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\documentclass[conference,table]{IEEEtran}
\IEEEoverridecommandlockouts
% The preceding line is only needed to identify funding in the first footnote. If that is unneeded, please comment it out.
\usepackage{cite}
\usepackage{amsmath,amssymb,amsfonts}
\usepackage{algorithmic}
\usepackage{array}
\usepackage{gensymb}
\usepackage{graphicx}
\usepackage{multirow}
\usepackage{pgfplots}
\usepackage{tabularx}
\usepackage{textcomp}
\def\BibTeX{{\rm B\kern-.05em{\sc i\kern-.025em b}\kern-.08em
T\kern-.1667em\lower.7ex\hbox{E}\kern-.125emX}}
\begin{document}
\title{Classification of Fruits and Vegetables on a Conveyor}
\author{
\IEEEauthorblockN{Arvind Suresh, Sriram Suresh, Enoch Ramesh}
\IEEEauthorblockA{\textit{University of Waterloo}\\
Waterloo, Canada \\
a7suresh/s9suresh/eramesh@edu.uwaterloo.ca}
}
\maketitle
\begin{abstract}
The aim of this paper is to develop a method to quickly and efficiently identify different types of fruits and vegetables that are travelling on a conveyor. Twenty four different categories of fruits and vegetables, with 80 images per category, are used for training. Segmentation is first performed on the images with two segmentation masks, which are then downsampled using max-pooling to 25\% of the original size. The masked images before downsampling are used with local binary patterns (LBP) and histogram oriented gradients (HOG) feature extraction methods to get the textures and shapes. Principal component analysis (PCA) is performed on the downsampled image and extracted features to reduce the number of principal components but is discarded due to too great a loss in accuracy. Finally, these are fed into the classifier to identify the category of fruit or vegetable. Classification is performed using bagged decision trees. The results show that the proposed classifier has a higher accuracy and faster runtime than Inception.
\end{abstract}
\begin{IEEEkeywords}
Fruit classification, vegetable classification, image classification, image segmentation, max-pooling, feature extraction, local binary patterns (LBP), histogram oriented gradients (HOG), bagging, decision tree
\end{IEEEkeywords}
\section{Introduction}
\input{./subsections/intro}
\section{Related Literature}
\input{./subsections/related_literature}
\section{Data Acquisition} \label{daq}
\input{./subsections/data_acquisition}
\section{Image Preprocessing} \label{preprocessing}
\input{./subsections/image_preprocessing}
\section{Classification} \label{classifiers}
\input{./subsections/classifiers}
\section{Results}
\input{./subsections/results}
\section{Summary and Conclusion}
\input{./subsections/conclusion}
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\end{document}