基于尺度不变特征变换的快速图像特征区域检测

针对快速图像特征区域检测受噪声干扰和尺度空间影响, 导致图像特征区域检测精度较低、 延时较长, 检测结果不可靠的问题, 提出一种基于尺度不变特征变换的快速图像特征区域检测方法. 先通过加权核函数, 加权平滑处理图像中各像素点, 实现图像去噪； 再在此基础上通过构建图像高斯尺度空间确定图像特征点区域, 删除低对比度像素点和边缘像素点, 快速提取图像特征点, 检测特征点所在区域即为图像特征区域. 仿真实验结果表明, 该方法能高效率、高精度地实现快速图像特征区域检测的全面检测.

Fast Image Feature Region Detection Based on Scale Invariant Feature Transformation

Aiming at the problem that the fast image feature region detection was affected by noise and scale space, which resulted in low detection accuracy, large delay and unreliable detection results, the author proposed a fast image feature region detection method based on scale invariant feature transformation. Each pixel in the image was weighted and smoothed by weighted kernel function to realize image denoising. On this basis, image feature points were determined by constructing image Gaussian scale space, low contrast pixels and edge pixels were deleted, and image feature points were quickly extracted and the detection of location of feature points was the image feature region. The simulation results show that the method can achieve fast and comprehensive detection of image feature region with high efficiency and precision.