Yazar "Incetas, Muersel Ozan" 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
    Spiking neural network-based edge detection model for content-based image retrieval
    (Springer London Ltd, 2024) Incetas, Muersel Ozan; Arslan, Rukiye Uzun
    Content-based image retrieval (CBIR) techniques are widely used for extracting specific images from large databases. Recent studies have shown that edge features, alongside colors, align closely with human perception in CBIR. However, most CBIR approaches detect edges using linear methods like gradients, which do not align with how the human visual system (HVS) perceives edges. Bioinspired approaches, based on HVS, have proven more effective for edge detection. This study introduces a novel bioinspired spiking neural network (SNN)-based edge detection method for CBIR. The proposed method reduces computational costs by approximately 2.5 times compared to existing SNN models and offers a simpler, easily integrated structure. When integrated into CBIR techniques using conventional edge detection methods (Sobel, Canny, and image derivatives), it increased the mean precision on the Corel-1k dataset by over 3%. These results indicate that the proposed method is effective for edge-based CBIR.