Machine Learning Methods for Autonomous Object Recognition.

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.

Learning Feature Hierarchies for Object Recognition.

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.


Object-Place-Context Recognition as an Animal Model of Episodic Memory Impairment in Schizophrenia ROMAIN LE COZANNET, MSc. Thesis submitted to the University of Nottingham for the degree of Doctor of Philosophy JUNE 2010.A method is presented to learn object class models from unlabeled and unsegmented cluttered scenes for the purpose of visual object recognition. The variability across a class of objects is modeled in a principled way, treating objects as flexible constellations of rigid parts (features). Variability is represented by a joint probability density function (pdf) on the shape of the constellation.

Object Recognition Phd Thesis

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.

Object Recognition Phd Thesis

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.

Object Recognition Phd Thesis

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.

Object Recognition Phd Thesis

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Object Recognition Phd Thesis

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Deep In-Situ Learning for Object Recognition - University.

Object Recognition Phd Thesis

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.

Object Recognition Phd 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.

Object Recognition Phd Thesis

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Object Recognition Phd Thesis

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Object Recognition Phd Thesis

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Newcastle University eTheses. - theses.ncl.ac.uk.

Object Recognition Phd Thesis

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Object Recognition Phd Thesis

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.

Object Recognition Phd Thesis

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