论文标题
最大程度地减少最大化推理的采集 - 关于打印错误检测的演示
Minimizing Acquisition Maximizing Inference -- A demonstration on print error detection
论文作者
论文摘要
是否可以在不查看图像中检测图像中的功能？已知图像在小波和其他类似的变换中具有更稀疏的表示。压缩传感是一种技术，它通过进行很少的随机线性测量（M）来提出同时采集和压缩任何信号。重建的质量直接与M有关，M对于可靠的恢复应高于一定阈值。由于这些测量值可以使用纯粹的分析方法，例如基础追踪，匹配追踪，迭代阈值等，在忠实地重建信号，因此我们可以确保这些压缩样品包含有关（图像）信号中包含的任何相关宏观特征的足够信息。因此，如果我们选择故意获得较少数量的测量值 - 为了阻止可理解的重建的可能性，但足够高以推断图像中是否存在相关特征 - 我们可以在保留其隐私时实现准确的图像分类。通过打印错误检测问题，可以证明可以在实践中实现这种新型系统。
Is it possible to detect a feature in an image without ever looking at it? Images are known to have sparser representation in Wavelets and other similar transforms. Compressed Sensing is a technique which proposes simultaneous acquisition and compression of any signal by taking very few random linear measurements (M). The quality of reconstruction directly relates with M, which should be above a certain threshold for a reliable recovery. Since these measurements can non-adaptively reconstruct the signal to a faithful extent using purely analytical methods like Basis Pursuit, Matching Pursuit, Iterative thresholding, etc., we can be assured that these compressed samples contain enough information about any relevant macro-level feature contained in the (image) signal. Thus if we choose to deliberately acquire an even lower number of measurements - in order to thwart the possibility of a comprehensible reconstruction, but high enough to infer whether a relevant feature exists in an image - we can achieve accurate image classification while preserving its privacy. Through the print error detection problem, it is demonstrated that such a novel system can be implemented in practise.
