Results 291 to 300 of about 3,919,259 (312)
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Detecting and predicting changes
Cognitive Psychology, 2009When required to predict sequential events, such as random coin tosses or basketball free throws, people reliably use inappropriate strategies, such as inferring temporal structure when none is present. We investigate the ability of observers to predict sequential events in dynamically changing environments, where there is an opportunity to detect true
Brown, Scott D., Steyvers, Mark
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Multiscale Change Point Detection
Theory of Probability & Its Applications, 2017zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Suvorikova, A., Spokoiny, V.
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Detecting Glacier Surface Changes Using Object-Based Change Detection
IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, 2018The concerns about global warming and climate change have produced widespread scientific interest in the response of glaciers. One of the important indicators of climate change is glacier change. The glaciers in Indian Himalaya have been retreating which can have several effects.
Kavita V. Mitkari +2 more
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Change: Detection and Modification
Applied Psychophysiology and Biofeedback, 2001Because human physiology is always changing, one of the challenges for those interested in biofeedback applications is how to establish techniques to determine if the biofeedback has altered the physiology. This paper explores some of the areas of concern and some solutions that have been reported.
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2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014
In traditional sparse recovery problems, the goal is to identify the support of compressible signals using a small number of measurements. In contrast, in this paper the problem of identification of a sparse number of statistical changes in stochastic phenomena is considered. This framework, which is newly introduced herein, is termed Compressed Change
Sarayanibafghi, Omid, Atia, George
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In traditional sparse recovery problems, the goal is to identify the support of compressible signals using a small number of measurements. In contrast, in this paper the problem of identification of a sparse number of statistical changes in stochastic phenomena is considered. This framework, which is newly introduced herein, is termed Compressed Change
Sarayanibafghi, Omid, Atia, George
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Thresholding for change detection
Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271), 2002Summary: Image differencing is used for many applications involving change detection. Although it is usually followed by a thresholding operation to isolate regions of change there are few methods available in the literature specific to (and appropriate for) change detection. We describe four different methods for selecting thresholds that work on very
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2009
In this chapter a wide range of change detection tools is addressed. They are grouped into methods suitable for optical and multispectral data, synthetic aperture radar (SAR) images, and 3D data. Optical and multispectral methods include unsupervised approaches, supervised and knowledge-based approaches, pixel-based and object-oriented approaches ...
Dekker, R.J. +9 more
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In this chapter a wide range of change detection tools is addressed. They are grouped into methods suitable for optical and multispectral data, synthetic aperture radar (SAR) images, and 3D data. Optical and multispectral methods include unsupervised approaches, supervised and knowledge-based approaches, pixel-based and object-oriented approaches ...
Dekker, R.J. +9 more
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2003
In time series analysis, autoregressive integrated moving average (ARIMA) models have found extensive use since the publication of Box and Jenkins (1976). For an introduction to the standard ARIMA modeling in S-PLUS, see S-PLUS Guide to Statistics. Regression models are also frequently used in finance and econometrics research and applications.
Eric Zivot, Jiahui Wang
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In time series analysis, autoregressive integrated moving average (ARIMA) models have found extensive use since the publication of Box and Jenkins (1976). For an introduction to the standard ARIMA modeling in S-PLUS, see S-PLUS Guide to Statistics. Regression models are also frequently used in finance and econometrics research and applications.
Eric Zivot, Jiahui Wang
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