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    Multi-timescale boosting for efficient and improved event camera face pose alignment
    (Academic Press Inc., 2023) Arman Savran
    The success of event camera (EC) vision in certain types of applications has been steadily shown thanks to energy-efficient sparse sensing high dynamic range and extremely high temporal resolution. However the utilization of ECs for facial processing tasks has remained rather limited. To enable high energy efficiency for large face pose alignment which is a crucial facial pre-processing stage we aim at leveraging EC by effective adaptation of the processing rate proportional to facial movement intensity. For this purpose we propose a novel alternative to the commonly employed constant time frame and event count frame strategies which combines their advantages and provides the benefits of supervised learning. This is realized by a multi-timescale boosting framework that can generate highly sparse pose-events at a variable rate via detection-based online timescale selection. Although detectors of multiple scales with boosted sensitivities operate as a cascade our method provides minimal delay essential for real-time applications. Comprehensive evaluations show that the proposed multi-timescale processing substantially improves the performance–efficiency trade-off over single-timescale frames and markedly over event count frames. Mega-floating-point-operations-per-second ranges from 2.5 at the moderate motion clips to 6.5 at the intense motion clips with negligible computation in the absence of activity. Also alignment errors are considerably reduced by online selection of small timescales at fast head motion and of bigger timescales at slower motion or local activity of lips and eyes. Being orthogonal and complementary to spatial domain techniques the proposed approach can also be conveniently integrated with future advances for further performance/efficiency improvements or for alignment extensions. © 2023 Elsevier B.V. All rights reserved.
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    Citation - WoS: 3
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    Multiple kernel fusion: A novel approach for lake water depth modeling
    (Academic Press Inc., 2023) Mir Jafar Sadegh Safari; Shervin Rahimzadeh Arashloo; Babak Vaheddoost; Rahimzadeh Arashloo, Shervin; Vaheddoost, Babak; Safari, Mir Jafar Sadegh; Arashloo, Shervin Rahimzadeh
    Multiple kernel fusion (MKF) refers to the task of combining multiple sources of information in the Hilbert space for improved performance. Very often the combined kernel is formed as a linear composition of multiple base kernels where the combination weights are learned from the data. As the first application of an MKF approach in hydrological modeling lake water depth as one of the pivot factors in the reservoir analysis is simulated by considering different hydro-meteorological variables. The role of each individual input parameter is initially investigated by applying a kernel regression approach. We then illustrate the utility of an MKF formalism which learns kernel combination of weights to yield an optimal composition for kernel regression. A set of 40-year data collected from 27 groundwater and streamflow stations and 7 meteorological stations for precipitation and evaporation parameters in the vicinity of Lake Urmia are utilized for model development. Both visual and quantitative statistical performance criteria illustrate a superior performance for the MKF approach compared to kernel ridge regression (KRR) the support vector regression (SVR) back propagation neural network (BPNN) and auto regressive (AR) models. More specifically while each individual input parameter fails to provide an accurate prediction for lake water depth modeling an optimal combination of all input parameters incorporating the groundwater level streamflow precipitation and evaporation via a multiple kernel learning approach enhances the predictive performance of the model accuracy in the multiple scenarios. The promising results (RMSE = 0.098 m, R2 = 0.987, NSE = 0.986) may motivate the application of a MKF approach towards solving alternative and complex hydrological problems. © 2022 Elsevier B.V. All rights reserved.