AICTC Research Center, Department of Computer Engineering, Sharif University of Technology, Tehran 1478619338, Iran.
IEEE Trans Image Process. 2011 Dec;20(12):3580-91. doi: 10.1109/TIP.2011.2156800. Epub 2011 May 19.
This paper presents a novel algorithm for matching image interest points. Potential interest points are identified by searching for local peaks in Difference-of-Gaussian (DoG) images. We refine and assign rotation, scale and location for each keypoint by using the SIFT algorithm . Pseudo log-polar sampling grid is then applied to properly scaled image patches around each keypoint, and a weighted adaptive lifting scheme transform is designed for each ring of the log-polar grid. The designed adaptive transform for a ring in the reference keypoint and the general non-adaptive transform are applied to the corresponding ring in a test keypoint. Similarity measure is calculated by comparing the corresponding transform domain coefficients of the adaptive and non-adaptive transforms. We refer to the proposed versatile system of Rotation And Scale Invariant Matching as RASIM. Our experiments show that the accuracy of RASIM is more than SIFT, which is the most widely used interest point matching algorithm in the literature. RASIM is also more robust to image deformations while its computation time is comparable to SIFT.
本文提出了一种新的图像兴趣点匹配算法。通过在高斯差分（DoG）图像中搜索局部峰值来识别潜在的兴趣点。我们使用 SIFT 算法对每个关键点进行细化和分配旋转、缩放和位置。然后对每个关键点周围的适当缩放图像块应用伪对数极采样网格，并为对数极网格的每个环设计加权自适应提升方案变换。在参考关键点中的一个环上设计的自适应变换和一般非自适应变换应用于测试关键点中的相应环。通过比较自适应变换和非自适应变换的对应变换域系数来计算相似性度量。我们将所提出的旋转和尺度不变匹配的通用系统称为 RASIM。我们的实验表明，RASIM 的准确性超过了文献中最广泛使用的兴趣点匹配算法 SIFT。RASIM 对图像变形也更具鲁棒性，而其计算时间与 SIFT 相当。
