Türkan, Mehmet
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Turkan, Mehmet
Turkan, M
Türkan, M
Turkan, M
Türkan, M
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Email Address
mehmet.turkan@ieu.edu.tr
mehmet.turkan@gmail.com
mehmet.turkan@gmail.com
Main Affiliation
05.05. Computer Engineering
Status
Current Staff
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Sustainable Development Goals
8
DECENT WORK AND ECONOMIC GROWTH
0
Research Products
9
INDUSTRY, INNOVATION AND INFRASTRUCTURE
4
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10
REDUCED INEQUALITIES
0
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17
PARTNERSHIPS FOR THE GOALS
0
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12
RESPONSIBLE CONSUMPTION AND PRODUCTION
0
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7
AFFORDABLE AND CLEAN ENERGY
0
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1
NO POVERTY
0
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5
GENDER EQUALITY
0
Research Products
13
CLIMATE ACTION
0
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4
QUALITY EDUCATION
1
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14
LIFE BELOW WATER
0
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2
ZERO HUNGER
0
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15
LIFE ON LAND
0
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16
PEACE, JUSTICE AND STRONG INSTITUTIONS
0
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6
CLEAN WATER AND SANITATION
0
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3
GOOD HEALTH AND WELL-BEING
5
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11
SUSTAINABLE CITIES AND COMMUNITIES
0
Research Products
Documents
55
Citations
968
h-index
15
Documents
47
Citations
617
Scholarly Output
49
Articles
13
Views / Downloads
36/123
Supervised MSc Theses
9
Supervised PhD Theses
2
WoS Citation Count
463
Scopus Citation Count
721
WoS h-index
7
Scopus h-index
9
Patents
0
Projects
0
WoS Citations per Publication
9.45
Scopus Citations per Publication
14.71
Open Access Source
17
Supervised Theses
11
| Journal | Count |
|---|---|
| 2022 Medıcal Technologıes Congress (Tıptekno'22) | 4 |
| Sıgnal Processıng | 4 |
| Proceedings - International Conference on Image Processing, ICIP | 3 |
| 2024 Medical Technologies Congress -- OCT 10-12, 2024 -- Bodrum, TURKIYE | 2 |
| 2021 Ieee Internatıonal Conference on Image Processıng (Icıp) | 2 |
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49 results
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Now showing 1 - 10 of 49
Conference Object Vision-Based Denim Quality Assessment(Institute of Electrical and Electronics Engineers Inc., 2023) Balkaya, P.; Basut, M.C.; Türkan, MehmetManual color quality control is a slow process in textile industry. The color separation of denim products is judged by human operators to decide the correct chemical process to be applied such as ozone washing, to obtain the desired colors and textural characteristics for end-users. This paper aims at designing a vision-based automated system for grouping industrial denim jeans in order to eliminate human error or any other external factor, and increase efficiency and reliability of the process. Firstly, uniquely identifying features are extracted from images of the products taken in similar conditions using an image capture system, that is specifically designed for it, then parameters of clustering algorithms such as the number of clusters are decided by considering different validity scores. Finally, using the outcomes of several algorithms, a clustering is made from a batch of denim products and then the results are sent to human operators to be evaluated. © 2023 IEEE.Master Thesis Hücre Sayım Uygulamaları için Akıllı Telefon Tabanlı Otomatize Opto-Akışkan Platform(2025) Avcı, Meryem Beyza; Türkan, MehmetHücre analiz teknolojileri, hücre canlılığı, yoğunluğu ve konfluens gibi temel parametrelerin yüksek doğrulukta değerlendirilmesini sağlayarak biyomedikal araştırmalarda önemli bir rol oynamaktadır. Gelişmiş ticari çözümlerin mevcudiyetine rağmen, birçok sistem yüksek maliyetler, karmaşık donanım gereksinimleri ve çeşitli deneysel ortamlarda kısıtlı uyarlanabilirlik nedeniyle sınırlı kalmaktadır. Bu sınırlamalara çözüm bulmak için; düşük maliyetli optik bileşenleri, otomatik sıvı yönetimini ve uyarlanabilir görüntü işleme algoritmalarını entegre eden, akıllı telefon tabanlı otomatik bir optoakışkan platform geliştirilmiştir. Platformun tasarımı, sade bir donanım yapısıyla güçlü analiz kabiliyetlerini birleştirmektedir. Sistem, hem tek kullanımlık hem de çok kullanımlık modları destekleyen temizlenebilir bir akış haznesi içermekte olup maliyet etkinliği ve esneklik sağlamaktadır. Platforma özel olarak geliştirilen mobil uygulama Qtouch; gerçek zamanlı donanım kontrolü, Bluetooth destekli iletişim ve sunucu tabanlı görüntü işlemeyi entegre ederek işlemleri kolaylaştırmaktadır. Görüntü işleme süreci, çoklu pozlama birleştirme, uyarlanabilir eşikleme ve morfolojik filtreleme teknikleri ile ham görüntü verilerini geliştirmektedir. Bu aşamalar, hücre morfolojisi veya kümelenme durumundan bağımsız olarak hücrelerin doğru