“Computer Vision and pattern recognition techniques for biometric authentication”
Diploma Theses for the Academic Year 2021-2022
Available |
Brief Description:
This dissertation aims to use machine learning and computer vision techniques for biometric handwritten signature verification. For more details the interested reader may refer to SV_survey.
Required background knowledge: Linear Algebra, Digital Signal Processing, Digital Image Processing, Matlab, C/C++, Python
Place: ZB203 – Research Laboratory TelSiP
Supervisor: Dr. Elias Zois, Assistant Professor
“Robust Distance Measure for Similarity-Based Classification on the SPD Manifold”
Available |
Brief Description:
This dissertation aims to adapt multi-metric learning techniques to manifolds through covariance matrices. Manifolds are state of the art methodologies applied in machine learning and computer vision applications. For more details the interested reader may refer to Paper_Ghao.
Required background knowledge: Linear Algebra, Digital Signal Processing, Digital Image Processing, Matlab, C/C++, Python
Place: ZB203 – Research Laboratory TelSiP
Supervisor: Dr. Elias Zois, Assistant Professor
“Cloud classification of ground-based infrared images combining manifold and texture features”
Available |
Brief Description:
Automatic cloud type recognition of ground-based infrared images is still a challenging task. A novel cloud classification method is proposed to group images into five cloud types based on manifold and texture features. Compared with statistical features in Euclidean space, manifold features extracted on symmetric positive definite (SPD) matrix space can describe the non-Euclidean geometric characteristics of the infrared image more effectively. The proposed method comprises three stages: pre-processing, feature extraction and classification. The datasets are comprised of the zenithal and whole-sky images taken by the Whole-Sky Infrared Cloud-Measuring System (WSIRCMS).
Required background knowledge: Linear Algebra, Digital Signal Processing, Digital Image Processing, Matlab, C/C++, Python
Place: ZB203 – Research Laboratory TelSiP
Supervisor: Dr. Elias Zois, Assistant Professor
“Image processing and computer vision with sparse representation techniques”
Available |
Brief Description:
This dissertation is focused towards the use of sparse representation algorithms in order to denoise images. Sparse representation and dictionary learning algorithms based on L1 norms shall be studied with the use of the SPAMS toolbox.
Required background knowledge: Linear Algebra, Geometry in Computational Vision, Digital Signal Processing, Digital Image Processing, Matlab, C/C++, Python
Place: ZB203 – Research Laboratory TelSiP
Supervisor: Dr. Elias Zois, Assistant Professor
“Machine Learning Applications using the Keras and TensorFlow libraries”
Available |
Brief Description:
The Keras and TensorFlow libraries are open source software libraries for high-performance numerical calculations. Its flexible architecture allows easy computing development on various platforms (CPUs, GPUs, TPUs) and from desktops to server arrays. Their environment will be studied and attempts will be made to develop computer vision and machine learning applications.
Required background knowledge: Linux, Docker, Python, Keras, TensorFlow
Place: ZB203, A119 – Research Laboratory TelSiP
Supervisors:
Dr. Elias Zois, Assistant Professor
Dr. Grigorios Koulouras, Associate Professor
“ΙοΤ data immutability by using Distributed Ledger Technology (DLT)”
Brief Description:
The security, integrity and immutability of data on the Internet of Things is something that has been of great concern to the research community in recent years. Distributed Ledger Technology (DLT) technology such as Blockchain is coming to fill this gap. In this Thesis will initially be implemented an intelligent system that will collect physical parameters (eg temperature, humidity, levels of air pollution, etc.). An attempt will then be made to write a smart contract, where this data is forwarded to a Blockchain network of the student’s choice.
Required background knowledge: Blockchain, DLT, Consensus mechanisms, Smart Contracts, IoT, Embedded Systems, Sensors, Linux, Docker, MQTT, Python
Place: ZB203 – Research Laboratory TelSiP
Supervisor: Dr. Grigorios Koulouras, Associate Professor
Student: Alkinoos Peratinos