Principal Component Analysis for the Approximation of a Fruit as an Ellipse

Principal component analysis (PCA) has been widely used in fields such as computer vision and image processing since its inception. Conceptually it describes the shape of a cluster of data starting with the direction in which there is most data variability. We investigate the novel idea of fitting an encapsulating ellipse to an image of a hypothetically ellipsoidal fruit using principal component analysis. This is achieved by representing the fruit as a distribution of data points in 2-D space, identifying the principal components and the variance of the data along the principal components, and determining the ellipse parameters using them.