Erdem Akagündüz

E-mail

akaerdem@metu.edu.tr

Department

Graduate School of Informatics

ORCID

0000-0002-0792-7306

Scopus Author ID

8331988500

Web of Science Researcher ID

W-1788-2018

Detecting Driver Drowsiness as an Anomaly Using LSTM Autoencoders Tüfekci, Gülin; Kayabaşı, Alper; Akagündüz, Erdem; Ulusoy, İlkay (2023-01-01) In this paper, an LSTM autoencoder-based architecture is utilized for drowsiness detection with ResNet-34 as feature extractor. The problem is considered as anomaly detection for a single subject; therefore, only the norma...
A survey on infrared image & video sets Danaci, Kevser Irem; Akagündüz, Erdem (2023-01-01) In this survey, we compile a list of publicly available infrared image and video sets for artificial intelligence and computer vision researchers. We mainly focus on IR image and video sets, which are collected and labelle...
Semantic Segmentation of Crop Areas in Remote Sensing Imagery using Spectral Indices and Multiple Channels ÜLKÜ, İREM; Akagündüz, Erdem (2023-01-01) This study focuses on pixel-wise semantic segmentation of crop production regions by using satellite remote sensing multispectral imagery. One of the principal aims of the study is to find out whether the raw multiple chan...
Augmentation of Atmospheric Turbulence Effects on Thermal Adapted Object Detection Models Uzun, Engin; Dursun, Ahmet Anil; Akagündüz, Erdem (2022-06-01)
A Survey on Deep Learning-based Architectures for Semantic Segmentation on 2D Images Ülkü, İrem; Akagündüz, Erdem (2022-02-01) Semantic segmentation is the pixel-wise labeling of an image. Boosted by the extraordinary ability of convolutional neural networks (CNN) in creating semantic, high-level and hierarchical image features; several deep learn...
Deep Semantic Segmentation of Trees Using Multispectral Images Ulku, Irem; Akagündüz, Erdem; Ghamisi, Pedram (2022-01-01) Forests can be efficiently monitored by automatic semantic segmentation of trees using satellite and/or aerial images. Still, several challenges can make the problem difficult, including the varying spectral signature of d...
Dynamical system parameter identification using deep recurrent cell networks: Which gated recurrent unit and when? Cifdaloz, Oguzhan; Akagündüz, Erdem (2021-01-01) In this paper, we investigate the parameter identification problem in dynamical systems through a deep learning approach. Focusing mainly on second-order, linear time-invariant dynamical systems, the topic of damping facto...
Defining Image Memorability Using the Visual Memory Schema Akagündüz, Erdem; Evans, Karla K. (2020-09-01) Memorability of an image is a characteristic determined by the human observers' ability to remember images they have seen. Yet recent work on image memorability defines it as an intrinsic property that can be obtained inde...
A Hybrid Framework for Matching Printing Design Files to Product Photos Kaplan, Alper; Akagündüz, Erdem (2020-04-01)
Representing Earthquake Accelerogram Records for CNN Utilization Cikis, Melis; Tileyoglu, Salih; Akagündüz, Erdem (2020-01-01) In this study, a spectrogram based false color representation of earthquake accelergrams is proposed and its usability for both human investigation and its application in convolutional networks are discussed. By using more...

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