Compressed Sensing for Computer Vision

计算机视觉的目标是通过处理图像和视频来实现自动视觉识别。CV明显落后于人类的视觉能力。然而，最近在商业应用中的一些成功案例值得注意：人脸检测和识别、光学字符识别、Kinect游戏、指纹验证等。因此，在其理论和实践的发展过程中，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(人工智能/机器学习)可以轻松处理的合适的图像数字表示。具体地说，一幅图像可能是一百万或更多像素的集合，这对于一个算法进行有意义的处理来说是压倒性的。这项技术将以压缩的形式表示这些像素，这将对算法有用。在这个研究项目中，我建议考虑基于压缩感知(CS)的图像表示以用于CV。CS是信号处理研究中一个迅速崛起的领域，它已经成为信号处理应用中的一种范式转换，因为使用CS可以使用比经典理论预测的更少的样本来重建一些信号。CS的一个突出应用例子是快速磁共振成像，信号采集的时间大大减少。在这个研究方案中，我解释了在CV应用程序中使用CS背后的原因。这项研究的潜在影响是CV方法的扩散到有限的记忆设备，如可穿戴设备和对CV的图像表示的理解。该项目还计划培训4名博士生和2名硕士研究生进行最先进的简历研究。

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.