are increasing, maintaining or losing weight. A few farmers will weigh samples from the mobs, but this is an expensive
and tedious operation, and consequently not carried out very often. If an inexpensive and
simple method could be devised for quickly obtaining the average live weight of a mob of
sheep this would markedly aid their successful management. This discussion paper contains outlines of the various methods that might be used as well as the problems with each method. There are also discussions covering the efforts made, as explained in the literature, for use in estimating the live weight of other species. This provides a means of generating ideas. The discussion paper concludes with recommendations on what appear to be the most promising approaches that might be further investigated. If such a system could be devised there is no
doubt many farmers around the world would utilise it to assist in the management of their feed supplies, and consequently improve the efficient production of meat and wool
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Weightless neural networks for face recognition
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The interface with the real-world has proved to be extremely challenging throughout the past 70 years in which computer technology has been developing. The problem initially is assumed to be somewhat trivial, as humans are exceptionally skilled at interpreting real-world data, for example pictures and sounds. Traditional analytical methods have so far not provided the complete answer to what will be termed pattern recognition.
Biological inspiration has motivated pattern recognition researchers since the early days of the subject, and the idea of a neural network which has self-evolving properties has always been seen to be a potential solution to this endeavour. Unlike the development of computer technology in which successive generations of improved devices have been developed, the neural network approach has been less successful, with major setbacks occurring in its development. However, the fact that natural processing in animals and humans is a voltage-based process, devoid of software, and self-evolving, provides an on-going motivation for pattern recognition in artificial neural networks. This thesis addresses the application of weightless neural networks using a ranking pre-processor to implement general pattern recognition with specific reference to face processing. The evaluation of the system will be carried out on open source databases in order to obtain a direct comparison of the efficacy of the method, in particular considerable use will be made of the MIT-CBCL face database. The methodology is cost effective in both software and hardware forms, offers real-time video processing, and can be implemented on all computer platforms. The results of this research show significant improvements over published results, and provide a viable commercial methodology for general pattern recognition