Gökberk, Berk

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Profile URL
https://hdl.handle.net/20.500.11779/2681
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Email Address
gokberkb@mef.edu.tr
Main Affiliation
02.02. Department of Computer Engineering
Status
Former Staff
Website
ORCID ID
0000-0001-6299-1610
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Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID
DUQ-7696-2022

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Documents

2

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Scholarly Output

6

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1197/2528

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WoS Citation Count

20

Scopus Citation Count

22

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0

WoS Citations per Publication

3.33

Scopus Citations per Publication

3.67

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1

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0

JournalCount
2015 23rd Signal Processing and Communications Applications Conference, SIU 2015 - Proceedings -- 2015 23rd Signal Processing and Communications Applications Conference, SIU 2015 -- 16 May 2015 through 19 May 2015 -- Malatya -- 1130521
2019 Ninth International Conference on Image Processing Theory, Tools and Applications = IPTA 20191
2021 29th Signal Processing and Communications Applications Conference (SIU)1
9th International Conference on Image Processing Theory, Tools and Applications, IPTA 20191
Conference: 23nd Signal Processing and Communications Applications Conference (SIU) Location: Inonu Univ, Malatya, TURKEY Date: MAY 16-19, 20151
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  • Thumbnail Image
    Conference Object
    Yüz Derinlik Imgelerinde Güdümlü İniş Yöntemini Kullanarak Nirengi Noktası Bulma
    (Institute of Electrical and Electronics Engineers Inc., 2015) Akarun, Lale; Camgöz, Necati Cihan; Gökberk, Berk
  • Thumbnail Image
    Conference Object
    Citation - WoS: 8
    Citation - Scopus: 5
    Recognizing Non-Manual Signs in Turkish Sign Language
    (IEEE, 2019) Gökberk, Berk; Akarun, Lale; Aktaş, Müjde
    Recognition of non-manual components in sign language has been a neglected topic, partly due to the absence of annotated non-manual sign datasets. We have collected a dataset of videos with non-manual signs, displaying facial expressions and head movements and prepared frame-level annotations. In this paper, we present the Turkish Sign Language (TSL) non-manual signs dataset and provide a baseline system for non-manual sign recognition. A deep learning based recognition system is proposed, in which the pre-trained ResNet Convolutional Neural Network (CNN) is employed to recognize question, negation side to side and negation up-down, affirmation and pain movements and expressions. Our subject independent method achieves 78.49% overall frame-level accuracy on 483 TSL videos performed by six subjects, who are native TSL signers. Prediction results of consecutive frames are filtered for analyzing the qualitative results.
  • Thumbnail Image
    Conference Object
    Citation - Scopus: 4
    Multi-View Reconstruction of 3d Human Pose With Procrustes Analysis
    (IEEE, 2019) Gökberk, Berk; Akarun, Lale; Temiz, Hüseyin; Gokherk, Berk
    Recovery of 3D human pose from cameras has been the subject of intensive research in the last decade. Algorithms that can estimate the 3D pose from a single image have been developed. At the same time, many camera environments have an array of cameras. In this paper, after aligning the poses obtained from single images using Procrustes Analysis, median filtering is utilized to eliminate outliers to find final reconstructed 3D body joint coordinates. Experiments performed on the CMU Panoptic, and Human3.6M databases demonstrate that the proposed system achieves accurate 3D body joint reconstructions. Additionally, we observe that camera selection is useful to decrease the system complexity while attaining the same level of reconstruction performance.
  • Thumbnail Image
    Conference Object
    Citation - WoS: 4
    Citation - Scopus: 8
    Facial Landmark Localization in Depth Images Using Supervised Descent Method
    (IEEE, 2015) Gökberk, Berk; Akarun, Lale; Camgoz, Necati Cihan
    This paper proposes using the state of the art 2D facial landmark localization method, Supervised Descent Method (SDM), for facial landmark localization in 3D depth images. The proposed method was evaluated on frontal faces with no occlusion from the Bosphorus 3D Face Database. In the experiments, in which 2D features were used to train SDM, the proposed approach achieved state-of-the-art performance for several landmarks over the currently available 3D facial landmark localization methods.
  • Thumbnail Image
    Conference Object
    Citation - WoS: 7
    Citation - Scopus: 3
    Facial Landmark Localization in Depth Images Using Supervised Ridge Descent
    (IEEE, 2015) Camgoz, Necati Cihan; Gökberk, Berk; Akarun, Lale; Struc, Vitomir; Kindiroglu, Ahmet Alp
    Supervised Descent Method (SDM) has proven successful in many computer vision applications such as face alignment, tracking and camera calibration. Recent studies which used SDM, achieved state of the-art performance on facial landmark localization in depth images [4]. In this study, we propose to use ridge regression instead of least squares regression for learning the SDM, and to change feature sizes in each iteration, effectively turning the landmark search into a coarse to fine process. We apply the proposed method to facial landmark localization on the Bosphorus 3D Face Database; using frontal depth images with no occlusion. Experimental results confirm that both ridge regression and using adaptive feature sizes improve the localization accuracy considerably.
  • Thumbnail Image
    Conference Object
    Citation - WoS: 1
    Citation - Scopus: 2
    Turcoins: Turkish Republic Coin Dataset
    (IEEE, 2021) Gökberk, Berk; Akarun, Lale; Temiz, Hüseyin
    In this paper, we present a novel and comprehensive dataset which contains Turkish Republic coins minted since 1924 and present a deep learning based system that can automatically classify coins. The proposed dataset consists of 11080 coin images from 138 different classes. To classify coins, we utilize a pre-trained neural network (ResNet50) which is pre-trained on ImageNet. We train the pre-trained neural networks on our dataset by transfer learning. The imbalanced nature of the dataset causes the classifier to show lower performance in classes with fewer samples. To alleviate the imbalance problem, we propose a StyleGAN2-based augmentation method providing realisticfake coins for rare classes. The dataset will be published in http://turcoins.