şekilde bölütlenmesini sağlamakta ve derin öğrenme modellerine ya da önceden tanımlanmış parametrelere olan gereksinimi ortadan kaldırmaktadır. Farklı hücre tipleriyle gerçekleştirilen doğrulama deneyleri, platformun hücre sayımında yüksek doğruluk sağladığını ortaya koymuş; sonuçların, hücre analizinde altın standart olarak kabul edilen akış sitometrisine göre %5'ten daha az sapma gösterdiği belirlenmiştir. Platform, yüksek verimli uygulamalara uyum sağlayabilecek kapasitede olup her bir testte 10.000'den fazla hücreyi işleyebilmekte ve istatistiksel varyasyonu en aza indirmektedir. Platform, uygun maliyetli, uyarlanabilir ve yüksek doğrulukta bir çözüm sunarak ileri düzey laboratuvar sistemleri ile kaynağı kısıtlı araştırma ortamları arasında etkili bir geçiş sağlamaktadır.Conference Object Dermoscopic Lesion Segmentation Via Optimal Color Channel Fusion(IEEE, 2024) Okur, Erdem; Unay, Devrim; Turkan, MehmetDeath caused by various kinds of cancer is on rise and skin cancer is one of the most common one worldwide. Due to the importance of early detection, dermoscopy is adopted for visualizing skin lesions and computer-aided detection benefits from these dermoscopic images for better diagnosis results. One of the most important phase of automated skin lesion detection or classification is segmentation, but it is a very challenging task because of several artifacts existing on these images. In this paper, a new method to improve skin lesion segmentation from the existing deep network architectures is proposed, based on the fusion of various results produced by existing models on different color channels. Experimental validations demonstrate that the proposed method increases the average accuracy, on lesion segmentation in terms of Sorensen-Dice and Jaccard indices, when compared to conventional techniques.Conference Object Citation - WoS: 1Citation - Scopus: 1Dictionary Learning With Residual Codes(Institute of Electrical and Electronics Engineers Inc., 2017) Oktar Y.; Türkan, MehmetIn conventional sparse representations based dictionary learning algorithms, initial dictionaries are generally assumed to be proper representatives of the system at hand. However, this may not be the case, especially in some systems restricted to random initialization. Therefore, a supposedly optimal state-update based on such an improper model might lead to undesired effects that will be conveyed to successive learning iterations. In this paper, we propose a dictionary learning method which includes a general error-correction process that codes the residual left over from a less intensive initial learning attempt and then adjusts the sparse codes accordingly. Experimental observations show that such additional step vastly improves rates of convergence in high-dimensional cases, also results in better converged states in the case of random initialization. Improvements also scale up with more lenient sparsity constraints. © 2017 IEEE.Conference Object Citation - Scopus: 1Patch Enhancement for Melanoma Detection With Bag of Visual Words(IEEE, 2022) Okur, Erdem; Turkan, MehmetMelanoma is a type of skin cancer caused by the ultraviolet radiation of Sun. Melanoma will become severe if it is not detected early, and it may spread to other body organs, most commonly the lungs, brain, liver, and bones. Dermatologists look for tell-tale signs of melanoma on pigmented skin lesions (moles) to detect it or, in some cases, differentiate it from other skin diseases. Unfortunately, imprecise subjective analysis may result in a series of biopsies that are unnecessary. Furthermore, this type of imprecision may allow a melanoma case to spread undetected. This study develops an automatic melanoma detection system to overcome this challenge. The proposed method is based on Bag of Visual Words (BoVW) with a new patch enhancement scheme, which incorporates both traditional and cutting-edge methods. Experimental comparisons between the proposed method and the well-known convolutional neural network models demonstrate the effectiveness of the developed system.Conference Object Citation - WoS: 3Citation - Scopus: 4Image Fusion Through Linear Embeddings(IEEE, 2021) Ulucan, Oguzhan; Karakaya, Diclehan; Turkan, MehmetThis paper proposes an effective technique for multi-exposure image fusion and visible-infrared image fusion problems. Multi-exposure fusion algorithms generally extract faulty weight maps when the input stack contains multiple and/or severely over-exposed images. To overcome this issue, an alternative method is developed for weight map characterization and refinement in addition to the perspectives of linear embeddings of images and adaptive morphological masking. This framework has then been extended to the visible and infrared image fusion problem. The comprehensive experimental comparisons demonstrate that the proposed algorithm significantly enhances the fused image quality both statistically and visually.Article Citation - WoS: 25Citation - Scopus: 32Ghosting-Free Multi-Exposure Image Fusion for Static and Dynamic Scenes(Elsevier, 2023) Ulucan, Oguzhan; Ulucan, Diclehan; Turkan, MehmetThe visual system enables humans to perceive