一種結合影像拼接與對比強化的環景照建構方法

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：18.118.7.85

姓名

魏辰晏(Chen-Yan Wei) 查詢紙本館藏

畢業系所

數學系

論文名稱

一種結合影像拼接與對比強化的環景照建構方法
(A Combined Method of Image Stitching and Contrast Enhancement for Constructing Panoramas)

相關論文

★ 遲滯型細胞神經網路似駝峰行進波之研究	★ 穩態不可壓縮那維爾-史托克問題的最小平方有限元素法之片狀線性數值解
★ Global Exponential Stability of Modified RTD-based Two-Neuron Networks with Discrete Time Delays	★ 二維穩態不可壓縮磁流體問題的迭代最小平方有限元素法之數值計算
★ 兩種迭代最小平方有限元素法求解不可壓縮那維爾-史托克方程組之研究	★ 非線性耦合動力網路的同步現象分析
★ 邊界層和內部層問題的穩定化有限元素法	★ 數種不連續有限元素法求解對流佔優問題之數值研究
★ 某個流固耦合問題的有限元素法數值模擬	★ 高階投影法求解那維爾-史托克方程組
★ 非靜態反應-對流-擴散方程的高階緊緻有限差分解法	★ 二維非線性淺水波方程的Lax-Wendroff差分數值解
★ Numerical Computation of a Direct-Forcing Immersed Boundary Method for Simulating the Interaction of Fluid with Moving Solid Objects	★ On Two Immersed Boundary Methods for Simulating the Dynamics of Fluid-Structure Interaction Problems
★ 生成對抗網路在影像填補的應用	★ 非穩態複雜流體的人造壓縮性直接施力沉浸邊界法數值模擬

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)

本文主要研究一種結合影像拼接與對比強化技術的環景照建構方法。關於影像拼接的部份，我們主要以 Brown-Lowe 對於影像拼接的探討為基礎，對每張影像使用尺度不變特徵轉換演算法提取特徵，對於各張影像的特徵進行特徵匹配，並且使用隨機抽樣一致法，消除錯誤匹配的特徵點對，並篩選出合適的單應性矩陣用於拼接的座標轉換，最後再利用多頻段融合技術，使得在拼接銜接處的色彩看起來更為自然。在完成影像拼接後，我們接續一種由 Hsieh-Shao-Yang 所提出的對比強化的自適應變分模型，其中使用 k -means 演算法將影像劃分成 k 個部分，對於此自適應的變分模型通過交錯最小化演算法中的分裂布雷格曼法將模型拆解成三個子問題處理，並使用最簡易色彩平衡讓整體飽和度提升。最後我們提供一系列的影像拼接數值實驗驗證此方法對建構環景照的有效性。

摘要(英)

In this thesis, we study a method for constructing panoramas that combines image-stitching and contrast enhancement techniques. Based on the works of Brown and Lowe for image stitching, we use the scale-invariant feature transform algorithm to extract features for each image, perform feature matching for each image feature, apply the random sampling consensus method to eliminate the mismatched feature point pairs, and construct the appropriate homography matrix for the coordinate transformation, and finally use the multi-band blending technology to make the color difference at the splicing joints look more naturally. After completing the image stitching, we employ an adaptive variational model proposed by Hsieh-Shao-Yang to enhance the contrast of the stitched image, where we use the k -means algorithm to divide the image into k parts. The split Bregman method is applied to split the problem into three sub-problems to solve the minimization problem associated with the adaptive variational model. We also use the simplest color balance technique to increase the overall saturation. Finally, we provide a series of image stitching numerical experiments to demonstrate the effectiveness of the proposed combined method.

關鍵字(中)

★ 尺度不變特徵轉換
★ 隨機抽樣一致法
★ 多頻段融合
★ 對比強化
★ 自適應變分模型
★ 分裂布雷格曼法

關鍵字(英)

★ scale-invariant feature transform
★ random sampling consensus
★ multi-band blending
★ contrast enhancement
★ adaptive variational model
★ split Bregman method

論文目次

摘要.........................1
1 前言.......................1
2 影像拼接....................4
2.1 特徵選取.................4
2.2 特徵匹配................20
2.3 隨機抽樣一致法..........21
2.4 多頻段融合..............24
2.5 影像圓柱投影............27
3 對比強化...................29
3.1 對定義域的劃分...........29
3.2 自適應模型...............31
3.3 分裂布雷格曼法...........32
3.4 最簡易色彩平衡...........34
4 數值實驗....................36
5 結語.......................46
參考文獻......................47

參考文獻

[1] A. B. Petro, C. Sbert, and J.-M. Morel, Automatic correction of image intensity
non-uniformity by the simplest total variation model, Methods and
Applications of Analysis, 21 (2014), pp. 91-104.

[2] C. Gatta, A. Rizzi, and D. Marini, ACE: an automatic color equalization algorithm,
Proceedings of the First European Conference on Color in Graphics, Image,
and Vision (CGIV02), 2002, pp. 316-320.

[3] D. G. Lowe, Object recognition from local scale-invariant features, Proceedings
of the International Conference on Computer Vision, 2 (1999), pp. 1150-1157.

[4] D. G. Lowe, Distinctive image features from scale-invariant keypoints, International
Journal of Computer Vision, 60 (2004), pp. 91-110.

[5] J.-M. Morel, A. B. Petro, and C. Sbert, Screened Poisson equation for image
contrast enhancement, Image Processing On Line, 4 (2014), pp. 16-29.

[6] J. S. Beis and D. G. Lowe, Shape indexing using approximate nearestneighbour
search in high-dimensional spaces, Conference on Computer Vision
and Pattern Recognition, (1997), pp. 1000-1006.

[7] T. Lindeberg, Scale-space theory: a basic tool for analyzing structures at different
scales, Journal of Applied Statistics, 21 (1994), pp. 225-270.

[8] M. Brown and D. G. Lowe, Recognising panorama, Proceedings of the 9th International
Conference on Computer Vision, (2003), pp. 1218-1225.

[9] M. Brown and D. G. Lowe, Invariant features from interest point groups,
Proceedings of the British Machine Conference, (2002), pp. 23.1-23.10.

[10] N. Limare, J.-L. Lisani, J.-M. Morel, A. B. Petro, and C. Sbert, Simplest color
balance, Image Process. On Line, 1 (2011), pp. 297-315.

[11] T. Lindeberg and B. M. ter Haar Romeny, Linear scale-space I: basic theory,
In: B. M. ter Haar Romeny (eds), Geometry-Driven Diffusion in Computer Vision,
(1994), pp. 1-38. Computational Imaging and Vision, Vol 1, Springer,
Dordrecht, Netherlands.

[12] P.-W. Hsieh, P.-C. Shao, and S.-Y. Yang, Adaptive variational model for contrast
enhancement of low-light images, SIAM Journal on Imaging Sciences, 13
(2020), pp. 1-28.

[13] https://pl.m.wikipedia.org/wiki/Plik:Standard_
deviation_diagram_(decimal_comma).svg

[14] http://www.tjxzj.net/1805.html#lg=1&slide=1

[15] https://www.researchgate.net/figure/
Example-of-a-multi-scale-representation-for-Lena-image-constructed-by-fig8_308747165

[16] https://blog.csdn.net/u012418573/article/details/
96731384

[17] https://blog.csdn.net/qinghuaci666/article/details/
81877169

指導教授

楊肅煜(Su-Yu Yang)

審核日期

2021-9-28

推文