Experimental demonstration of high spatial coherence of laser-triggered field emitters
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: 2014DOI: 10.1109/IROS.2014.6942984
Keyword(s): control engineering computing,graph theory,image classification,image colour analysis,image sensors,inference mechanisms,robot vision,shape recognition,unsupervised learning,Kinect-like sensor,RGB-augmented 3D point clouds,RGBD images,cluttered indoor scenes,energy minimization inference procedure,hierarchical graph constellation models,hierarchical graphical model,object class learning,object shape categorization,part-based shape categorization,shape category,shape detailedn
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Experimental demonstration of high spatial coherence of laser-triggered field emitters
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| contributor author | Ehberger, D. , Hammer, J. , Hommelhoff, P. | |
| date accessioned | 2020-03-12T23:28:29Z | |
| date available | 2020-03-12T23:28:29Z | |
| date issued | 2014 | |
| identifier other | 6894735.pdf | |
| identifier uri | https://libsearch.um.ac.ir:443/fum/handle/fum/1112989?locale-attribute=en | |
| format | general | |
| language | English | |
| publisher | IEEE | |
| title | Experimental demonstration of high spatial coherence of laser-triggered field emitters | |
| type | Conference Paper | |
| contenttype | Metadata Only | |
| identifier padid | 8281980 | |
| subject keywords | control engineering computing | |
| subject keywords | graph theory | |
| subject keywords | image classification | |
| subject keywords | image colour analysis | |
| subject keywords | image sensors | |
| subject keywords | inference mechanisms | |
| subject keywords | robot vision | |
| subject keywords | shape recognition | |
| subject keywords | unsupervised learning | |
| subject keywords | Kinect-like sensor | |
| subject keywords | RGB-augmented 3D point clouds | |
| subject keywords | RGBD images | |
| subject keywords | cluttered indoor scenes | |
| subject keywords | energy minimization inference procedure | |
| subject keywords | hierarchical graph constellation models | |
| subject keywords | hierarchical graphical model | |
| subject keywords | object class learning | |
| subject keywords | object shape categorization | |
| subject keywords | part-based shape categorization | |
| subject keywords | shape category | |
| subject keywords | shape detailedn | |
| identifier doi | 10.1109/IROS.2014.6942984 | |
| journal title | acuum Nanoelectronics Conference (IVNC), 2014 27th International | |
| filesize | 965920 | |
| citations | 0 |