Gözde Akar

E-mail
bozdagi@metu.edu.tr
Department
Department of Electrical and Electronics Engineering
Scopus Author ID
Web of Science Researcher ID
CHAOS Challenge- combined (CT-MR) healthy abdominal organ segmentation
Kavur, A. Emre; et. al. (2021-04-01)
Segmentation of abdominal organs has been a comprehensive, yet unresolved, research field for many years. In the last decade, intensive developments in deep learning (DL) introduced new state-of-the-art segmentation system...
Exploiting Local Indexing and Deep Feature Confidence Scores for Fast Image-to-Video Search
Ozkan, Savas; Akar, Gözde (2021-01-01)
The cost-effective visual representation and fast query-by-example search are two challenging goals that should be maintained for web-scale visual retrieval tasks on moderate hardware. This paper introduces a fast and robu...
Comparison of semi-automatic and deep learning-based automatic methods for liver segmentation in living liver transplant donors
Kavur, A. Emre; GEZER, NACİYE SİNEM; Baris, Mustafa; Sahin, Yusuf; Ozkan, Savas; Baydar, Bora; Yuksel, Ulas; Kilikcier, Caglar; Olut, Sahin; Akar, Gözde; Ünal, Gözde; DİCLE, OĞUZ; SELVER, MUSTAFA ALPER (AVES Publishing Co., 2020-01-01)
PURPOSE
Hyperspectral data to relative lidar depth: an inverse problem for remote sensing
Akar, Gözde; Özkan, Savaş (IEEE Computer Society; 2019-07-13)
Hyperspectral data provides rich information about a scene in terms of spectral details since it encapsulates measurements/observations from a wide large range of spectrum. To this end, it has been used in different proble...
Effect of Visual Context Information for Super Resolution Problems
Akar, Gözde; Özkan, Savaş; Aykut, Ekin; Cengiz, Baran; Bocek, Kadircan (2019-04-26)
In this study, the effect of visual context information to the performance of learning-based techniques for the super resolution problem is analyzed. Beside the interpretation of the experimental results in detail, its the...
Combination of physics-based and image-based features for landmine identification in ground penetrating radar data
Genc, Alper; Akar, Gözde (SPIE-Intl Soc Optical Eng, 2019-4-23)
Ground penetrating radar (GPR) is a powerful technology for detection and identification of buried explosives, especially with little or no metal content. However, subsurface clutter and soil distortions increase false ala...
Convolutional neural networks analysed via inverse problem theory and sparse representations
Tarhan, Cem; Akar, Gözde (Institution of Engineering and Technology (IET), 2019-04-01)
Inverse problems in imaging such as denoising, deblurring, superresolution have been addressed for many decades. In recent years, convolutional neural networks (CNNs) have been widely used for many inverse problem areas. A...
EndNet: Sparse AutoEncoder Network for Endmember Extraction and Hyperspectral Unmixing
Ozkan, Savas; Kaya, Berk; Akar, Gözde (2019-01-01)
Data acquired from multichannel sensors are a highly valuable asset to interpret the environment for a variety of remote sensing applications. However, low spatial resolution is a critical limitation for previous sensors, ...
Automated Image Processing for Scratch Detection on Specular Surfaces
Okbay, Volkan; Akar, Gözde; Yaman, Ulaş (null; 2018-10-26)
Deep Spectral Convolution Network for Hyperspectral Unmixing
Akar, Gözde; ÖZKAN, savaş (2018-10-10)
In this paper, we propose a novel hyperspectral unmixing technique based on deep spectral convolution networks (DSCN). Particularly, three important contributions are presented throughout this paper. First, fully-connected...
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