Despite its ability to draw precise inferences from large and complex datasets, the use of data analytics in the field of condensed matter and materials sciences -- where vast quantities of complex metrology data are regularly generated -- has remained surprisingly limited. Specifically, such approaches could dramatically reduce the engineering complexities of devices that directly exploit the physical properties of materials. Here, we present a cyber-physical system for accurately estimating th...
This paper presents a novel unsupervised probabilistic model estimation of visual background in video sequences using a variational autoencoder framework. Due to the redundant nature of the backgrounds in surveillance videos, visual information of the background can be compressed into a low-dimensional subspace in the encoder part of the variational autoencoder, while the highly variant information of its moving foreground gets filtered throughout its encoding-decoding process. Our deep probabil...
The exploding availability of new data streams now enables insights to be garnered through the integration (fusion) of multiple data sources (modalities); however, currently, it remains difficult to predict a priori which multimodal data fusion (MMDF) methods and architectures will be best suited for a novel application, leading to trial-and-error approaches that are inefficient in both time and cost. Although MMDF strategies are being applied ad hoc in many different fields (e.g., healthcare, a...
This article covers a deep learning-based decision fusion approach for action or gesture recognition via simultaneous utilization of a depth camera and a wearable inertial sensor. The deep learning approach involves using a convolutional neural network (CNN) for depth images captured by a depth camera and a combination of CNN and long short–term memory network for inertial signals captured by a wearable inertial sensor, followed by a decision-level fusion. Due to the limited size of the training...
We present a novel approach for unsupervised detection of moving objects with nonsalient movements (e.g., rodents in their home cage). The proposed approach starts with separating the moving object from its background by modeling the background in a computationally efficient way. The background modeling is based on the assumption that background in natural videos lies on a low-dimensional subspace. We formulated and solved this problem using a low-rank matrix completion framework. To achieve com...