以關聯式動態規劃法做雙眼立體視覺偵測

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：17

、訪客IP：3.138.174.174

姓名

林育正(Yu-Cheng Lin) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

以關聯式動態規劃法做雙眼立體視覺偵測
(Binocular stereo vision detection based on the associated dynamic programming)

相關論文

★ 適用於大面積及場景轉換的視訊錯誤隱藏法	★ 虛擬觸覺系統中的力回饋修正與展現
★ 多頻譜衛星影像融合與紅外線影像合成	★ 腹腔鏡膽囊切除手術模擬系統
★ 飛行模擬系統中的動態載入式多重解析度地形模塑	★ 以凌波為基礎的多重解析度地形模塑與貼圖
★ 多重解析度光流分析與深度計算	★ 體積守恆的變形模塑應用於腹腔鏡手術模擬
★ 互動式多重解析度模型編輯技術	★ 以小波轉換為基礎的多重解析度邊線追蹤技術(Wavelet-based multiresolution edge tracking for edge detection)
★ 基於二次式誤差及屬性準則的多重解析度模塑	★ 以整數小波轉換及灰色理論為基礎的漸進式影像壓縮
★ 建立在動態載入多重解析度地形模塑的戰術模擬	★ 以多階分割的空間關係做人臉偵測與特徵擷取
★ 以小波轉換為基礎的影像浮水印與壓縮	★ 外觀守恆及視點相關的多重解析度模塑

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)

隨著經濟的成長，機動車輛愈來愈多，因而交通事故也愈來愈多。有鑑於此，發展車輛輔助安全駕駛的議題，也就愈顯示其重要性。市區的行人防撞是其中一項重要議題。在本研究中，我們提出一個利用雙眼立體視覺來取得深度資訊的方法，並應用於車輛前方的行人偵測上，以避免己車撞及行人。另外在倒車輔助的應用上，我們使用平面投影轉換的立體視覺方法來偵測障礙物，以避免倒車時碰撞到障礙物。
在車輛前方的行人偵測中，我們先根據兩張影像平均亮度的比值來調整兩張影像的亮度，使其較為相近。再根據水平投影累積資訊調整兩張影像的垂直位置，使對應的掃描線能夠儘量接近極線 (epipolar line) 的位置。之後再透過我們所提出的關聯式動態規劃法計算出像差圖；再透過形態學平滑化，將雜訊消除。接著產生連結區塊，將行人或障礙物框選出來，並根據像差值，求得該物體與攝影機之距離。
在倒車輔助方面，我們先利用相機校正取得兩相機各相對於地面之轉換矩陣，再將左影像透過反轉換轉至右影像平面。之後將兩影像相減平方，並利用形態學平滑化去除雜訊。最後產生連結區塊，框出障礙物並利用垂直邊資訊，框出較精確的地面上障礙物範圍。

摘要(英)

In these few decades, the vehicle number is rapidly increasing due to people’s incomes increasing. In addition to the vehicle number, more factors of road situation, driving environment, and human attention result in a large amount of traffic accidents and casualties. If there is a mechanism to help the driver to detect the road situation and driving environment, and then provide some useful information to the driver in these situations, the danger is therefore avoided. It is important to develop real-time automotive driver assistance systems. Pedestrian collision avoidance is one of the important issues. In this study, we propose a method to get depth information with binocular stereo vision, and apply to pedestrian detection in front of the vehicle. Moreover, we use homography to detect obstacles to avoid the close collision.
In pedestrian detection system, we first adjust the illumination and vertical position of the image pair. Then, we use associated dynamic programming to generate disparity map. Thirdly, we use morphology to reduce noise. Finally, we generate connected component to detect pedestrians or obstacles and estimate distances based on the disparity.
In parking assistance system, we first use camera calibration to get the transform matrices between the cameras and the ground coordinate system. Then, we transform left image into the right image plane via the ground coordinate system. Thirdly, we subtract the right original image and the re-projected image to generate a difference image. Fourthly, we use morphology to reduce the noise in the difference image. Finally, we generate connected component from the difference image to detect obstacles.

