Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Abstract 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 re 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), re presented. Today, these are regarded as the most accurate fitting methods among all known techniques. This book describes these algorithms as ll implementation details and applications to 3-D scene analysis.  also present general mathematical theories of statistical optimization underlying all ellipse fitting algorithms, including rigorous covariance and bias analyses and the theo...
['Kenichi Kanatani', 'Yasuyuki Sugaya', 'Yasushi Kanazawa']
I am writing a research paper on a idea Ellipse Fitting for Computer Vision: Implementation and Applications
[{"from": "human", "value": "I am writing a research paper on a idea Ellipse Fitting for Computer Vision: Implementation and Applications"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Abstract 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 re 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), re presented. Today, these are regarded as the most accurate fitting methods among all known techniques. This book describes these algorithms as ll implementation details and applications to 3-D scene analysis.  also present general mathematical theories of statistical optimization underlying all ellipse fitting algorithms, including rigorous covariance and bias analyses and the theo..."}]