Uma Mahesh R. N. is an Associate Professor in the Department of CSE (AI and ML) at ATME College of Engineering, Mysore, India. He earned his master’s degree in VLSI Design and Embedded Systems and his bachelor’s degree in Electronics and Communication Engineering from Visvesvaraya Technological University. He qualified for the UGC-NET examination in December 2019 and pursued research at VIT Chennai. His research interests include digital holography, artificial intelligence, and machine learning.

    Deep CNN-Based Binary Classification of 3-D Objects Using Phase-Only Digital Holographic Information

Abstract:  A deep CNN-based binary classification of three-dimensional (3-D) objects for phase-only digital holographic information has been presented. The 3-D objects considered for the binary classification task are ‘triangle-square’, ‘circle-square’, ‘square-triangle’, and ‘triangle-circle’. The 3-D object ‘triangle-square’ is considered for the TRUE class and the remaining 3-D objects ‘circle-square’, ‘square-circle’, and ‘triangle-circle’ are considered for the FALSE class. The 3-D object volume ‘triangle-square’ was constructed in such a way that the feature triangle was considered in the first plane and the feature square was considered in the second plane. Each plane is separated by various distances d1d_1, and d2d_2 respectively. The remaining three 3-D objects were constructed similarly except that the different features were considered in the first and second planes respectively. The digital holograms of 3-D objects have been formed using the two-step phase-shifting digital holography (PSDH) technique and computationally post-processed to obtain phase-only digital holographic data. The phase-only image dataset was prepared by performing a rotation of 0.5∘0.5^\circ on each phase image. Then the training of the deep CNN was performed on a phase-only image dataset consisting of 2880 images to produce the results. The results such as the loss and accuracy curves, confusion matrix, Receiver Operating Characteristic (ROC), and precision-recall characteristic are shown for the confirmation of the work. The classification of phase images implies the classification of 3-D objects using deep CNN.