Master Degree / Yüksek Lisans Tezleri

Permanent URI for this collectionhttps://hdl.handle.net/11147/3008

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Author: search.filters.author.Özuysal, MustafaSubject: search.filters.subject.Deep learning

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  • Master Thesis
    Multi-Frame Super-Resolution Without Priors
    (01. Izmir Institute of Technology, 2023) Gülmez, Veli; Özuysal, Mustafa
    There are mainly two types of super-resolution methods: traditional methods and deep learning methods. While traditional methods define closed-form expressions with assumptions, deep learning methods rely on priors learned from data sets. However, both of them have disadvantages such as being too simple and having strong trust in priors. We focus on how to generate a high-resolution image using low-resolution images without priors by utilizing spatial hash encoding. We propose a grid-based super-resolution model using spatial hash encoding to map coordinate information into higher dimensional space. Our aim is to eliminate long training times and not rely on priors from data sets that are not able to cover all real-world scenarios. Therefore, our proposed model is able to do task- specific super-resolution without priors and eliminate potential hallucination effects caused by wrong priors.
  • Master Thesis
    Vehicle Type Classification With Deep Learning
    (Izmir Institute of Technology, 2020) Yaraş, Neriman; Özuysal, Mustafa
    In this thesis, we studied the vehicle type classification problem from several perspectives. We apply a deep learning technique with different parameters such as image size and the number of images in data sets to the classification of an image as one of the nine vehicle types. After choosing the most appropriate one among trained models, we convert the problem into a hierarchical tree classification problem so that it could be analyzed in three different tree hierarchies. Experiments are performed using three computational methods for calculating possibilities for each of the nine classes that correspond to the leaves of the hierarchical trees. These studies result in a conclusion that 0.762812 average accuracy is obtained when traditional arithmetic mean computation applied on the hierarchical tree with level-2 using the Stanford Dataset by 224 image size on ResNet34 architecture.