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基于sift的图像拼接技术
应用背景
SIFT特征匹配算法, 在没有先验知识条件下, 对旋转、尺度缩放、光照、视角变化等图像变化因素保持一定鲁棒性, 且对低重叠区域(30%左右的相似重叠区域)、部分遮挡(只出现在其中一张图上的物体)和噪声等因素也保持较好的可匹配性。可用于多尺度下的特征匹配,以及鲁棒性的图像拼接。
关键技术
1.高斯尺度空间极值检测
2.特征点位置的确定
3.特征点方向的分配
4.生成SIFT特征向量
5.图像拼接
6.图像融合
提取图像的sift特征,构造特征描述子,基于sift特征构造BBF-tree,根据描述子之间的欧式距离进行初步的匹配,然后进过鲁棒的Ransac估计出最佳运动模型H,然后用距离加权的方法在重叠区进行融合。
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- 积分:1
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实用.....经本人检测...............................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................
- 2022-08-17 03:26:34下载
- 积分:1
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- 2023-04-03 14:50:03下载
- 积分:1
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