Doctoral defence: Kadir Aktas ”Cosmic Ray Tomography based Object Reconstruction and Recognition”

On 31 October at 16:15 Kadir Aktas will defend his doctoral thesis ”Cosmic Ray Tomography based Object Reconstruction and Recognition” for obtaining the degree of Doctor of Philosophy (in Physical Engineering)

Professor Gholamreza Anbarjafari, University of Tartu
Associate Professor Madis Kiisk, University of Tartu
Senior Research Andrea Giammanco, Université catholique de Louvain, Belgium

Professor Ahmet Enis Çetin, University of Illinois Chicago, Chicago, USA


Improvement of the technology and successful research in the artificial intelligence (AI) field have resulted in the surge of using AI-based methods to handle low-level tasks such as object recognition. Object recognition aims to find the objects within an image or sequence of images. The goal is to localize the existing objects within the image and identify the classes of them. Due to its wide application in significant real-world problems, object recognition is one of the main tasks in computer vision. Some examples of these applications include tomography systems, human-behaviour analysis, medical imaging, and sports. Although object recognition has been a hot topic for quite some time, there are still many challenges remaining due to the wide range of application areas. For example, such emerging technologies as cosmic ray tomography have a big research gap for utilizing deep learning techniques to improve the system’s object recognition performance. In order to improve the reconstruction and recognition performance in a cosmic ray tomography system, a neural network based approach is presented in this thesis. A cosmic ray tomography system uses the hit positions of muons on detector plates to reconstruct the images of volume of interest (VOI). Afterwards, these images are used to recognize the objects. Therefore, the recognition performance is directly impacted by the reconstruction performance, hence also by the muon hit position estimation. A significant improvement over the conventional Center of Gravity (CoG) method is obtained by the utilisation of deep neural networks (DNN) for this task. Moreover, in this thesis, object recognition methods based on deep convolutional neural networks (DCNN) are presented. Their capabilities to recognize the objects from a single image and a time-series data are presented. Successful performances are demonstrated through the experimentation with challenging tasks on the diverse datasets.

Defence can be also followed in Zoom: (Meeting ID: 953 058 8152 Passcode: kaitsmine)



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