關鍵字(中)

★ 動態規劃
★ 雙眼立體視覺

關鍵字(英)

★ binocular stereo vision
★ dynamic programming

論文目次

摘要 II
Abstract III
誌謝 IV
目錄 V
圖表目錄 VII
表格目錄 IX
第一章緒論 1
1.1 研究動機 1
1.2 系統架構 1
1.2.1 調整影像 3
1.2.2 產生像差圖 3
1.2.3 框選障礙物 3
1.2.4 估測障礙物距離 4
1.3 論文架構 4
第二章相關研究 5
2.1 密集像差圖的立體視覺法 5
2.2 稀疏像差圖的立體視覺法 8
2.3 逆透視投影偵測法 12
第三章產生像差圖 14
3.1 雙眼立體視覺法 14
3.2 調整影像亮度 15
3.3 調整影像水平高度 17
3.4 關聯式動態規劃法 18
第四章框選並估測障礙物距離 23
4.1 框選障礙物 23
4.1.1 形態學平滑化 23
4.1.2 區塊連結 25
4.1.3 縮小框選範圍 27
4.2 障礙物距離估測 29
4.2.1 相機參數校正 30
4.2.2 距離估測 39
第五章平面投影轉換尋找障礙物 40
5.1 將左影像經地面轉換至右影像平面 40
5.2 尋找障礙物點 41
5.3 形態學去除雜訊 42
5.4 產生連結區塊並框出物體 44
第六章實驗 46
6.1 實驗平台 46
6.2 實驗結果 46
6.2.1 各種雙眼立體視覺方法之比較 47
6.2.2 原始的動態規劃法與關聯式動態規劃法之比較 49
6.2.3 距離估計之準確度 52
6.2.4 平面投影轉換框選物體之結果 55
第七章結論與未來展望 58
7.1 結論 58
7.2 未來工作 58
參考文獻 60

參考文獻

[1] Bertozzi, M. and A. Broggi, "Vision-based vehicle guidance," Computer, vol.30, no.7, pp.49-55, 1997.
[2] Bertozzi, M. and A. Broggi, "GOLD: a parallel real-time stereo vision system for generic obstacle and lane detection," IEEE Trans. Image Processing, vol.7, no.1, pp.62-81, 1998.
[3] Bertozzi, M., A. Broggi, and A. Fascioli, "Stereo inverse perspective mapping theory and applications," Image and Vision Computing, vol.16, no.8, pp.585-590, 1998.
[4] Birchfield, S., B. Natarajan, and C. Tomasi, "Correspondence as energy-based segmentation," Image and Vision Computing, vol.25, no.8, pp.1329-1340, 2007.
[5] Boykov, Y., O. Veksler, and R. Zabih, "Fast approximate energy minimization via graph cuts," IEEE Trans. Pattern Analysis and Machine Intelligence, vol.23, no.11, pp.1222-1239, 2001.
[6] Cox, I. J., S. L. Hingorani, S. B. Rao, and B. M. Maggs, "A maximum likelihood stereo algorithm," Computer Vision and Image Understanding, vol.63, no.3, pp.542-567, 1996.
[7] Deng, Y. and X. Lin, "A fast line segment based dense stereo algorithm using tree dynamic programming," in Proc. 9th European Conference on Computer Vision, Graz, Austria, May 7-13, 2006, pp.201-212.
[8] Fang, Y., I. Masaki, and B. Horn, "Depth-based target segmentation for intelligent vehicles fusion of radar and binocular stereo," IEEE Trans. Intelligent Transportation Systems, vol.3, no.3, pp.196-202, 2002.
[9] Gong, M. and Y. Yang, "Fast unambiguous stereo matching using reliability-based dynamic programming," IEEE Trans. Pattern Analysis and Machine Intelligence, vol.27, no.6, pp.998-1003, 2005.
[10] Huh, k., J. Park, J. Hwang, and D. Hong, "A stereo vision-based obstacle detection system in vehicles," Optics and Lasers in Engineering, vol.46, no.2, pp.168-178, 2008.
[11] Kim, J., K. Lee, B. Choi, and S. Lee, "A dense stereo matching using two-pass dynamic programming with generalized ground control points," in Proc. of Conf. on Computer Vision and Pattern Recognition, San Diego, CA, June 20-25, 2005, pp.1075-1082.
[12] Klaus, A., M. Sormann, and K. Karner, "Segment-based stereo matching using belief propagation and a self-adapting dissimilarity measure," in Proc. 18th International Conference on Pattern Recognition, Hong Kong, Aug.20-24, 2006, pp.15-18.
[13] Kolmogorov, V. and R. Zabih, "Computing visual correspondence with occlusions using graph cuts," in Proc. 8th International Conference on Computer Vision, Vancouver, Canada, July 7-14, 2001, pp.508-515.
[14] Kolmogorov, V. and R. Zabih, "Multi-camera scene reconstruction via graph cuts," in Proc. 7th European Conference on Computer Vision, Copenhagen, Denmark, May 28-31, 2002, pp.82-96.
[15] Larsen, S., P. Mordohai, M. Pollefeys, and H. Fuchs., " Temporally consistent reconstruction from multiple video streams using enhanced belief propagation," in Proc. 11th International Conference on Computer Vision, Rio de Janeiro, Brazil, Oct. 14-21, 2007, pp.1-8.
[16] Loop, C. and Z. Zhang, "Computing Rectifying Homographies for Stereo Vision," in Proc. of Conf. on Computer Vision and Pattern Recognition, Fort Collins, Colorado, June 23-25, 1999, pp.125-131.
[17] Mattoccia, S., F. Tombari, and L. D. Stefano, "Stereo vision enabling precise border localization within a scanline optimization framework," in Proc. 8th Asian Conference on Computer Vision, Tokyo, Japan, Nov.18-22, 2007, pp.517-527.
[18] Min, D. and K. Sohn, "Cost aggregation and occlusion handling with WLS in stereo matching," IEEE Trans. Image Processing, vol.17, no.8, pp.1431-1442, 2008.
[19] Scharstein, D. 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, 2002.
[20] Su, J., R. Chung, and L. Jin, "Homography-based partitioning of curved surface for stereo correspondence establishment," Pattern Recognition Letters, vol.28, no.12, pp.1459-1471, 2007.
[21] Toulminet, G., M. Bertozzi, S. Mousset, A. Bensrhair, and A. Broggi, "Vehicle detection by means of stereo vision-based obstacles features extraction and monocular pattern analysis," IEEE Trans. Image Processing, vol.15, no.8, pp.2364-2375, 2006.
[22] Veksler, O., "Stereo correspondence by dynamic programming on a tree," in Proc. of Conf. on Computer Vision and Pattern Recognition, San Diego, CA, June 20-25, 2005, pp.384-390.
[23] Wang, Z. and Z. Zheng, "A region based stereo matching algorithm using cooperative optimization," in Proc. of Conf. on Computer Vision and Pattern Recognition, Anchorage, Alaska, June 24-26, 2008, pp.1-8.
[24] Xu, Z., L. Ma, M. Kimachi, and M. Suwa, "Efficient contrast invariant stereo correspondence using dynamic programming with vertical constraint," Visual Computer, vol.24, no.1, pp.45-55, 2008.
[25] Yang, Q., L. Wang, R. Yang, S. Wang, M. Liao, and D. Nist?r, "Real-time global stereo matching using hierarchical belief propagation," in Proc. 17th British Machine Vision Conference, Edinburgh, UK, Sep.4-7, 2006, pp.989-998.
[26] Yang, Q., L. Wang, R. Yang, H. Stew?nius, and D. Nist?r, "Stereo matching with color-weighted correlation, hierarchical belief propagation and occlusion handling," IEEE Trans. Pattern Analysis and Machine Intelligence, vol.31, no.3, pp.492-504, 2009.
[27] Yang, R. and M. Pollefeys, "Multi-resolution real-time stereo on commodity graphics hardware," in Proc. of Conf. on Computer Vision and Pattern Recognition, Madison, Wisconsin, June 18-20, 2003, pp.211-217.
[28] Yu, T., R. Lin, B. Super, and B. Tang, "Efficient message representations for belief propagation," in Proc. 11th International Conference on Computer Vision, Rio de Janeiro, Brazil, Oct. 14-21, 2007, pp.1-8.
[29] Zhang, Z., "Determining the epipolar geometry and its uncertainty: A review," International Journal of Computer Vision, vol.27, no.2, pp.161-195, 1998.
[30] Zhang, Z., "A flexible new technique for camera calibration," IEEE Trans. Pattern Analysis and Machine Intelligence, vol.22, no.11, pp.1330-1334, 2000.

指導教授

曾定章(Din-Chang Tseng)

審核日期

2009-7-21

推文