Real-Time Monocular Pose Estimation of 3D Objects Using Temporally Consistent Local Color Histograms

Author(s):  
Henning Tjaden ◽  
Ulrich Schwanecke ◽  
Elmar Schomer
2021 ◽  
Vol 11 (1) ◽  
Author(s):  
T. Notake ◽  
T. Iyoda ◽  
T. Arikawa ◽  
K. Tanaka ◽  
C. Otani ◽  
...  

AbstractThe capability for actual measurements—not just simulations—of the dynamical behavior of THz electromagnetic waves, including interactions with prevalent 3D objects, has become increasingly important not only for developments of various THz devices, but also for reliable evaluation of electromagnetic compatibility. We have obtained real-time visualizations of the spatial evolution of THz electromagnetic waves interacting with a single metal micro-helix. After the micro-helix is stimulated by a broadband pico-second pulse of THz electromagnetic waves, two types of anisotropic re-emissions can occur following overall inductive current oscillations in the micro-helix. They propagate in orthogonally crossed directions with different THz frequency spectra. This unique radiative feature can be very useful for the development of a smart antenna with broadband multiplexing/demultiplexing ability and directional adaptivity. In this way, we have demonstrated that our advanced measurement techniques can lead to the development of novel functional THz devices.


2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Samy Bakheet ◽  
Ayoub Al-Hamadi

AbstractRobust vision-based hand pose estimation is highly sought but still remains a challenging task, due to its inherent difficulty partially caused by self-occlusion among hand fingers. In this paper, an innovative framework for real-time static hand gesture recognition is introduced, based on an optimized shape representation build from multiple shape cues. The framework incorporates a specific module for hand pose estimation based on depth map data, where the hand silhouette is first extracted from the extremely detailed and accurate depth map captured by a time-of-flight (ToF) depth sensor. A hybrid multi-modal descriptor that integrates multiple affine-invariant boundary-based and region-based features is created from the hand silhouette to obtain a reliable and representative description of individual gestures. Finally, an ensemble of one-vs.-all support vector machines (SVMs) is independently trained on each of these learned feature representations to perform gesture classification. When evaluated on a publicly available dataset incorporating a relatively large and diverse collection of egocentric hand gestures, the approach yields encouraging results that agree very favorably with those reported in the literature, while maintaining real-time operation.


Sensors ◽  
2015 ◽  
Vol 15 (6) ◽  
pp. 12410-12427 ◽  
Author(s):  
Hanguen Kim ◽  
Sangwon Lee ◽  
Dongsung Lee ◽  
Soonmin Choi ◽  
Jinsun Ju ◽  
...  

Author(s):  
Yidan Zhou ◽  
Jian Lu ◽  
Kuo Du ◽  
Xiangbo Lin ◽  
Yi Sun ◽  
...  

2019 ◽  
Vol 19 (6) ◽  
pp. 2338-2346 ◽  
Author(s):  
Aniket Gadwe ◽  
Hongliang Ren
Keyword(s):  

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