Yazar "Kilicaslan, Mahmut" seçeneğine göre listele

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    Öğe
    Adaptive Color Quantization Method with Multi-level Thresholding
    (Springernature, 2023) Kilicaslan, Mahmut; Incetas, Muersel Ozan
    In this study, a novel color quantization approach which automatically estimates the number of colors by multi-level thresholding based on the histogram is proposed. The method consists of three stages. First, red-green-blue is clustered by threshold values. Thus, the pixels are positioned in a cluster or sub-prism. Second, the color palette is produced by determining the centroids of the clusters. Finally, the pixels are reassigned to clusters based on their distance from each centroid. The average of the pixels included in each cluster also represents the color of that cluster. While conventional methods are user-dependent, the proposed algorithm automatically generates the number of colors by considering the pixels assigned to the clusters. Additionally, the multi-level thresholding approach is also a solution to the initialization problem, which is another important issue for quantization. Consequently, the experimental results of the method tested with various images show better performance than many frequently used quantization techniques.
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    Öğe
    Image watermarking based on spiking neural networks
    (Springer, 2025) Incetas, Mursel Ozan; Kilicaslan, Mahmut
    Image watermarking serves as a crucial technique for protecting copyrights and verifying the ownership of digital images. The watermarking process involves embedding data into images to prevent unauthorized usage of digital content. In this study, an innovative image watermarking method, using Spiking Neural Networks to embed robust and imperceptible watermarks, is proposed. The proposed method benefits from the edge detection capabilities of spiking neural networks to identify optimal regions for watermark locations. By targeting edge detection, this watermarking approach ensures significant resistance to common image processing attacks such as compression, noise addition, and cropping, while maintaining minimal perceptual distortion. The edge image obtained with the spiking neural network approach is divided into 16 x 16 non-overlapping blocks, and edge pixel definitions are made. Moreover, to increase the security level, the watermark image is scrambled with the help of a chaotic substitution box. The scrambled image is placed on the pixels marked as edges in the edge image. Afterward, it is divided into sub-bands by applying a discrete wavelet transform. The watermark image is inserted into the HH (High-High) band with the help of the discrete wavelet transform and the singular value decomposition approach. In the extraction stage, the HH band of the original image is used together with the watermarked image. Comprehensive experiments are conducted to evaluate the proposed technique, revealing its superiority in preserving both image quality and watermark integrity compared to conventional approaches.