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SPIS - Signal Processing and Information Systems Lab.MISAM - Machine Intelligence for Speech Audio and Multimedia.
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VPA Research where Müjdat Çetin is Involved

Research Activities

Model Error Correction in SAR Imaging - We are working on a framework, in which image formation and model error correction in SAR imaging are jointly evaluated.
Facial Expression Recognition - Facial Expression Analysis is an interdisciplinary research area that combines related areas such as psychological and behavioral studies and computer vision. The FER system has a number of application areas such as emotion communication, clinical psychology, psychiatry,pain assessment, lie detection and human computer interface.

Research Activities

Dendritic Spine Analysis
- The long-term vision of this research project is to develop efficient and flexible image processing modules that have the ability to learn from and adapt to the provided data, and to share these modules with neuro-scientists. In this respect, the algorithms developed in this project will be included in a Matlab-based toolbox, SpineS, that will have an easy-to-use graphical user interface.

Turkish Academy of Sciences Distinguished Young Scientist Research Project
- The objective of TÜBA-GEBİP is to foster young, outstanding scientists who are at the stage of establishing their own research programmes in Turkey after finishing their post-doctoral research activities.

National Young Researchers Career Development Programme
- The purpose of this programme is to encourage young scientists who hold a PhD degree and who are at the beginning of their scientific career by supplying project funds to their studies.

MR - based Analysis, Indexing, and Retrieval of Brain Iron Deposition in Basal Ganglia
- This multi-disciplinary project targets to significantly improve the understanding of neurodegenerative diseases by developing automatic methods that enable relating the brain iron accumulation to various diseases and complications.
This project involves a multi-disciplinary research requiring novel solutions from medical image processing, pattern recognition, search and retrieval, and clinical science.
This is a joint project with Philips Research Eindhoven.

Development of Electroencephalography (EEG) Signal Analysis Techniques for Brain Computer Interface (BCI) Systems - Electroencephalography (EEG) based Brain-Computer Interface (BCI) systems are a new development in the field of applied neurophysiology. In this project, our objective is to develop new signal analysis and pattern recognition algorithms based on statistical graphical models, and to demonstrate the effectiveness of these algorithms both on standard data sets and on the data collection and BCI system we will set up.

Development of Electroencephalography (EEG) Signal Analysis Techniques for Brain Computer Interface (BCI) Systems
- Development of Electroencephalography (EEG) Signal Analysis Techniques for Brain Computer Interface (BCI) Systems

Slice Matching - The aim is to retrieve relevant slice from a 3D medical image data of a subject given a query image of another subject. Such a solution can aid the medical experts in diagnosing anatomical structure specific diseases, such as basal ganglia or hypocampus disorders.

Patient Search - Patient-to-patient search, which can be defined as comparing multiple patients and retrieving relevant cases among them, should especially help the medical expert in diagnosis of diseases whose causes and progress have not yet been completely unraveled, and diseases that affect large number of patients such as Alzheimer’s and Parkinson’s.

Iron Quantification - Iron accumulation in the brain is a normal process that starts after age 20 and observed in every individual. However, in those developing neurodegenerative diseases deep gray matter structures of the brain accumulate abnormal (larger) amounts of iron. Here, our aim is to analyze and quantify iron deposited in the brain using image analysis techniques.

Communication Constrained Distributed Estimation - We conduct research along subjects related to Collaborative Signal and Information Processing motivated by networked sensing.

Signal Processing and Advanced Information Technologies for improving Driver/Driving Prudence and Accident Reduction
- In this initiative, we propose to create conditions for prudent driving on highways and roadways with the purposes of reducing accidents caused by driver behavior.

Coupled Non-Parametric Shape and Moment-Based Inter-Shape Pose Priors for Multiple Basal Ganglia Structure Segmentation - Brain tissue and structure segmentation in magnetic resonance (MR) images is a fundamental problem in clinical studies of brain structure and function. Due to limitations such as low contrast, partial volume effects, and field inhomogeneities, the delineation of subcortical (basal ganglia) structures such as caudate nucleus, putamen, and thalamus from white matter, gray matter and cerebrospinal fluid (CSF) is a very challenging problem.

New Generation Information Processing Techniques for Imaging Sensors and Wireless Sensor Networks
- The objective of this project is to develop principled and practical new signal and information processing methods for a number of sensing systems including wide-angle radar, magnetic resonance brain imaging, and wireless micro-sensor networks.

Regularized Image Reconstruction - Our research is mostly concentrated on the selection of the regularization parameter when a non-quadratic regularizer is incorporated to the solution.

Machine Learning Systems For Detecting Driver Drowsiness - In this project the development of machine learning systems for drowsy driver detection is proposed.

Stereo Based 3D Head Pose Tracking - In this project a new stereo-based 3D head tracking technique, based on scale-invariant feature transform (SIFT) features, that is robust to illumination changes is proposed.

Graphical Model Based Facial Feature Point Tracking - Feature point tracking is a challenging topic in case of arbitrary head movements and uncertain data because of noise and/or occlusions. With this motivation, a graphical model that incorporates not only temporal information about feature point movements, but also information about the spatial relationships between such points is built.

Segmentation - In the framework of IronDB project, we provide results of brain Basal Ganglia organs segmentations, such as Caudate Nucleus and Putamen.

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