Browsing by Author "Yayci Z.O."

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Citation - Scopus: 4
Audio-Visual Speech Recognition Using 3d Convolutional Neural Networks
(Institute of Electrical and Electronics Engineers Inc., 2021) Belhan C.; Fikirdanis D.; Cimen O.; Pasinli P.; Akgun Z.; Yayci Z.O.; Türkan, Mehmet
Lip reading, described as extracting speech data from the observable deeds in the face, particularly the jaws, lips, tongue and teeth, is a very challenging task. It is indeed a beneficial skill that helps people to comprehend and interpret the content of other people's speech, when it is not sufficient to recognize either audio or expression. Even experts require a certain level of experience and need an understanding of visual expressions to interpret spoken words. However, this may not be efficient enough for the process. Nowadays, lip sequences can be converted into expressive words and phrases with the aid of computers. Thus, the usage of neural networks (NNs) is increased rapidly in this field. The main contribution of this study is to use Short-Time Fourier Transformed (STFT) audio data as an extra image input and employing 3D Convolutional NNs (CNNs) for feature extraction. This generates features considering the change in consecutive frames and makes use of visual and auditory data together with the attributes from the image. After testing several experimental scenarios, it turns out to be the proposed method has a strong promise for further development in this research domain. © 2021 IEEE.
Citation - WoS: 2
Citation - Scopus: 2
MICROSCALE IMAGE ENHANCEMENT VIA PCA AND WELL-EXPOSEDNESS MAPS
(IEEE Computer Society, 2022) Yayci Z.O.; Dura U.; Kaya Z.B.; Cetin A.E.; Türkan, Mehmet
The restrictions of accessing high-end microscopes, microscale cameras and high-tech imaging lenses result in a high demand on low-cost microscopes. However, low-cost microscopes are facing with many image capture and quality limitations due to incompatible equipped instrumentation. This study aims at overcoming illumination and contrast problems, color aberration issues, and blur and noise corruption in low-cost microscopes at high image magnification rates. The three color channels of the input image are enhanced via principal component analysis and well-exposedness feature maps by means of cross-channel histogram matching, Laplacian and non-local means filtering. The proposed approach produces sharper, and better color and illumination fixed outputs when compared to existing methods in literature. © 2022 IEEE.