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Rapid detection of collapsed built-up structures is crucial for disaster mitigation in earthquake-affected areas. This paper proposes a method on detecting collapsed built-up structures using only post-event images. Its essence is to track and detect debris of collapsed built-up structures using features obtained through the derivative morphological profile. Experiments are carried out on images of...
This paper looks at the problem of detecting a particular type of social behavior in discussions: attempts to establish credibility as an authority on a particular topic. Using maximum entropy modeling, we explore questions related to feature extraction and turn vs. discussion-level modeling in experiments with online discussion text given only a small amount of labeled training data. We also introduce...
Nowadays large populations worldwide are suffering from eye diseases such as astigmatism, myopia, and hyperopia which are caused by ophthalmologically refractive errors. This paper presents an effective approach to computer aided diagnosis of such eye diseases due to ophthalmologically refractive errors. The proposed system consists of two major steps: (1) image segmentation and geometrical feature...
Fault diagnosis and failure prognosis are essential techniques to improve the safety of many mechanical systems. However, vibration signals are often corrupted by noise and, therefore, the performance of diagnostic/prognostic routines is degraded. In this paper, a novel de-noising structure is proposed and applied to vibration signals collected from a seeded-fault testbed of the main gearbox of a...
Based on the kernel principal component regression (KPCR) recently proposed in the literature, a new kernel auto-associator (KAA) model is proposed for classification and novelty detection. For face recognition problem, KAA model can efficiently characterize each subject thus offering a good recognition performance. Steming from the Principal component regression (PCR). a simple technique using principal...
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