问题描述
我已经创建，抓住关键点，并保存它到一个文本文件中的小应用SIFT。我用这抓住从标识信息（例如AT＆amp; T公司），并用它来比较与其他标志的图像。问题是，我的很多图片都认为，由于缩放，旋转，或照明不把它捡起来标识的变化。 ，我想知道是否有可能得到一组图片抓住它的关键点，并通过某种训练算法的运行它来提高检测。
I have created a small SIFT application that grabs the keypoints and saves it out to a text file. I am using this to grab information from a logo (say AT&T) and use that to compare against other images with that logo. The problem is many of my images have variations of the logo that, due to the scaling, rotation, or lighting it does not pick it up. I was wondering if it was possible to get a set of images, grab it's keypoints, and run it through some sort of training algorithm to enhance the detection.
我在网上搜索了培训SIFT关键点的方式，但他们都在某种博士纸张进入这一切的数学算法当中，说实话的，我投了，因为我还没有采取任何数学一流的一段时间。
I've searched online for ways of training the SIFT keypoints, but they are all in some sort of phd paper that goes into all this mathematical algorithms which, to be honest, throws me off as I haven't taken any math class for awhile.
如果任何人有任何建议或链接能够理解培训的工作方式还是什么需要做，以实现一个请让我知道。或者，如果任何人有这样做没有SIFT的一个简单的方法，然后我将不胜AP preciate其他形式的检测。以下是我已经尝试了列表：
If anyone has any advice or links to be able to understand how training works or what needs to be done to implement one please let me know. Or if anyone has a simpler means of doing this without SIFT then I would greatly appreciate other forms of detection. Below is a list of what I've tried:
- SURF
- 失败，因为它是返回结果无效
- 失败，因为我开始在2011年7月11日训练以100负图片100正面模型和它仍在运行为2011/7/19
- 无法为我会成倍需要创建的次数基于标识，它无法检测到图像中的任何
在此先感谢
推荐答案
一个简单的起点是收集一些AT＆放SIFT / SURF描述;吨标识，并使用 FLANN 他们。然后，采取测试图像，计算描述并做了搜索范围，并确定最近邻的距离等，并揣摩的亲密关系的度量。
A simple starting point would be to collect SIFT/SURF descriptors of several AT&T logos, and use FLANN on them. Then, take a test image, compute the descriptors and do a range search and determine the nearest-neighbor distance, etc. and try to figure out a metric of "closeness".
这篇关于在OpenCV的培训SIFT特征的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！