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Daniel Stanley Tan edited subsection_Infected_Part_Segmentation__.tex
over 8 years ago
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\subsection{Infected Part Segmentation}
They converted the image first 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. The centroids represent the clusters and $k$ is a user defined parameter that sets the number of clusters to be formed. 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 nearest to them. This study used the Euclidean distance as their distance function. (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.
After clustering, each of the pixels are labeled based on their corresponding cluster. The image is then segmented based on their labels. The general idea of their work is that the infected part of the fruit would be similar in color and will tend to be in a separate cluster from the healthy part of the fruit. This approach is fast, simple, and straightforward. However, clusters containing the infected pixels have to be manually identified.
