背景故事,在我的国家,每个银行面额都有其创始人的照片:
(来源:76.my)
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我想通过冲浪检测器找到这两个图像之间的相似性。系统将通过这两个图像进行训练。用户将通过网络摄像头呈现底部图片或顶部图片,并使用它们之间的相似度得分来找到其面额值。
My pseudocode:
1.Detect keypoints and the corresponding descriptors of both the images via surf detector and descriptor .
2.a.Calculate the matching vector between the query and each of the trained example .Find number of good matches / total number of matches for each image .
2.b.OR Apply RANSAC algorithm and find the highest number of closest pair between query and training algorithm
3.The one having the higher value will have higher score and better similarity.
我的方法是否足够合理,或者是否有其他方法可以找到两个图像之间的相似性,其中查询图像将经历各种变换。我已经为此寻找解决方案,例如寻找曼哈顿距离或寻找相关性,但是它们都不足以解决这个问题。
是的,你的做法是正确的
1) You create a training set and store all its feature-points .
2) Perform ratio test for matches with the query and train feature-points.
3) Apply ransac test and draw matches (apply homograpghy if you want highlight the detected note).
这篇论文可能会有帮助,他们正在使用 SIFT 做类似的事情
你的算法看起来不错,但你有更多可以利用的信息。我将为您提供一份信息列表,您可以使用这些信息来进一步改善您的结果:
1. Location of the part where denominations are written on the image.
2. Information about how denominations are written - Script knowledge.
3. Homo-graphic information as you know the original image and The observed image
利用以上所有信息来改善结果。