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Citation - WoS: 13
Automatic HTML Code Generation from Mock-Up Images Using Machine Learning Techniques
(IEEE, 2019) Asiroglu, Batuhan; Mate, Busra Rumeysa; Yildiz, Eyyup; Nalcakan, Yagiz; Sezen, Alper; Dagtekin, Mustafa; Ensari, Tolga
The design cycle for a web site starts with creating mock-ups for individual web pages either by hand or using graphic design and specialized mock-up creation tools. The mock-up is then converted into structured HTML or similar markup code by software engineers. This process is usually repeated many more times until the desired template is created. In this study, our aim is to automate the code generation process from hand-drawn mock-ups. Hand drawn mock-ups are processed using computer vision techniques and subsequently some deep learning methods are used to implement the proposed system. Our system achieves 96% method accuracy and 73% validation accuracy.
Robust Keypoint Matching for Three Dimensional Scenes and Object Recognition
(IEEE, 2017) Koksal, Ali; Uzyildirim, Furkan Eren; Ozuysal, Mustafa
In this paper, we adapt a recently proposed keypoint matching approach for binary descriptors and planar objects to three dimensional objects. We also evaluate the performance of this approach for a museum object recognition application containing more than one hundred paintings. Moreover, we quantify the effect of selecting only descriptors with high matching ratio on the success rate of the object recognition application.

WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection