基於匹配代價曲線特徵之遮蔽偵測之研究

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：128

、訪客IP：3.137.220.120

姓名

周雅婷(Ya-ting Chou) 查詢紙本館藏

畢業系所

通訊工程學系

論文名稱

基於匹配代價曲線特徵之遮蔽偵測之研究
(A Study on Occlusion Detection Using Features of Matching Cost Curves)

相關論文

★ 應用於車內視訊之光線適應性視訊壓縮編碼器設計	★ 以粒子濾波法為基礎之改良式頭部追蹤系統
★ 應用於空間與CGS可調性視訊編碼器之快速模式決策演算法	★ 應用於人臉表情辨識之強健式主動外觀模型搜尋演算法
★ 結合Epipolar Geometry為基礎之視角間預測與快速畫面間預測方向決策之多視角視訊編碼	★ 基於改良式可信度傳遞於同質區域之立體視覺匹配演算法
★ 以階層式Boosting演算法為基礎之棒球軌跡辨識	★ 多視角視訊編碼之快速參考畫面方向決策
★ 以線上統計為基礎應用於CGS可調式編碼器之快速模式決策	★ 適用於唇形辨識之改良式主動形狀模型匹配演算法
★ 以運動補償模型為基礎之移動式平台物件追蹤	★ 基於匹配代價之非對稱式立體匹配遮蔽偵測
★ 以動量為基礎之快速多視角視訊編碼模式決策	★ 應用於地點影像辨識之快速局部L-SVMs群體分類器
★ 以高品質合成視角為導向之快速深度視訊編碼模式決策	★ 以運動補償模型為基礎之移動式相機多物件追蹤

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

影像的遮蔽偵測為電腦視覺領域的重要研究課題，本論文提出利用匹配代價曲線特徵以進行遮蔽偵測。本論文方案可分成四個步驟，第一部分找出曲線上最低點的左與右有效區域。第二部分以變異數(variance)擷取曲線特徵，再區分為左右變異數較高之T+T區域，及至少有一區為變異數較低之非T+T區域。針對此兩種區域，第三部份各進行不同的遮蔽偵測方案。由於此兩個遮蔽偵測的方法在遮蔽區域誤判率較高，故針對遮蔽區域再使用另一以匹配影像曲線為基礎且結合geometry-based uniqueness constraint (MC-GUC)之遮蔽方案確認，以增強偵測準確率，減少非遮蔽區域之偵測誤判。第四部份，我們結合跟形像學(morphological image processing)改進遮蔽圖，以提高遮蔽偵測準確率。本論文方案相較於MC_GUC，可有效降低非遮蔽區域之遮蔽誤判平均達5.55%，而整體準確率也能提升1.96%。

摘要(英)

Occlusion detection is important for applications of computer vision. Thus, this thesis proposes an occlusion detection scheme using features of matching cost curves. The proposed scheme can be divided into four steps. First, we find the left and right effective regions around the disparity that has the lowest cost of the matching cost curve. Secondly, based on the variances of the left and right effective regions, a matching cost curve can be divided into either a T + T region, having large variances in both left and right effective regions, or a non-T + T region, having small variance on at least one effective region. Accordingly, the third part of the proposed scheme applies two different occlusion detection methods on these two kinds of regions. Since the aforementioned occlusion detection methods have high false positive rate in occluded regions, we use another matching cost based occlusion detection algorithm that combines with geometry-based uniqueness constraint (MC-GUC) to enhance detection accuracy and reduce errors on non-occluded regions simultaneously. Finally, we use morphological image processing to improve the accuracy of occlusion map. Compared with MC_GUC, the proposed scheme can effectively reduce error rate around an average of 5.55% on non-occluded regions, while the overall detection accuracy can be improved around 1.96%.

關鍵字(中)

★ 立體視覺匹配
★ 遮蔽偵測
★ 匹配代價曲線
★ 曲線配適

關鍵字(英)

★ stereo matching
★ occlusion detection
★ matching cost curve
★ curve fitting

論文目次

摘要 I
ABSTRACT II
致謝 III
目錄 IV
圖目錄 VI
表目錄 IX
第一章緒論 1
1.1 前言 1
1.2 研究動機 1
1.3 研究方法 2
1.4 論文架構 2
第二章立體視覺影像之錯誤對應問題 3
2.1 立體視覺匹配問題 3
2.2 立體視覺匹配之代價合併技術現況 5
2.2.1 適應性視窗演算法(Adaptive Window Algorithm) 5
2.2.2 適應性支持權重演算法
(Adaptive Support-Weight Algorithm) 8
2.3 錯誤對應問題 11
2.4 總結 13
第三章立體視覺匹配中之遮蔽偵測 15
3.1 計算視差過程進行之遮蔽偵測 15
3.2 基於視差圖之遮蔽偵測 16
3.2.1 以視差結果為主之遮蔽偵測 16
3.2.2 基於視差與原始影像資訊之遮蔽偵測 18
3.3 基於匹配代價之遮蔽偵測 20
3.4 總結 22
第四章本論文提出之基於匹配代價曲線特徵之遮蔽偵測 23
4.1 曲線特徵之有效區間 24
4.1.1 利用曲線配適(curve fitting)之左區間界定 24
4.1.2 利用二次微分界定右區間 28
4.1.3 去除無效區間 29
4.1.4 擷取有效區間演算法 29
4.2 紋理區域與非紋理區域之匹配代價曲線特徵 31
4.3 基於匹配代價曲線特徵之遮蔽偵測 32
4.3.1 T+T區域之匹配代價曲線之特性 33
4.3.2 非T+T區域之彩色影像之特性 37
4.4 利用形態學(MORPHOLOGICAL IMAGE PROCESSING)改善遮蔽圖41
4.4.1 使用Closing改善結合Geometry-Based Uniqueness Constraint與匹配代價(MC_GUC)之遮蔽偵測 42
4.4.2 使用Erosion填補不連續遮蔽之區域 43
4.5 結論 44
第五章實驗結果與討論 45
5.1 測試影像與參數設定 45
5.1.1 測試影像 45
5.1.2 參數設定 47
5.2 遮蔽偵測之效能評估與分析 48
5.3 總結 55
第六章結論與未來展望 56
參考文獻 57

參考文獻

[1]O. Veksler, “Stereo correspondence with compact windows via minimum ratio cycle,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 12, pp. 1654-1660, Dec. 2002.
[2]K.-J. Yoon, and I. -S. Kweon, “Adaptive support-weight approach for correspondence search,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 4, pp. 650-656, Apr. 2006.
[3]G. Egnal, and R. P. Wildes, “Detecting binocular half-occlusions: empirical comparisons of five approaches,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 8, pp. 1127-1133, Aug. 2002.
[4]S.-H. Li, “Asymmetric Occlusion Detection Using Matching Cost for Stereo Matching,” Master thesis, Deptartment of Communication Engineering, National Central University, Taiwan. Jul. 2011.
[5]M. Z. Brown, D. Burschka, and G. D. Hager, “Advances in computational stereo,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 8, pp. 993-1008, Aug. 2003.
[6]D. Scharstein, and R. Szeliski, “A taxonomy and evaluation of dense two-frame stereo correspondence algorithms,” International Journal of Computer vision, vol. 47, no. 1-3, pp. 7-42, Apr. 2002.
[7]H. Hirschmuller, “Stereo processing by semiglobal matching and mutual information,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30, no. 2, pp. 328-341, Feb. 2008.
[8]T. Kanade, and M. Okutomi, “A stereo matching algorithm with an adaptive window: Theory and experiment,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 16, no. 9, pp. 920-932, Sep. 1994.
[9]Y. Boykov, O. Veksler, and R. Zabih, “A variable window approach to early vision,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, no. 12, pp. 1283-1294, Dec. 1998.
[10]K. Nakayama, and S. Shimojo, “Da Vinci stereopsis: Depth and subjective occluding contours from unpaired image points,” Vision research, vol. 30, no. 11, pp. 1811-1825, May 1990.
[11]S. T. Barnard, and M. A. Fischler, “Computational stereo,” ACM Computing Surveys, vol. 14, no. 4, pp. 553-572, Dec. 1982.
[12]M. Bleyer, C. Rother, and P. Kohli, “Surface stereo with soft segmentation,” IEEE Conference on Computer Vision and Pattern Recognition, pp. 1570-1577, Jun. 2010.
[13]M. Bleyer, and M. Gelautz, “Graph-cut-based stereo matching using image segmentation with symmetrical treatment of occlusions,” Signal Processing: Image Communication, vol. 22, no. 2, pp. 127-143, Feb. 2007.
[14]Q. Yang, L. Wang, R. Yang et al., “Stereo matching with color-weighted correlation, hierarchical belief propagation, and occlusion handling,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 3, pp. 492-504, Mar. 2009.
[15]S. Ince, and J. Konrad, “Geometry-based estimation of occlusions from video frame pairs,” IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 2, pp. ii/933-ii/936 Vol. 2, Mar. 2005.
[16]C. L. Zitnick, and T. Kanade, “A cooperative algorithm for stereo matching and occlusion detection,” Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 22, no. 7, pp. 675-684, Jul. 2000.
[17]W. Xiong, H. S. Chung, and J. Jia, “Fractional stereo matching using expectation-maximization,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 3, pp. 428-443, Mar. 2009.
[18]P.-M. Jodoin, C. Rosenberger, and M. Mignotte, “Detecting half-occlusion with a fast region-based fusion procedure,” Proceedings of the British Machine Vision Conference, pp. 417-426, Sep. 2006.
[19]J. D. Oh, S. Ma, and C.-C. Kuo, “Stereo matching via disparity estimation and surface modeling,” IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-8, Jun. 2007.
[20]M. Bleyer, M. Gelautz, C. Rother et al., “A stereo approach that handles the matting problem via image warping,” IEEE Conference on Computer Vision and Pattern Recognition, pp. 501-508, Jun. 2009.
[21]W. Chen, M.-J. Zhang, and Z.-H. Xiong, “Fast semi-global stereo matching via extracting disparity candidates from region boundaries,” IET computer vision, vol. 5, no. 2, pp. 143-150, Mar. 2011.

指導教授

唐之瑋(Chih-wei Tang)

審核日期

2013-7-25

推文