Compressed Sensing for Computer Vision

计算机视觉（CV）的目标是通过处理图像和视频进行自动视觉识别。CV明显落后于人类视觉能力。然而，近年来在商业应用方面的一些成功案例值得关注：人脸检测与识别、光学字符识别、Kinect游戏、指纹识别等，因此CV在理论和实践方面的发展存在着大量的研究机会。计算机视觉研究成功的主要障碍似乎是缺乏计算机算法（AI /机器学习）可以轻松处理的图像的合适数值表示。特别是，图像可以是一百万或更多像素的集合，这对于算法有意义地处理来说是压倒性的。艺术将以压缩形式表示这些像素，这对算法是有用的。在这项研究计划中，我建议看看基于压缩感知（CS）的CV图像表示。CS是信号处理研究中的一个迅速兴起的领域，它已经成为信号处理应用中的一个范式转变，因为使用CS，一些信号可以使用比经典理论预测的少得多的样本来重建。CS的一个突出应用示例是快速磁共振成像，其中信号采集的时间已经显著减少。在这个研究建议中，我解释了在CV应用程序中使用CS的理由。拟议研究的潜在影响是CV方法向有限内存设备（如可穿戴设备）的扩散以及对CV图像表示的理解。该计划还计划培养4名博士和2名硕士学生在国家的最先进的简历研究。

The goal of computer vision (CV) is automated visual recognition by processing images and videos. CV significantly lags behind human visual capability. However, a few recent success stories in commercial applications are noteworthy: face detection and recognition, optical character recognition, Kinect gaming, fingerprint verification and so on. Thus, ample research opportunity exists in the progression of CV in its theory and practice. The principal roadblock to the success of computer vision research seem to be the lack of a suitable numerical representation of images that the computer algorithms (AI / machine learning) can easily process. In particular, an image could be a collection of a million or more pixels, which is overwhelming for an algorithm to process meaningfully. The art will be representing these pixels in a compressed form, which would be useful for the algorithms. In this research program, I propose to look at compressed sensing (CS)-based representation of images for CV. CS is a rapidly emerging area in signal processing research that has acted as a paradigm shift in signal processing applications, because using CS, some signals can be reconstructed using far fewer samples than what the classical theory predicts. A prominent application example of CS is rapid magnetic resonance imaging, where the time of signal acquisition has been significantly reduced. In this research proposal, I explain the rational behind using CS within CV applications. Potential impact of the proposed research is the proliferation of CV methods to limited memory devices, such as wearable devices and understanding of the image representation for CV. The program also plans to train 4 PhD and 2 MSc students in the state-of-the-art CV research.