Browsing by Author "Karakaya, Diclehan"

Now showing 1 - 9 of 9

Citation - WoS: 10
Citation - Scopus: 47
A Comparative Analysis on Fruit Freshness Classification
(IEEE, 2019) Karakaya, Diclehan; Ulucan, Oguzhan; Turkan, Mehmet
Automatic classification of food freshness plays a significant role in the food industry. Food spoilage detection from production to consumption stages needs to be performed minutely. Traditional methods which detect the spoilage of food are slow, laborious, subjective and time consuming. As a result, fast and accurate automatic methods need to be introduced to industrial applications. This study comparatively analyses an image dataset containing samples of three types of fruits to distinguish fresh samples from those of rotten. The proposed vision based framework utilizes histograms, gray level co-occurrence matrices, bag of features and convolutional neural networks for feature extraction. The classification process is carried out through well-known support vector machines based classifiers. After testing several experimental scenarios including binary and multi-class classification problems, it turns out to be the highest success rates are obtained consistently with the adoption of the convolutional neural networks based features.
Citation - WoS: 251
Citation - Scopus: 323
Electronic Nose and Its Applications: a Survey
(Springernature, 2020) Karakaya, Diclehan; Ulucan, Oguzhan; Turkan, Mehmet
In the last two decades, improvements in materials, sensors and machine learning technologies have led to a rapid extension of electronic nose (EN) related research topics with diverse applications. The food and beverage industry, agriculture and forestry, medicine and health-care, indoor and outdoor monitoring, military and civilian security systems are the leading fields which take great advantage from the rapidity, stability, portability and compactness of ENs. Although the EN technology provides numerous benefits, further enhancements in both hardware and software components are necessary for utilizing ENs in practice. This paper provides an extensive survey of the EN technology and its wide range of application fields, through a comprehensive analysis of algorithms proposed in the literature, while exploiting related domains with possible future suggestions for this research topic.
Image Declipping: Saturation Correction İn Images
(İzmir Ekonomi Üniversitesi, 2020) Karakaya, Diclehan; Türkan, Mehmet
Yüksek dinamik aralığa (YDA) sahip görüntüler bir sahnedeki ince detayları sunabilir ve düşük dinamik aralıktaki (DDA) resimlerden görsel olarak daha çekicidirler, çünkü renklerin dinamik aralığı YDA'da daha fazladır. Günümüzde YDA ile uyumlu cihazlar pahalı olduğundan ton haritalama algoritmaları düşük maliyetle yüksek kaliteye sahip görüntüler elde etmek için sıklıkla kullanılmaktadır. Ancak ton haritalanmış görüntüler yanmış piksel bölgeleri barındırabilir; bilgi kaybının önüne geçmek ve görsel olarak hoş görüntüler elde etmek için bu piksellerdeki kırpma düzeltilmelidir. Ton haritalanmış tek bir resimde, kırpılmış alanlardaki renk ve doku bilgisinin düzeltilmesi görüntü işleme alanında zorlu fakat çekici bir çalışma dalıdır. Literatürde birçok algoritma bulunmasına rağmen, farklı türdeki görüntüleri düzeltebilen genel bir sistem yaratmayı başarmak zordur. Bu tezde doğrusal gömme, piksel değerlerinin farkı ve blok aramaya dayanan bir tek resim kırpma düzeltme yolu önerilmiştir. Ton haritalanmış bir YDA veri seti ve ham DDA görüntüler ile yapılan deneyler, önerilen algoritmanın farklı tür resimlerdeki kırpılmış pikselleri başarılı bir şekilde düzelttiğini göstermiştir. Yapılan detaylı görsel ve istatistiksel incelemeler, önerilen yöntemin ortalamada hem ton haritalanmış hem de (ham) DDA görüntülerde var olan tekniklerden daha iyi sonuçlar verdiğini göstermiştir.
Citation - WoS: 3
Citation - Scopus: 6
Image declipping: Saturation correction in single images
(Academic Press Inc Elsevier Science, 2022) Karakaya, Diclehan; Ulucan, Oguzhan; Turkan, Mehmet
High 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.
Citation - WoS: 3
Citation - Scopus: 4
Image Fusion Through Linear Embeddings
(IEEE, 2021) Ulucan, Oguzhan; Karakaya, Diclehan; Turkan, Mehmet
This 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.
