deletions | additions       diff --git a/subsection_Measuring_the_Infestation_Level__.tex b/subsection_Measuring_the_Infestation_Level__.tex  index 510776e..2694048 100644  --- a/subsection_Measuring_the_Infestation_Level__.tex  +++ b/subsection_Measuring_the_Infestation_Level__.tex 
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\subsection{Measuring the Infestation Level}  Measuring the infestation level of the pods required three major steps: (Step 1) segmentation of healthy and infected parts; (Step 2) classification of the clusters as either healthy or infected; and (Step 3) providing an infestation index.  The first step involved the segmentation of the infected and non-infected parts of the pods. We adopted Dubey's \cite{dubey2013infected} solution of using K-Means algorithm for a simple and straightforward image segmentation. Following their work, we converted the image from the RGB color space to the L*a*b* color space. Commission Internationale d’Eclairage (CIE) designed the L*a*b* color space to match how humans perceive differences in color and luminance \cite{szeliski2010computer}, thus making it a good color space for computing distances. It is composed of a luminosity or lightness dimension (L*) and two chromaticity or color dimension (a*b*). Isolating the color information to two dimensions (in L*a*b*) makes it computationally more efficient than having the color information spread to three dimensions (in RGB) \cite{dubey2013infected}.   The pixels are then clustered in the a*b* space using the K-Means algorithm. The K-Means algorithm starts by randomly selecting $k$ pixels as the initial centroids for the clusters. $K$ is a user defined parameter that sets the number of clusters to be formed. The centroids represent the clusters and is used to determine which pixels belong to it. The rest of the algorithm is an iterative process and proceeds as follows: (Step 1) Assign all the pixels to the cluster with the centroid closest to them. We used the Euclidean distance in computing for the similarity -- the lesser the distance the more similar they are. (Step 2) Compute for the new centroids of each cluster by getting the mean of all the pixels within that cluster. (Step 3) Repeat Steps 1 and 2 until the clusters do not change anymore.