Three-dimensional distortion-tolerant object recognition using integral imaging

This paper addresses 3D distortion-tolerant object recognition using Integral Imaging (II). II is one of the techniques considered for 3D image recording and display. Multiple elemental images are captured to record different but continuous viewing zones of 3D scenes. We develop a distortion-tolerant 3D object recognition by using the II capture system. We adopt Principal Component Analysis (PCA) and Fisher Linear Discriminant (FLD) classifier followed by the nearest neighbor decision rule and the statistical distance metric. Performance is analyzed in terms of probability of correct decision, Root Mean Square Error (RMSE) between raw input vectors and reconstructed vectors from the PCA subspace and the PCA-FLD cost function. Decision strategies are compared by the varying number of training data.