Published in ArXiv, 2016
In this paper, we have proposed an extension of binary local feature descriptor which leverages the color information.
Recommended citation: A. Abdulkhaev, O. Yilmaz (2016). "U-CATCH: Using Color ATtribute of image patCHes in binary descriptors" ArXiv. https://alisher-ai.github.io/files/U-CATCH.pdf
Published in Technical report, 2016
In this paper, we proposed an algorithm for the identification of shiny objects by fusing motion and static image cues which dramatically improves detection performance.
Recommended citation: O. Yilmaz, A. Abdulkhaev (2016). "Combining image and video cues for specular object detection" Journal 1. 1(2). https://alisher-ai.github.io/files/Specular_Object_Blind_Review.pdf
Published in Thesis, 2016
Digital Watermarking plays a significant role in data authentication and proof of ownership. In this study, a novel approach for Video Watermarking is proposed, in which the Genetic Algorithm is used to optimize the parameters of digital video watermarking scheme. One of the most straightforward watermarking method is LSB (Least Signifiant Bit) in which watermark is embedded into least significant bit of host media. Our work is based on LSB watermarking method. Although, standard LSB watermarking is good choice for data embedding, it is not robust against bit-deletion, gaussian, median filtering attacks. We use genetic algorithm to select the most optimum bit plane during the embedding process and the watermark is embedded into optimum (6th,7th or 8th) bit of host video frames, to overcome above mentioned attacks’ degradations. Decision is done with respect to Peak Signal Noise Ratio (PSNR) of watermarked frame and Normalized Cross Correlation (NCC) values of the original and extracted watermark. Experiments are performed for various watermarks, host video, and a set of Genetic Algorithm parameters, such as population size, number of iteration, tournament range. Results suggest that modified LSB watermarking scheme is more robust to bit-plane deletion, gaussian, median filtering and other similar attacks.
Recommended citation: A. Abdulkhaev (2016). "A new approach for video watermarking" https://alisher-ai.github.io/files/Masters-thesis--A-new-approach-for-video-watermarking.pdf
Published in EEMKON2015, 2015
In this paper we have proposed a new approach for video watermarking using genetic algorithm.
Recommended citation: A. Abdulkhaev, S.K. Kayhan (2015). "A New Approach For Video Watermarking Using Genetic Algorithm." EEMKON2015