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See detailFlexible body scanning without template models
Munoz-Salinas, Rafael; Sarmadi, Hamid; Cazzato, Dario UL et al

in Signal Processing (2019), 154

The apparition of low-cost depth cameras has lead to the development of several reconstruction methods that work well with rigid objects, but tend to fail when used to manually scan a standing person ... [more ▼]

The apparition of low-cost depth cameras has lead to the development of several reconstruction methods that work well with rigid objects, but tend to fail when used to manually scan a standing person. Specific methods for body scanning have been proposed, but they have some ad-hoc requirements that make them unsuitable in a wide range of applications: they either require rotation platforms, multiple sensors and a priori template model. Scanning a person with a hand-held low-cost depth camera is still a challenging unsolved problem. This work proposes a novel solution to easily scan standing persons by combining depth information with fiducial markers without using a template model. In our approach, a set of markers placed in the ground are used to improve camera tracking by a novel algorithm that fuses depth information with the known location of the markers. The proposed method analyzes the video sequence and automatically divides it into fragments that are employed to build partial overlapping scans of the subject. Then, a registration step (both rigid and non-rigid) is applied to create a final mesh of the scanned subject. The proposed method has been compared with the state-of-the-art KinectFusion [1], ElasticFusion [2], ORB-SLAM [3, 4], and BundleFusion [5] methods, exhibiting superior performance. [less ▲]

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See detailNormalized LMS Algorithm and Data-selective Strategies for Adaptive Graph Signal Estimation
Jorge Mendes Spelta, Marcelo; Alves Martins, Wallace UL

in Signal Processing (2019)

This work proposes a normalized least-mean-squares (NLMS) algorithm for online estimation of bandlimited graph signals (GS) using a reduced number of noisy measurements. As in the classical adaptive ... [more ▼]

This work proposes a normalized least-mean-squares (NLMS) algorithm for online estimation of bandlimited graph signals (GS) using a reduced number of noisy measurements. As in the classical adaptive filtering framework, the resulting GS estimation technique converges faster than the least-mean-squares (LMS) algorithm while being less complex than the recursive least-squares (RLS) algorithm, both recently recast as adaptive estimation strategies for the GS framework. Detailed steady-state mean-squared error and deviation analyses are provided for the proposed NLMS algorithm, and are also employed to complement previous analyses on the LMS and RLS algorithms. Additionally, two different time-domain data-selective (DS) strategies are proposed to reduce the overall computational complexity by only performing updates when the input signal brings enough innovation. The parameter setting of the algorithms is performed based on the analysis of these DS strategies, and closed formulas are derived for an accurate evaluation of the update probability when using different adaptive algorithms. The theoretical results predicted in this work are corroborated with high accuracy by numerical simulations. [less ▲]

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See detailDistributed Power Control with Received Power Constraints for Time-Area-Spectrum Licenses
Pérez-Neira, Ana I.; Veciana, Joaquim M.; Vazquez, Miguel A. et al

in Signal Processing (2016)

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See detailDistributed Power Control with Received Power Constraints for Time-Area-Spectrum Licenses
Pérez-Neira, Ana; Veciana, Joaquim; Vázquez, Miguel Angel et al

in Signal Processing (2015)

Detailed reference viewed: 70 (4 UL)