Scale-Invariant Feature Transform (SIFT)

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Table of contents

Contents
List of Figures
Nomenclature
Executive Summary
1 Introduction
1.1 Motivation
1.2 Aims
1.3 Main Contributions of this Work
1.4 Relevant Publications
1.5 Structure of this Thesis
2 Literature Review
2.1 Introduction to Computer Vision
2.2 Neuromorphic Imaging Devices
2.2.1 Neuromorphic Engineering
2.2.2 Address Event Representation (AER)
2.2.3 Silicon Retinas
2.2.4 Asynchronous Time-based Imaging Device (ATIS)
2.3 Feature Detection
2.3.1 Shape-based and Contour-based Features
2.3.2 Scale-Invariant Feature Transform (SIFT)
2.3.3 Histograms of Oriented Gradients (HOG)
2.4 Neuromorphic Approaches
2.4.1 Event-Based Visual Algorithms
2.4.2 Neuromorphic Hardware Systems
2.4.3 Spiking Neural Networks
2.5 Linear Solutions to Higher Dimensional Interlayers (LSHDI)
2.5.1 Structure of an LSHDI Network
2.5.2 The Online Pseudoinverse Update Method
2.5.3 The Synaptic Kernel Inverse Method
3 Event-Based Feature Detection
3.1 Introduction
3.2 Contributions
3.3 Feature Detection using Surfaces of Time
3.3.1 Surfaces of Time
3.3.2 Feature Detection on Time Surfaces
3.3.3 Time Surface Descriptors
3.4 Feature Detection using Orientated Histograms
3.4.1 Introduction to Circular Statistics
3.4.2 Mixture Models of Circular Distributions
3.4.3 Noise Filtering through Circular Statistics
3.4.4 Feature Selection using Mixture Models
3.5 Discussion
4 Event-Based Object Classication
4.1 Introduction
4.2 Contributions
4.3 Spiking Neuromorphic Datasets
4.3.1 MNIST and Caltech101 Datasets
4.3.2 Existing Neuromorphic Datasets
4.3.3 Conversion Methodology
4.3.4 Conclusions
4.4 Object Classication using the N-MNIST Dataset
4.4.1 Classication Methodology
4.4.2 Digit Classication using SKIM
4.4.3 Error Analysis of the SKIM network Result
4.4.4 Output Determination in Multi-Class SKIM Problems
4.4.5 Analysis of Training Patterns for SKIM
4.4.6 Conclusions
4.5 Object Classication on the N-Caltech101 Dataset
4.5.1 Classication Methodology
4.5.2 Handling Non-Uniform Inputs
4.5.3 Revising the Binary Classication Problem with SKIM
4.5.4 Object Recognition with SKIM
4.5.5 5-Way Object Classication with SKIM
4.5.6 101-Way Object Classication with SKIM
4.5.7 Conclusions
4.6 Spatial and Temporal Downsampling in Event-Based Visual Tasks
4.6.1 The SpikingMNIST Dataset
4.6.2 Downsampling Methodologies
4.6.3 Downsampling on the N-MNIST Dataset
4.6.4 Downsampling on the SpikingMNIST Dataset
4.6.5 Downsampling on the MNIST Dataset
4.6.6 Downsampling on the N-Caltech101 Dataset
4.6.7 Discussion
5 Object Classication in Feature Space
5.1 Introduction
5.2 Contributions
5.3 Classication using Orientations as Features
5.3.1 Classication Methodology
5.3.2 Classication using the SKIM Network
5.3.3 Classication using an ELM Network
5.3.4 Discussion
5.4 Object Classication using Time Surface Features
5.4.1 Classication Methodology
5.4.2 Adaptive Threshold Clustering on the Time Surfaces
5.4.3 Surface Feature Classication with ELM
5.4.4 Classication using Random Feature Clusters
5.4.5 Surface Feature Classication with SKIM
5.4.6 Discussion
6 Conclusions
6.1 Validation of the Neuromorphic Datasets
6.2 Viability of Event-Based Object Classication
6.3 Applicability of SKIM and OPIUM to Event-Based Classication
6.4 The Importance of Motion in Event-Based Classication
6.5 Future Work
References
Appendix A: Detailed Analysis of N-MNIST and N-
Appendix B: Optimisation Methods for LSHDI Networks
Appendix C: Additional Tables and Figures