Ellipse Fitting for Computer Vision: Implementation and Applications
暫譯: 電腦視覺中的橢圓擬合:實作與應用
Kenichi Kanatani, Yasuyuki Sugaya, Yasushi Kanazawa
- 出版商: Morgan & Claypool
- 出版日期: 2016-04-21
- 售價: $1,930
- 貴賓價: 9.5 折 $1,834
- 語言: 英文
- 頁數: 142
- 裝訂: Paperback
- ISBN: 1627054588
- ISBN-13: 9781627054584
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相關分類:
Computer Vision
海外代購書籍(需單獨結帳)
商品描述
Because circular objects are projected to ellipses in images, ellipse fitting is a first step for 3-D analysis of circular objects in computer vision applications. For this reason, the study of ellipse fitting began as soon as computers came into use for image analysis in the 1970s, but it is only recently that optimal computation techniques based on the statistical properties of noise were established. These include renormalization (1993), which was then improved as FNS (2000) and HEIV (2000). Later, further improvements, called hyperaccurate correction (2006), HyperLS (2009), and hyper-renormalization (2012), were presented. Today, these are regarded as the most accurate fitting methods among all known techniques. This book describes these algorithms as well implementation details and applications to 3-D scene analysis.
We also present general mathematical theories of statistical optimization underlying all ellipse fitting algorithms, including rigorous covariance and bias analyses and the theoretical accuracy limit. The results can be directly applied to other computer vision tasks including computing fundamental matrices and homographies between images.
This book can serve not simply as a reference of ellipse fitting algorithms for researchers, but also as learning material for beginners who want to start computer vision research. The sample program codes are downloadable from the website: https: //sites.google.com/a/morganclaypool.com/ellipse-fitting-for-computer-vision-implementation-and-applications.
商品描述(中文翻譯)
因為圓形物體在影像中投影為橢圓,因此橢圓擬合是計算機視覺應用中對圓形物體進行三維分析的第一步。因此,橢圓擬合的研究自1970年代計算機開始用於影像分析以來便已展開,但直到最近,基於噪聲統計特性的最佳計算技術才得以建立。這些技術包括重正規化(renormalization,1993),隨後改進為FNS(2000)和HEIV(2000)。之後,進一步的改進被稱為超精確修正(hyperaccurate correction,2006)、HyperLS(2009)和超重正規化(hyper-renormalization,2012)。如今,這些方法被認為是所有已知技術中最準確的擬合方法。本書描述了這些算法以及實現細節和三維場景分析的應用。
我們還介紹了所有橢圓擬合算法背後的統計優化一般數學理論,包括嚴謹的協方差和偏差分析以及理論準確性極限。這些結果可以直接應用於其他計算機視覺任務,包括計算基本矩陣和影像之間的單應性。
本書不僅可以作為研究人員的橢圓擬合算法參考,還可以作為希望開始計算機視覺研究的初學者的學習材料。示例程式碼可從網站下載:https://sites.google.com/a/morganclaypool.com/ellipse-fitting-for-computer-vision-implementation-and-applications。