Robust Real-Time Ellipse Detection by Direct Least-Square-Fitting

Author(s):  
Jianping Wu
Author(s):  
Nicola Greggio ◽  
Luigi Manfredi ◽  
Cecilia Laschi ◽  
Paolo Dario ◽  
Maria Chiara Carrozza

2004 ◽  
Vol 14 (04n05) ◽  
pp. 261-276 ◽  
Author(s):  
NILOY J. MITRA ◽  
AN NGUYEN ◽  
LEONIDAS GUIBAS

In this paper we describe and analyze a method based on local least square fitting for estimating the normals at all sample points of a point cloud data (PCD) set, in the presence of noise. We study the effects of neighborhood size, curvature, sampling density, and noise on the normal estimation when the PCD is sampled from a smooth curve in ℝ2or a smooth surface in ℝ3, and noise is added. The analysis allows us to find the optimal neighborhood size using other local information from the PCD. Experimental results are also provided.


2015 ◽  
Vol 3 (Suppl 1) ◽  
pp. A319
Author(s):  
S Spadaro ◽  
S Grasso ◽  
V Cricca ◽  
F Dalla Corte ◽  
R Di Mussi ◽  
...  

2007 ◽  
Vol 127 (4) ◽  
pp. 591-598 ◽  
Author(s):  
Yuusuke Sakashita ◽  
Hironobu Fujiyoshi ◽  
Yutaka Hirata ◽  
Hisanori Takamaru ◽  
Naoki Fukaya

Author(s):  
Dali Chen ◽  
Dingyu Xue ◽  
YangQuan Chen

Firstly the one-dimension digital fractional order Savitzky-Golay differentiator (1-D DFOSGD), which generalizes the Savitzky-Golay filter from the integer order to the fractional order, is proposed to estimate the fractional order derivative of the noisy signal. The polynomial least square fitting technology and the Riemann-Liouville fractional order derivative definition are used to ensure robust and accuracy. Experiments demonstrate that 1-D DFOSGD can estimate the fractional order derivatives of both ideal signal and noisy signal accurately. Secondly, the two-dimension DFOSGD is obtained from 1-D DFOSGD by defining a group of direction operators, and a new image enhancing method based on 2-D DFOSGD is presented. Experiments demonstrate that 2-D DFOSGD has very good performance on image enhancement.


2015 ◽  
Vol 23 (1) ◽  
pp. 282-287
Author(s):  
于树海 YU Shu-hai ◽  
王建立 WANG Jian-li ◽  
董磊 DONG Lei ◽  
刘欣悦 LIU Xin-yue ◽  
王亮 WANG Liang

Sign in / Sign up

Export Citation Format

Share Document