Nithiyanandham E K, Srutha Keerthi B
Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology Chennai Campus, Chennai - 600 127, India.
Heliyon. 2024 May 18;10(10):e31430. doi: 10.1016/j.heliyon.2024.e31430. eCollection 2024 May 30.
This research introduces a new approach to elevate the precision of image edge detection through a new algorithm rooted in the coefficients derived from the subclass (CSKP model). Our method employs convolution operations on input image pixels, utilizing the CSKP mask window in eight distinct directions, fostering a comprehensive and multi-directional analysis of edge features. To gauge the efficacy of our algorithm, image quality is assessed through perceptually significant metrics, including contrast, correlation, energy, homogeneity, and entropy. The study aims to contribute a valuable tool for diverse applications such as computer vision and medical imaging by presenting a robust and innovative solution to enhance image edge detection. The results demonstrate notable improvements, affirming the potential of the proposed algorithm to advance the current state-of-the-art in image processing.
本研究引入了一种新方法，通过基于从子类（CSKP模型）导出的系数的新算法来提高图像边缘检测的精度。我们的方法对输入图像像素进行卷积操作，利用CSKP掩码窗口在八个不同方向上进行，促进对边缘特征的全面和多方向分析。为了评估我们算法的有效性，通过包括对比度、相关性、能量、均匀性和熵等在感知上有意义的指标来评估图像质量。该研究旨在通过提出一种强大且创新的解决方案来增强图像边缘检测，为计算机视觉和医学成像等各种应用贡献一个有价值的工具。结果显示出显著的改进，证实了所提出算法推进当前图像处理技术水平的潜力。
