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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 趙鍵哲 | |
| dc.contributor.author | Yu-Lin Chen | en |
| dc.contributor.author | 陳昱霖 | zh_TW |
| dc.date.accessioned | 2021-06-16T05:08:25Z | - |
| dc.date.available | 2019-09-05 | |
| dc.date.copyright | 2014-09-05 | |
| dc.date.issued | 2014 | |
| dc.date.submitted | 2014-08-19 | |
| dc.identifier.citation | Banz, C., Pirsch, P., and Blume H., 2012. Evaluation of penalty functions for semi-global matching cost aggregation, International Archives of Photogrammetry and Remote Sensing, ISPRS 2012 Congress, Melbourne, Australia, 39, Part B3:1-6.
Bleyer, M. and Gelautz, M., 2005. A layered stereo matching algorithm using image segmentation and global visibility constraints, ISPRS Journal of Photogrammetry and Remote Sensing, 59(3): 128-150. Birchfield, S. and Tomasi, C., 1998. Depth discontinuities by pixel to-pixel stereo, In Proceedings of the Sixth IEEE International Conference on Computer Vision, 35(3): 269-293. Fua, P., 1993. A parallel stereo algorithm that produces dense depth maps and preserves image features, Machine vision and applications, 6(1): 35-49. Gerke, M., 2009. Dense matching in high resolution oblique airborne images, CMRT09 Object Extraction for 3D City Models, Road Databases and Traffic Monitoring - Concepts, Algorithms and Evaluation , ISPRS 2009 Congress, 38.Part 3/W4, Paris, France, pp. 77-82. Grun A., 1985. Adaptive least squares correlation - a powerful image matching technique, South African Journal of Photogrammetry, Remote Sensing and Cartography, 14(3): 175-187. Haala, N., 2011. Multiray photogrammetry and dense image matching. Photogrammetric Week 2011:185-195. Hirschmuller, H., 2005. Accurate and Efficient Stereo Processing by Semi-Global Matching and Mutual Information, IEEE Conf. on Computer Vision and Pattern Recognition, 2: 807-814. Hirschmuller, H., 2008. Stereo processing by semiglobal matching and mutual information, IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(2):328-341. Hirschmuller, H. and Scharstein, D., 2009. Evaluation of Stereo Matching Costs on Images with Radiometric Differences, IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(9): 1582-1599. Hirschmuller, H., 2011. Semi-Global Matching-Motivation, Developments and Applications, Photogrammetric Week 2011:173-184. Kim, J., Kolmogorov, V. and Zabih, R., 2003. Visual correspondence using energy minimization and mutual information, International Conference on Computer Vision Congress, pp.1033-1040. Klaus, A., Sormann, M., & Karner, K., 2006. Segment-based stereo matching using belief propagation and a self-adapting dissimilarity measure, ICPR on Pattern Recognition, 3: 15-18. Kolmogorov, V., and Zabih, R., 2001. Computing visual correspondence with occlusions using graph cuts, In Computer Vision, 2001. ICCV 2001. Proceedings. Eighth IEEE International Conference on, 2: 508-515) Lei, C., Selzer, J., and Yang, Y. H., 2006. Region-tree based stereo using dynamic programming optimization. IEEE Conf. on Computer Vision and Pattern Recognition, 2: 2378-2385. Michael, M., Salmen, J., Stallkamp, J., and Schlipsing, M., 2013. Real-Time Stereo Vision: Optimizing Semi-Global Matching, In Proceedings of the IEEE Intelligent Vehicles Symposium, pp.1197-1202. Sun, J., Li, Y., Kang, S. B., and Shum, H. Y., 2005. Symmetric stereo matching for occlusion handling. IEEE Conf. on Computer Vision and Pattern Recognition, 2: 399-406. Van Meerbergen, G., Vergauwen, M., Pollefeys M. and Van Gool, L., 2002. A Hierarchical Symmetric Stereo Algorithm using Dynamic Programming, International Journal of Computer Vision, 47(1-3):275-285. Veksler, O., 2005, Stereo correspondence by dynamic programming on a tree, IEEE Conf. on Computer Vision and Pattern Recognition,2: 384-390. Viola, P. and Wells, W. M., 1997. Alignment by maximization of mutual information, International Journal of Computer Vision, 24(2):137-154. Yang, Q., Wang, L., Yang, R., Wang, S., Liao, M., andNister, D., 2006. Stereo Matching with Color-Weighted Correlation, Hierachical Belief Propagation and Occlusion Handling. IEEE Conf. on Computer Vision and Pattern Recognition, 2: 2347-2354. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/55783 | - |
