Tuesday, 30 April 2013

Skeleton of my thesis

I've come across some road blocks around the way when it comes to training a classifier, so that part of the work is on standby right now.

Meanwhile I've been drawing the skeleton of my thesis, and this is my first draft.

1 – Introduction

Introducing the problem, motivations and objectives of the present work. 

1.1 – Problems

Problems normally associated with visual human detection.

1.2 – State of the Art

Brief explanation of some methods already developed for human detection, possibly referring to any real application that might already be implemented (not sure if any). Also a brief overview of the evolution of visual object detection algorithms in general.

1.3 – Solution

State my approach to solve the problem and why it was chosen, rather than any other.

2 – Experimental setup

Detailed explanation of the experimental platform implemented in ROS for the development of the present work. Also stating and explaining the main software tools used for elaborating the code (openCV). Possibly bring out that this application is to be implemented in the ATLAScar thus ilustrating the setup in run-time. This chapter will probably be divided in sub-topics.

3 – Integral Channel Features

A compact explanation of the algorithm.

3.1 – Channels

What is a channel of an image, which were computed and how

3.2 – Integral Images

What an integral image is, what they are for, how they are computed, why they are useful for this work.

3.3 – Features

What is a feature, how they are computed, how many and why. Ilustration of the random mechanism constructed for obtaining random parameters for feature harvesting.

3.4 – “The whole picture” (not sure of the name yet, but seems to me an important sub-topic)

An explanation of the architecture of the code, meaning, how the image is being treated, probably a fluxogram of some sort will come in handy.

4 – Machine Learning Method

Brief explanation of what a ML method is, why it is absolutely necessary for these detection problems.

4.1 – Adaboost

What is adaboost, why is it ideal for the present work

4.2 – Training a classifier

Explain all the steps necessary for successfully training a classifier.

5 – Experiments and Results

Explain how the results were acquired, and what makes this method a valid confirmation of the results.

5.1 – Results

Show results.

6 – Conclusions and Future Work

The title explains it self.

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