Micro Signal Extraction and Analytics
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This dissertation studies the extraction of signals that have smaller magnitudes—typically one order of magnitude or more—than the dominating signals, or the extraction of signals that have a smaller topological scale than what conventional algorithms resolve. We name such a problem the micro signal extraction problem. The micro signal extraction problem is challenging due to the relatively low signal strength. In terms of relative magnitude, the micro signal of interest may very well be considered as one signal within a group of many types of tiny, nuisance signals, such as sensor noise and quantization noise. This group of nuisance signals is usually considered as the “noisy,” unwanted component in contrast to the “signal” component dominating the multimedia content. To extract the micro signal that has much smaller magnitude than the dominating signal and simultaneously to protect it from being corrupted by other nuisance signals, one usually has to tackle the problem with extra caution: the modeling assumptions behind a proposed extraction algorithm needs to be closely calibrated with the behavior of the multimedia data. In this dissertation, we tackle three micro signal extraction problems by synergistically applying and adapting signal processing theories and techniques. In the first part of the dissertation, we use mobile imaging to extract a collection of directions of microscopic surfaces as a unique identifier for authentication and counterfeit detection purposes. This is the first work showing that the 3-D structure at the microscopic level can be precisely estimated using techniques related to the photometric stereo. By enabling the mobile imaging paradigm, we have significantly reduced the barriers for extending the counterfeit detection system to end users. In the second part of the dissertation, we explore the possibility of extracting the Electric Network Frequency (ENF) signal from a single image. This problem is much more challenging compared to its audio and video counterparts, as the duration and the magnitude of the embedded signal are both very small. We investigate and show how the detectability of the ENF signal changes as a function of the magnitude of the embedded ENF signal. In the last part of the dissertation, we study the problem of heart-rate from fitness exercise videos, which is challenging due to the existence of fitness motions. We show that a highly precise motion compensation scheme is the key to a reliable heart-rate extraction system.