| dc.description.abstract | 近年來利用影像產製高密度點雲為攝影測量和電腦視覺領域中之重要議題。基於互資訊之半全域匹配法利用多路徑進行匹配值加總，提升逐像元匹配運算效率。然而，為減少匹配錯誤，利用補償值於匹配值加總時進行平滑約制，因此路徑上的平滑約制補償值設定成為決定視差成果的關鍵。本研究分析補償值對半全域影像匹配之影響，並以補償值函數最小值設定模式進行補償函數特性分析，以視差影像之一致性像元數趨勢提供補償值設定參考，於實驗室影像和實際影像中探討作業參數擬定。 | zh_TW |
| dc.description.abstract | Over the last few years, dense image matching for point cloud generation has attracted research attention in both the photogrammetry and computer vision communities. The semi-global matching (SGM) algorithm based on mutual information is one of well-known methods which applies multi-directional smoothing constraint in cost aggregation to efficiently enhance the rate of stereo matching. On the other hand, the smoothing constraint, among others, is the core issue to equip the penalty function with sufficient power for reducing erroneous matches if appropriately chosen. For that, the purpose of this study is to characterize the smoothing constraint by setting mode of minimum of the penalty function. And offer reference of tuning parameters of penalty function by trend of consistency pixel numbers. Tests on Middlebury Stereo Datasets and real image have been carried out and evaluated. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-16T05:08:25Z (GMT). No. of bitstreams: 1 ntu-103-R01521114-1.pdf: 7702266 bytes, checksum: a69eb949701ba636fa2df0cdd43c5aa0 (MD5) Previous issue date: 2014 | en |
| dc.description.tableofcontents | 中文摘要 i
ABSTRACT ii 目錄 iii 圖目錄 v 表目錄 viii 第一章 緒論 1 1.1 研究動機與目的 1 1.2 文獻回顧 1 1.2.1 半全域匹配相關研究 1 1.2.2 半全域匹配之平滑約制補償值相關研究 2 1.3 研究方法與流程 3 1.4 論文架構 4 第二章 研究背景 5 2.1 匹配運算模式 5 2.2 平滑約制原理 6 第三章 研究方法 7 3.1 半全域式匹配演算法 7 3.1.1 匹配值計算 8 3.1.2 匹配值加總 10 3.1.3 視差估算 12 3.1.4 平滑約制補償值 13 3.2 半全域式匹配演算法平滑約制補償值設定 13 3.2.1 關於平滑約制補償值設定 13 3.2.2 關於平滑約制補償函數設定 14 3.2.3 平滑約制補償值常數設定分析 15 3.2.4 平滑約制補償值函數設定分析 16 3.2.5 補償值P2函數最小值設定模式 17 3.3 匹配品質評估指標及統計數據參考指標 18 3.3.1 錯誤像元百分率 19 3.3.2 均方根誤差 19 3.3.3 未通過一致性檢查像元百分率 19 3.3.4 一致性檢查均方根差異 19 第四章 實驗成果與分析 21 4.1 實驗架構與配置 21 4.2 平滑約制常數設定探討 23 4.2.1 平滑約制常數補償特性分析 27 4.2.2 平滑約制常數設定經驗範圍 38 4.3 平滑約制函數設定探討 38 4.3.1 平滑約制函數補償特性分析 50 4.3.2 平滑約制函數設定經驗範圍 62 4.4 實際影像平滑約制補償值函數設定實驗 63 4.4.1 實際影像函數補償特性分析 73 第五章 結論與建議 89 5.1 結論 89 5.2 建議與未來展望 89 參考文獻 91 | |
| dc.language.iso | zh-TW | |
| dc.subject | 補償值函數最小值設定模式 | zh_TW |
| dc.subject | 半全域匹配法 | zh_TW |
| dc.subject | 互資訊 | zh_TW |
| dc.subject | 補償函數 | zh_TW |
| dc.subject | 匹配值加總 | zh_TW |
| dc.subject | Cost aggregation | en |
| dc.subject | Semi-global matching algorithm | en |
| dc.subject | Setting mode of minimum of the penalty function | en |
| dc.subject | Mutual information | en |
| dc.subject | Penalty function | en |
| dc.title | 基於互資訊之半全域匹配補償函數特性及作業參數擬定初探 | zh_TW |
| dc.title | Preliminary Study on Analyzing as well as Tuning Penalty Parameters of Mutual Information Based Semi-Global Matching | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 102-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 蔡展榮,邱式鴻 | |
| dc.subject.keyword | 半全域匹配法,互資訊,補償函數,匹配值加總,補償值函數最小值設定模式, | zh_TW |
| dc.subject.keyword | Semi-global matching algorithm,Mutual information,Penalty function,Cost aggregation,Setting mode of minimum of the penalty function, | en |
| dc.relation.page | 93 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2014-08-19 | |
| dc.contributor.author-college | 工學院 | zh_TW |
| dc.contributor.author-dept | 土木工程學研究所 | zh_TW |
| 顯示於系所單位： | 土木工程學系 | |
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