Browsing by Author "Cheng, Hsin-Min"

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Correlation between photoluminescence and varied growth pressure of well-aligned ZnO nanorods on fused silica substrate
(2007-11-01)
Yang, Song
;
Hsu, Hsu-Cheng
;
Liu, Wei-Ren
;
Cheng, Hsin-Min
;
Hsieh, Wen-Feng
Well-aligned ZnO nanorods with variable diameters were fabricated on fused silica substrates by the simple physical vapor deposition method with and without pre-deposited NiO particles. The diameter control of ZnO nanorods on the fused silica substrates was achieved by varying the growth pressure. The room-temperature photoluminescence spectra of the ZnO nanorods exhibit strong exciton emission at 3.25 eV that reveals good crystal quality. The exciton emission peak shifts toward 3.22 eV for the samples made at the larger growth pressure that is attributed to the influence of the electron-acceptor-level transition. The ZnO nanorods were examined by high resolution transmission electron microscopy and X-ray diffraction to show single crystal quality and preferentially c-axis alignment. Furthermore, we used scanning electron microscope to verify not only the size but also the growth mechanisms of ZnO nanorods. The results show a ZnO buffer layer formed between the flat top-facet nanorods and the substrate. In comparison with the samples without pre-deposited NiO particles on the substrate, the pre-deposited NiO particles act as nucleation centers for Zn vapor during the deposition process that improves the quality of the buffer layer. (C) 2007 Elsevier B.V. All rights reserved.
SINGLE-IMAGE 3-D DEPTH ESTIMATION FOR URBAN SCENES
(2013-01-01)
Cheng, Hsin-Min
;
Tseng, Chen-Yu
;
Hsin, Cheng-Ho
;
Wang, Sheng-Jyh
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電子工程學系及電子研究所
In this paper, we focus on recovering a 3-D depth map from a single image. Given an image of urban scene, we extract linear perspective information to establish the 3-D scene model. Unlike approaches which use only occlusion relationship between objects to estimate the relative depth of the image, we further combine the perspective geometry information with the occlusion relationship between objects. Besides, we propose the construction of depth gradient maps to represent the depth variation trend along the vertical and horizontal directions. The image is first partitioned into geometric components and initial depth gradient maps are generated based on the relative position between the vanishing point and the classified components. Incorporating main directions of vanishing lines and occlusion boundaries in the initial depth gradient maps, a refined depth map is obtained by using a CRF (conditional random field) model. We demonstrate that our approach can produce realistic relative depth maps for images of urban scenes.