Abstract:

In order to solve the problem of poor performance of feature point extraction algorithm in uneven illumination, an improved SIFT（Scale Invariant Feature Transform） algorithm is proposed to suppress the influence of uneven illumination. Firstly, in the scale-space construction, the input image is filtered by Gaussian high-pass filtering in frequency domain to filter the light components, and combined with Top-hat transform, Gaussian filter parameter selection is weakened, then a pyramid of difference of Gaussian is constructed by Gaussian convolution which based on light filtering and parameter weakening. Finally, SIFT algorithm is fused to generate GT-SIFT descriptor with good illumination invariance for feature point extraction and matching. The experimental results show that compared with the traditional algorithms, the improved algorithm under the condition of uneven illumination has better robustness, image feature point extraction and matching effect is better.

Key words: Scale Invariant Feature Transform（SIFT）, Gaussian filter, Top-hat transform, difference of Gaussian, descriptor

摘要：

为解决在光照不均匀情况下图像特征点提取算法表现效果不佳的问题，提出了一种改进的尺度不变特征转换（Scale Invariant Feature Transform，SIFT）算法抑制光照不均的影响。该方法在尺度空间构造中对输入的图像进行频域上的高斯高通滤波处理来滤除光照成分，并结合Top-hat变换弱化高斯滤波器参数选取难度，利用高斯卷积构建基于光照滤除与参数弱化的高斯差分金字塔，融合SIFT算法生成具有良好光照不变性的GT-SIFT描述子，进行特征点提取与匹配。实验结果表明，与传统算法相比改进算法在光照不均匀条件下具有更好的鲁棒性，图像特征点提取与匹配效果更好。

关键词: 尺度不变特征转换（SIFT）, 高斯滤波, Top-hat变换, 高斯差分, 描述子