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Improvements in sensor technology and small, handheld wireless communication devices provide new opportunities for smart home applications to support independent living for elder care. However, with addition of new sensing technology in the smart home, intelligent methods are needed that process data collected by the sensors to recognize activities for monitoring the well-being of the home's inhabitants...
A significant proportion of Web traffic is now attributed to Web robots, and this proportion is likely to grow over time. These robots may threaten the security, privacy, functionality, and performance of a Web server due to their unregulated crawling behavior. Therefore, to assess their impact, it must be possible to accurately detect Web robot requests. Contemporary detection approaches, however,...
During the 2010-2011 winter season, we deployed seven geophones on a mountain outside of Davos, Switzerland and collected over 100 days of seismic data containing 385 possible avalanche events (33 confirmed slab avalanches). In this article, we describe our efforts to develop a pattern recognition workflow to automatically detect snow avalanche events from passive seismic data. Our initial workflow...
Inspired by the image de-noising techniques using learned dictionaries and sparse representation, we present a fabric defect detection scheme via sparse dictionary reconstruction. Fabric defects can be regarded as local anomalies against the relatively homogeneous texture background. Following from the flexibility of sparse representation, normal fabric samples can be efficiently represented using...
We introduce Convolutional Neural Support Vector Machines (CNSVMs), a combination of two heterogeneous supervised classification techniques, Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs). CNSVMs are trained using a Stochastic Gradient Descent approach, that provides the computational capability of online incremental learning and is robust for typical learning scenarios in...
The work presented in this paper proposes a new approach of using subspace grids for recognizing patterns in multidimensional data. The proposed approach addresses the two problems often associated with this task: i) curse of dimensionality ii) cases with small sample sizes. To handle the curse of dimensionality problem, this paper introduces subspace grids and shows how it can be employed for pattern...
Criminal activity in virtual worlds is becoming a major problem for law enforcement agencies. Forensic investigators are becoming interested in being able to accurately and automatically track people in virtual communities. In this paper a set of algorithms capable of verification and recognition of avatar faces with high degree of accuracy are described. Results of experiments aimed at within-virtual-world...
Despite of much research for facial expression recognition, recognizing facial expressions across different persons is still a challenging computer vision task. However, facial expression analysis seems naturally for human visual system. Motivated by visual biology, this paper proposes an invariant feature extraction method for subject-independent facial expression recognition. In particular, we extract...
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