ROI-Based Dual-Camera HDR Synthesis
碩士 === 國立中正大學 === 電機工程研究所 === 103 === Most of existing methods acquired high quality images from a sequence of differently exposed images that can be differentiated either high dynamic range (HDR) [1] or exposure fusion [2]. Most of them are focused on global HDR image generation for static scenes a...
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ndltd-TW-103CCU004420442016-08-19T04:10:18Z http://ndltd.ncl.edu.tw/handle/53947693138170159038 ROI-Based Dual-Camera HDR Synthesis 基於ROI之雙鏡頭高動態範圍合成 Yu-Sheng Lin 林于盛 碩士 國立中正大學 電機工程研究所 103 Most of existing methods acquired high quality images from a sequence of differently exposed images that can be differentiated either high dynamic range (HDR) [1] or exposure fusion [2]. Most of them are focused on global HDR image generation for static scenes and assume the input images are perfectly aligned. However, these methods are not effective to cope with practical scenario. This paper presents an effective and real-time ROI-base image alignment technique for two different exposed images automatic-exposed and complementary-exposed with different viewpoint. In previous work, SIFT (Scale-invariant feature transform) can accurately match the feature points from two different images with the same exposure time. But it will be hard to match the correspondence feature points in HDR synthesis system due to the different exposed images. In this paper, we propose a matching method that overcomes the cause of exposure limits by taking advantage of consistent luminance distributions. In previous work, it is well-known with great time consumption because of global alignment. Therefore we utilize the camera geometric relations with our matching method to replace the global alignment in order to decrease the candidates of matching region. Our experiments demonstrate that our results are effective and high-quality image alignment for HDR synthesis system. Ching-Wei Yeh 葉經緯 2015 學位論文 ; thesis 45 en_US |
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碩士 === 國立中正大學 === 電機工程研究所 === 103 === Most of existing methods acquired high quality images from a sequence of differently exposed images that can be differentiated either high dynamic range (HDR) [1] or exposure fusion [2]. Most of them are focused on global HDR image generation for static scenes and assume the input images are perfectly aligned. However, these methods are not effective to cope with practical scenario. This paper presents an effective and real-time ROI-base image alignment technique for two different exposed images automatic-exposed and complementary-exposed with different viewpoint. In previous work, SIFT (Scale-invariant feature transform) can accurately match the feature points from two different images with the same exposure time. But it will be hard to match the correspondence feature points in HDR synthesis system due to the different exposed images. In this paper, we propose a matching method that overcomes the cause of exposure limits by taking advantage of consistent luminance distributions. In previous work, it is well-known with great time consumption because of global alignment. Therefore we utilize the camera geometric relations with our matching method to replace the global alignment in order to decrease the candidates of matching region. Our experiments demonstrate that our results are effective and high-quality image alignment for HDR synthesis system.
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author2 |
Ching-Wei Yeh |
author_facet |
Ching-Wei Yeh Yu-Sheng Lin 林于盛 |
author |
Yu-Sheng Lin 林于盛 |
spellingShingle |
Yu-Sheng Lin 林于盛 ROI-Based Dual-Camera HDR Synthesis |
author_sort |
Yu-Sheng Lin |
title |
ROI-Based Dual-Camera HDR Synthesis |
title_short |
ROI-Based Dual-Camera HDR Synthesis |
title_full |
ROI-Based Dual-Camera HDR Synthesis |
title_fullStr |
ROI-Based Dual-Camera HDR Synthesis |
title_full_unstemmed |
ROI-Based Dual-Camera HDR Synthesis |
title_sort |
roi-based dual-camera hdr synthesis |
publishDate |
2015 |
url |
http://ndltd.ncl.edu.tw/handle/53947693138170159038 |
work_keys_str_mv |
AT yushenglin roibaseddualcamerahdrsynthesis AT línyúshèng roibaseddualcamerahdrsynthesis AT yushenglin jīyúroizhīshuāngjìngtóugāodòngtàifànwéihéchéng AT línyúshèng jīyúroizhīshuāngjìngtóugāodòngtàifànwéihéchéng |
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1718377720899960832 |