Browsing by Author "Ulucan O."

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Citation - Scopus: 1
Binocular Vision Based Convolutional Networks
(Institute of Electrical and Electronics Engineers Inc., 2020) Oktar Y.; Ulucan O.; Karakaya D.; Ersoy E.O.; Türkan, Mehmet
It is arguable that whether the single camera captured (monocular) image datasets are sufficient enough to train and test convolutional neural networks (CNNs) for imitating the biological neural network structures of the human brain. As human visual system works in binocular, the collaboration of the eyes with the two brain lobes needs more investigation for improvements in such CNN-based visual imagery analysis applications. It is indeed questionable that if respective visual fields of each eye and the associated brain lobes are responsible for different learning abilities of the same scene. There are such open questions in this field of research which need rigorous investigation in order to further understand the nature of the human visual system, hence improve the currently available deep learning applications. This paper analyses a binocular CNNs architecture that is more analogous to the biological structure of the human visual system than the conventional deep learning techniques. While taking a structure called optic chiasma into account, this architecture consists of basically two parallel CNN structures associated with each visual field and the brain lobe, fully connected later possibly as in the primary visual cortex. Experimental results demonstrate that binocular learning of two different visual fields leads to better classification rates on average, when compared to classical CNN architectures. © 2020 IEEE.
Citation - Scopus: 3
A Comparative Study on Electronic Nose Data Analysis Tools
(Institute of Electrical and Electronics Engineers Inc., 2020) Karakaya D.; Ulucan O.; Türkan, Mehmet
In the last decades, the electronic nose technology has been providing considerable advantages in practical applications including food and beverage quality assessment, medical diagnosis, security systems and air monitoring. Electronic nose systems include both hardware and software components. Sensors allow the system to collect gas/odor samples and the software carries out the classification process. While choosing robust, sensitive and compact elements is significant for the hardware requirements, the key point in the software part is selecting the appropriate algorithm, which is typically a challenging, time consuming and laborious process. Therefore in this study, an extensive comparison of the most commonly employed unsupervised data analysis algorithms in practical electronic nose applications is carried out. These approaches are also compared with supervised methods. Frequently used four dimensionality reduction techniques and four distinct clustering and classification algorithms are employed aiming at choosing the most suitable algorithms for further research in this domain. © 2020 IEEE.
Citation - Scopus: 69
A Large-Scale Dataset for Fish Segmentation and Classification
(Institute of Electrical and Electronics Engineers Inc., 2020) Ulucan O.; Karakaya D.; Türkan, Mehmet
Assessing the quality of seafood both in retail and during packaging at the production side must be carried out minutely in order to avoid spoilage which causes severe human health problems and also economic loss. Since the illnesses and decay in seafood presents distinct symptoms in different species, primarily the classification of species is required. In this field, the inadequacy of the current laborious and slow traditional methods can be overcome with systems based on machine learning and image processing, which present fast and precise results. In order design such systems, practical and suitable datasets are required. However, most of the publicly available datasets are not fit for the mentioned purpose. These datasets either contain images taken underwater or consist of seafood which is generally not (widely) consumed. In this study, a practical and large dataset containing nine distinct seafood widely consumed in the Aegean Region of Turkey is formed. Additionally, comprehensive experiments based on different classification approaches are performed to analyze the usability of this collected dataset. Experimental results demonstrate very promising outcomes; therefore, this dataset will be made publicly available for further investigations in this research domain. © 2020 IEEE.