The work presented in this thesis is based on a statistical representation of shape known as a pairwise geometric histogram which has been demonstrated by other researchers in 2-dimensional object recognition tasks.
This thesis also developed an automatic detection and recognition framework for badger monitoring for the first time. Badger plays a key role in the transmission of bovine tuberculosis, which is described by government as the most pressing animal health problem in the UK.This dissertation is about in-situ object recognition, meaning that specific objects (instances) can be learned from a few training examples that depict them within the place where such objects are commonly present or being used.To enhance the existing models of object recognition and to overcome the above-mentioned issues, three major contributions are made that can be summarised as the followings 1. building a hierarchical model within an abstract architecture that achieves good performances in challenging image object datasets; 2. investigating the contribution for each region of vision for object and scene images.
Selective visual attention provides an effective mechanism to serialize perception of complex scenes in both biological and machine vision systems. In extension of previous models of saliency-based visual attention by Koch and Ullman (Human Neurobiology, 4:219-227, 1985) and Itti et al. (IEEE PAMI, 20(11):1254-1259, 1998), we have developed a new model of bottom-up salient region selection.
Razeghi, Orod (2015) An investigation of a human in the loop approach to object recognition. PhD thesis, University of Nottingham. Access from the University of Nottingham repository.
The thesis proposes the use of a statistical framework for the recognition of objects. Statistics allows to incorporate information into the recognition process such as a priori information, context information and information provided by different sensors.
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The new model family introduced in this thesis is summarized under the term Recursive Deep Learning. The models in this family are variations and extensions of unsupervised and supervised recursive neural networks (RNNs) which generalize deep and feature learning ideas to hierarchical structures. The RNN models of this thesis.
The aim of this thesis is to propose a modular vision system for automatic 2D object recognition, to determine its performance (with respect to accuracy, robustness, and efficiency), and to compare its performance to the performance of the neocognitron.
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This thesis contains research into incidence geometry applied to non-rigid object recognition. The first part of this work measures correlation accuracy to recover angular displacements.
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