帮助 Android 增强现实
我只需要一些关于如何检测标记并制作输出文本的指南..例如：带有狗图像的标记，当检测到时，我在文本字段中有一个输出文本“DOG”..有人可以帮助我吗按照我的想法？哦，顺便说一句，对于我的想法，使用 nyartoolkit 或 andar 哪个更有效？谢谢：）需要帮助..！
i just need some guide on how to detect a marker and make an output text.. for ex: a marker with an image of a dog , when detected, i have an output text "DOG" in a textfield .. can someone help me with my idea? oh, btw which one is more effective to use nyartoolkit or andar for my idea?thanks:) need help..!
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您正在寻找的不是增强现实，而是对象识别。 AR 主要关注呈现叠加在现实世界上的数据，因此每一帧都致力于计算以确定对象相对于相机的位置。如果您不想使用这些数据，AR 库可能效率低下。也就是说...
AR 标记跟踪库通常通过角等突出特征来查找标记，并且可以通过标记内部或标记边框中编码的二进制模式来区分标记。如果您对在标记边框中编码“狗”部分感到满意，可以使用一些库，例如 高通的 AR 开发套件。该库和 Metaio 的 Unifeye mobile 还可以对预定义图像进行自然特征跟踪。如果您对能够识别预先定义的一张或多张特定的狗图像感到满意，那么其中任何一个都应该没问题。您可能需要处理您的狗图像以获得它们可以识别和跟踪的良好特征。自然物体可能会产生问题。
一般物体识别（能够识别任何事先未知的狗的图片）仍然是一个研究课题。有一些方法，但它们大多是计算密集型的，并且大多数移动解决方案都涉及将大量计算卸载到服务器。然而，简单轮廓草图的识别更容易处理，有一篇很棒的论文，名为“移动增强现实的形状识别和姿势估计”（我在网上找不到副本，但是 IEEE 链接位于），它使用轮廓来识别对象 - 这足够轻，可以在移动设备上运行（并且这是纯粹的天才）。
What you're looking for isn't augmented reality, it's object recognition. AR is chiefly concerned with presenting data overlaid on the the real world, so computation is devoted each frame to determining the position relative to the camera of the object. If you don't intent to use this data, AR libraries may be an inefficient. That said...
AR marker tracking libraries usually find markers by prominent features like corners, and can distinguish markers by binary patters encoded inside the marker, or in the marker's borders. If you're happy with having the "dog" part encoded in the border of a marker, there are libraries you can use like Qualcomm's AR development kit. This library, and Metaio's Unifeye mobile can also do natural feature tracking on pre-defined images. If you're happy with being able to recognize one specific image or images of dogs that you have defined in advance, either of these should be ok. You might have to manipulate your dog images to get good features they can identify and track. Natural objects can be problematic.
General object recognition (being able to recognize a picture of any dog, not known beforehand) is still a research topic. There are approaches, but they're mostly very computationally intensive, and most mobile solutions involve offloading the serious computation to a server. Recognition of simple outline sketches however is more tractable, there's a great paper called "Shape recognition and pose estimation for mobile augmented reality" (I can't find a copy online, but the IEEE link is here) that uses contours to identify objects - this is light enough to run on a mobile (and it's pure genius).