Citation - WoS: 6
Citation - Scopus: 16
Meat Quality Assessment Based on Deep Learning
(IEEE, 2019) Ulucan, Oguzhan; Karakaya, Diclehan; Turkan, Mehmet
Under unsuitable sales conditions, red meat containing rich amount of protein might receive a negative perception from consumers. Importantly, nutrients lose their effectiveness, while at the same time the formation of harmful microorganisms becomes a threat to human health. The main purpose of this study is to keep the quality of the open department sales service offered to the consumers in the retail red meat sector at the highest level, to ensure the sustainability of resources and to provide immediate economic precautions by reducing the disposal of the red meat due to possible deterioration. To do so, one tray of meat cubes has been monitored for a long time with a stable camera mounted on a pilot red meat counter and RGB images have been acquired in every two minutes. In parallel, expert data has been gathered and used as reference labels. After a preprocessing mechanism on the acquired images, a deep convolutional neural networks architecture has been modeled and trained to classify images as fresh or spoiled. The obtained experimental results and comparisons prove that deep learning methods will be very successful in this research field. However, the most important challenge in this subject is to collect large volumes of training datasets of various types of meat and meat products which are individually labeled by food experts.
Citation - WoS: 30
Citation - Scopus: 31
Multi-Exposure Image Fusion Based on Linear Embeddings and Watershed Masking
(Elsevier, 2021) Ulucan, Oguzhan; Karakaya, Diclehan; Turkan, Mehmet
High dynamic range imaging (HDRI) is a challenging technology but yet demanding for modern imaging applications. Low-cost image sensors have limited dynamic range, and it is not always possible to capture and display natural scenes with high contrast and information loss in any exposure is inevitable. Three solutions for HDRI are using expensive high dynamic range (HDR) cameras with HDR-compatible displays, tone mapping operators for low dynamic range (LDR) screens, and capturing and fusing multiple exposures of the same LDR scene via image fusion algorithms. Companies that produce user grade devices prefer multi-exposure fusion (MEF) approaches to obtain HDR-like images for LDR screens due to its low cost. Hence, merging a stack of images containing different exposures of the same scene into a single informative image is an attractive research field. In this study, a novel, simple yet effective method is proposed for static image exposure fusion. The developed technique is based on weight map extraction via linear embeddings and watershed masking. The main advantage lies in watershed masking-based adjustment for obtaining accurate weights for image fusion. The comprehensive experimental comparisons demonstrate very strong visual and statistical results, and this approach should facilitate future MEF studies. (C) 2020 Elsevier B.V. All rights reserved.
Citation - WoS: 19
Citation - Scopus: 26
PAS-MEF: MULTI-EXPOSURE IMAGE FUSION BASED ON PRINCIPAL COMPONENT ANALYSIS, ADAPTIVE WELL-EXPOSEDNESS AND SALIENCY MAP
(IEEE, 2022) Karakaya, Diclehan; Ulucan, Oguzhan; Turkan, Mehmet
High dynamic range (HDR) imaging enables to immortalize natural scenes similar to the way that they are perceived by human observers. With regular low dynamic range (LDR) capture/display devices, significant details may not be preserved in images due to the huge dynamic range of natural scenes. To minimize the information loss and produce high quality HDR-like images for LDR screens, this study proposes an efficient multi-exposure fusion (MEF) approach with a simple yet effective weight extraction method relying on principal component analysis, adaptive well-exposedness and saliency maps. These weight maps are later refined through a guided filter and the fusion is carried out by employing a pyramidal decomposition. Experimental comparisons with existing techniques demonstrate that the proposed method produces very strong statistical and visual results.
Citation - WoS: 1
Citation - Scopus: 1
Saturated Region Recovery in Tone-Mapped Hdr Images
(IEEE, 2021) Ulucan, Oguzhan; Karakaya, Diclehan; Turkan, Mehmet
Tone-mapping is one of the prevailing methods to overcome high dynamic range imaging limitations over low dynamic range display devices, but the tone-mapped output image may suffer from saturated regions with texture and color information loss. In this paper, a novel approach is proposed to solve the so-called clipping problem in tone-mapped high dynamic range images. A successful saturation correction framework, which relies on linear embeddings, difference of pixel intensities and gradient-guided block-search, is developed as a post-processing technique to tone-mapping. Experimental results demonstrate that the proposed method successfully recovers clipped regions for the saturation problem in tone-mapped output images while avoiding artifacts.