Classification of Thermal Tomographic Images using Eigenface Method

Our current work proposes an experimental method that captures the thermal tomographic images under a heat flow process carried out for various boundary temperatures in a volumetric space. Thermal images generated thus experimentally, are then analysed using image processing technique for performance evaluation of heating quality (e.g. Uniform, Non-uniform etc.) In pre-processing stage, the images are converted into grey-scale images containing spatial pixel intensities. The pre-processed greyscale images are classified for quality of heating. This stage includes the normalisation, determination of Eigenface, training and testing. This Eigenface yields the feature value used for comparison with that of different classes like images for uniform and non-uniform heating etc. In Eigenface technique, the space of images is projected onto a low dimensional space using Principal Component Analysis. Eigenface is used to calculate pixel proximities between images. The likeness for the similarity of the test image as captured are found out after calculating the Euclidean Distance and PCA based Similarity Factor between the known Eigenfaces and test Eigenface.