all details of the real-world with vivid colors, while high dynamic range (HDR) technology aims at capturing natural scenes in a closer way to human perception through a large dynamic range of color gamut. Especially for traditional -low dynamic range (LDR)- de-vices, HDR-like image generation is an attractive research topic. Blending a stack of input LDR exposures is called multi-exposure image fusion (MEF). MEF is indeed a very challenging problem and it is highly prone to halo effects or ghosting and motion blur in the cases when there are spatial discontinuities in between input exposures. To overcome these artifacts, MEF keeps the best quality regions of each exposure via a weight characterization scheme. This paper proposes an effective weight map extraction framework which relies on principal component analysis, adaptive well-exposedness and saliency maps. The characterized maps are later refined by a guided filter and a blended output image is obtained via pyramidal decomposition. Comprehensive experiments and comparisons demonstrate that the developed algorithm generates very strong statistical and visual results for both static and dynamic scenes. In ad-dition, the designed method is successfully applied to the visible-infrared image fusion problem without any further optimization.(c) 2022 Elsevier B.V. All rights reserved.Article Citation - WoS: 3Citation - Scopus: 6Image declipping: Saturation correction in single images(Academic Press Inc Elsevier Science, 2022) Karakaya, Diclehan; Ulucan, Oguzhan; Turkan, MehmetHigh dynamic range (HDR) images present fine details in a scene and are visually more appealing than low dynamic range (LDR) images, since they contain a greater dynamic range of color gamut. HDR compatible displays are currently high-cost, therefore tone-mapping algorithms have widely been used to obtain high quality images for LDR screens with a lower cost. However, tone-mapped images may contain clipped pixel regions, which should be corrected to retrieve the lost information, to acquire visually pleasing LDR images. In a single image, the recovery of color and texture information in clipped regions is challenging, yet an attractive research field in image processing. Although there are several algorithms present in literature, developing a general framework for different types of image content is hard to achieve. This study proposes a single image declipping method based on linear embeddings, difference of pixels and block-search. Experimental results carried out on a tone-mapped HDR image dataset and LDR images demonstrate that the proposed algorithm is able to successfully recover saturated pixels in various types of images. Detailed statistical and visual comparisons show that this approach produces superior results on average for both tone-mapped and LDR images when compared to existing techniques.(c) 2022 Elsevier Inc. All rights reserved.Master Thesis Optimized Exemplar-Based Light Field Super-Resolution(İzmir Ekonomi Üniversitesi, 2023) Aydeniz, Burhan; Türkan, MehmetIşık alanı görüntüleme tekniği, farklı konumlarda ve yönlerde yakalanan ışık hüzmelerinin görüntülerini üretebilir. Çeşitli donanım kısıtlamaları nedeniyle, ışık alanı görüntüleri düşük uzamsal çözünürlüğe sahiptir. Görüntü üretim modelinde, görüntü detaylarının korunması amacıyla, farklı yüksek çözünürlüklü görüntüler tahmin edilebilir. Bu kötü konumlanmış optimizasyon problemini çözmek için literatürde bir cok süper-çözünürlük yöntemi önerilmiştir. Bu tezde, düşük çözünürlüklü görüntülerden çıkarılan örnek yama çiftleri aracılığıyla doğrusal yerleştirme ve dikgen eşleştirme takibi tabanlı algoritmalar kullanılarak örnek tabanlı ışık alanı süper-çözünürlük algoritmaları geliştirilmiştir. Önerilen yöntemler, düşük eşitsizlikli ışık alanı verisetlerinde yüksek çözünürlüklü görüntüleri tahmin etmektedir. İstatistiksel ve görsel sonuçlara göre, en ileri teknoloji algoritmalar ile karşılaştırıldığında önerilen örnek tabanlı ışık alanı süper çözünürlük yaklaşımı dikkate değer bir performans sağlamaktadır.Conference Object Citation - WoS: 2Citation - Scopus: 2Nled: Neighbor Linear-Embedding Denoising for Fluorescence Microscopy Images(IEEE, 2022) Kirmiziay, Cagatay; Aydeniz, Burhan; Turkan, MehmetAs noise corruption is an inevitable issue for all imaging technologies, this problem causes serious difficulties in analyzing the biological fine-details of fluorescence microscopy images. While Gaussian only, Poisson only and mixture of Poisson-Gaussian can generally be observed, the mixed-noise is more prominent in fluorescence microscopy. In this paper, a novel patch-based denoiser-learning approach is proposed for the images captured by fluorescence microscopy. The developed algorithm mainly builds upon linear-embeddings of neighboring image patches, and it learns a linear transformation between noisy and clean intrinsic geometric properties of patch-spaces. Experimental results demonstrate that the proposed Neighbor Linear-Embedding Denoising (NLED) has competitive performance both visually and statistically when compared to other algorithms in literature, for noise corrupted fluorescence microscopy images.
