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New-Folder-(4)
- In this research two automatic video annotation techniques are considered. The first technique uses ontology to reduce the semantic gap during video retrieval and other performs a group based image retrieval using video files. The proposed algorithm
image-matching--
- 首先对图像 进行高斯和 Wallis 滤波处理,然后采用简化 SIFT 算法进行特征点提取,最后通过特征点双向 匹配方法实现图像的精确匹配。通过对缺陷版图图像的试验验证了该方法具有匹配点数量 多、准确率高、无重复点等优点。-First of all Gaussian image filtering and Wallis and simplified SIFT feature point extraction algorithm, and finally through the fea
image-matching-
- 针对 128 维 SIFT 特 征向量,采用距离匹配和余弦相似度匹配相结合的测度方法,利用特征点方向一致性进一步降低误匹配率 . 实验结 果表明:改进算法对图像的缩放、旋转、光照、噪声和小尺度的视角变换均有较好的匹配效果 . 与原算法相比,在保 证匹配点数和匹配时间的基础上,改进算法对旋转、缩放、噪声模糊和光照变换的误匹配率平均降低 10%~20% , 对于小尺度的视角变换,误匹配率平均降低 5%. -For 128-dimensi
quanjingtu
- 采用sift算法的全景图拼接技术的改进,讲解的很详细。如果pdf打不开,就用CAJvieweru-Improved algorithm using sift panorama stitching techniques, explain in great detail. If pdf open, use CAJvieweru
based-on
- 提出改进的SIFT算法,该算法可提高运算速度和匹配准确率,增强算法的鲁棒性。-Improved SIFT proposed algorithm can improve the computational speed and matching accuracy, enhance the robustness of the algorithm.
final_tejas
- a presentation regardin image mosaic using sift and surf algorithm
sift_method
- SIFT(Scale-invariant feature transform)是一种检测局部特征的算法,该算法通过求一幅图中的特征点(interest points,or corner points)及其有关scale 和 orientation 的描述子得到特征并进行图像特征点匹配,获得了良好效果-SIFT (Scale-invariant feature transform) is a local feature detection algorithm by finding a pictur